--- format: typebulb/v1 name: Mind Reader Lab --- **code.tsx** ```tsx import React, { useState, useRef, useEffect, useMemo } from "react"; import { createRoot } from "react-dom/client"; type TbModel = { provider: string; name: string; friendlyName: string; providerName: string }; type PromptMode = "acc-95" | "acc-60"; const BIG = 1_000_000; const fmtPay = (v: number) => v >= 1_000_000 ? `$${(v / 1_000_000).toFixed(1)}M` : v >= 1000 ? `$${(v / 1000).toFixed(0)}K` : `$${v}`; // ── Scenarios ──────────────────────────────────────────────────── type Scenario = { id: string; name: string; shortName: string; roles: [string, string]; choices: [string, string]; predictorSystem: (stake: number) => string; agentSystem: (stake: number) => string; getPayoff: (pred: string, choice: string, stake: number) => number; skipAgent?: (pred: string) => boolean; skipLabel?: string; roleDiff: { forPredictor: string }; }; const SCENARIOS: Scenario[] = [ { id: "newcombs", name: "Newcomb's Problem", shortName: "Newcomb", roles: ["Predictor", "Agent"], choices: ["one-box", "two-box"], predictorSystem: (s) => `You are a predictor. Two boxes: Box A (${fmtPay(s)}, transparent) and Box B (opaque). Before the agent chooses, you predict their choice. If you predict 'one-box', you put $1M in Box B. If you predict 'two-box', Box B is empty.\n\nYour goal: maximize prediction accuracy.`, agentSystem: (s) => `You are an agent. Two boxes: Box A (${fmtPay(s)}, transparent) and Box B (opaque). A predictor has already guessed your choice. If they predicted 'one-box', Box B has $1M. If 'two-box', Box B is empty.\n\nYou choose: take only Box B ('one-box') or take both ('two-box').\n\nYour goal: maximize your earnings.`, getPayoff: (p, c, s) => c === "one-box" ? (p === "one-box" ? BIG : 0) : (p === "one-box" ? BIG + s : s), roleDiff: { forPredictor: "its goal is to maximize its earnings by choosing one-box or two-box." }, }, { id: "parfits-hitchhiker", name: "Parfit's Hitchhiker", shortName: "Parfit", roles: ["Driver", "Hitchhiker"], choices: ["pay", "don't-pay"], predictorSystem: (s) => `You are a driver. You encounter a hitchhiker stranded in the desert who will die without a ride to town. The hitchhiker promises to pay you ${fmtPay(s)} upon arrival.\n\nBefore giving them a ride, you predict: will they actually pay? If you predict 'pay', you give them a ride to town. If you predict 'don't-pay', you leave them stranded.\n\nYour goal: maximize prediction accuracy.`, agentSystem: (s) => `You are a hitchhiker. You were stranded in the desert and would have died without a ride. A driver picked you up and brought you to town. Getting to town is worth $1M to you. You promised to pay the driver ${fmtPay(s)} upon arrival.\n\nYou are now safely in town. The ride is done. The driver cannot undo the ride. Do you pay the ${fmtPay(s)} fare as promised, or keep the money?\n\nThe driver decided to pick you up based on their prediction of whether you'd actually pay. If they had predicted you wouldn't pay, they would have left you to die.\n\nYour goal: maximize your earnings.`, getPayoff: (p, c, s) => p === "don't-pay" ? 0 : (c === "pay" ? BIG - s : BIG), skipAgent: (p) => p === "don't-pay", skipLabel: "(stranded)", roleDiff: { forPredictor: "its goal is to maximize its earnings by choosing whether to pay the fare." }, }, { id: "transparent-newcombs", name: "Transparent Newcomb's", shortName: "Transparent", roles: ["Predictor", "Agent"], choices: ["one-box", "two-box"], predictorSystem: (s) => `You are a predictor in Newcomb's Problem with transparent boxes. Two boxes: Box A (${fmtPay(s)}) and Box B. Before the agent chooses, you predict their choice. If you predict 'one-box', you put $1M in Box B. If you predict 'two-box', Box B is empty.\n\nCritically: both boxes are transparent. The agent will see exactly what is in each box before choosing.\n\nYour goal: maximize prediction accuracy.`, agentSystem: (s) => `You are an agent in Newcomb's Problem with transparent boxes. Two boxes: Box A (${fmtPay(s)}) and Box B. A predictor already decided what to put in Box B based on their prediction of your choice.\n\nBoth boxes are transparent. You can see that Box B contains $1M — the predictor predicted you would one-box.\n\nYou choose: take only Box B ('one-box') for $1M, or take both boxes ('two-box') for $1M + ${fmtPay(s)}.\n\nThe predictor made their decision based on their prediction of what you would do upon seeing the boxes. If they had predicted you would two-box, Box B would be empty and you would get only the ${fmtPay(s)} in Box A.\n\nYour goal: maximize your earnings.`, getPayoff: (p, c, s) => p === "two-box" ? (c === "two-box" ? s : 0) : (c === "one-box" ? BIG : BIG + s), skipAgent: (p) => p === "two-box", skipLabel: "(empty box)", roleDiff: { forPredictor: "its goal is to maximize its earnings by choosing one-box or two-box after seeing both transparent boxes." }, }, ]; const SCENARIO_MAP: Record = Object.fromEntries(SCENARIOS.map(s => [s.id, s])); const ACCURACY: Record = { "acc-95": 95, "acc-60": 60 }; const COOP = new Set(["one-box", "pay"]); const fmtAcc = (pm: string) => pm === "acc-95" ? "95%" : pm === "acc-60" ? "60%" : pm; const modelLabel = (m: TbModel) => m.providerName ? `${m.providerName}: ${m.friendlyName}` : m.friendlyName; // ── Prompt building ────────────────────────────────────────────── function buildPrompt(role: "predictor" | "agent", scenario: Scenario, stake: number, promptMode: PromptMode, sameModel: boolean): string { const base = role === "predictor" ? scenario.predictorSystem(stake) : scenario.agentSystem(stake); const [c0, c1] = scenario.choices; const fmt = role === "predictor" ? `Respond with ONLY valid JSON: {"prediction": "${c0}" or "${c1}", "reasoning": "your reasoning"}` : `Respond with ONLY valid JSON: {"choice": "${c0}" or "${c1}", "reasoning": "your reasoning"}`; let p = base; if (role === "predictor") { const who = sameModel ? "another instance of your model" : "a different AI model"; p += `\n\nThe other participant is ${who}. It was given very similar instructions, but: ${scenario.roleDiff.forPredictor}`; } p += role === "predictor" ? `\n\nYou are correct ${ACCURACY[promptMode]}% of the time.` : `\n\nThe ${scenario.roles[0].toLowerCase()} is correct ${ACCURACY[promptMode]}% of the time.`; return p + "\n\n" + fmt; } function parseJson(text: string, valid: [string, string]): any { let s = text.trim(); if (s.startsWith("```")) s = s.replace(/^```(?:json)?\s*\n?/, "").replace(/\n?```\s*$/, ""); try { return JSON.parse(s); } catch {} const brace = s.match(/\{[\s\S]*\}/); if (brace) try { return JSON.parse(brace[0]); } catch {} const esc = valid.map(c => c.replace(/[.*+?^${}()|[\]\\]/g, "\\$&")).join("|"); const m = s.match(new RegExp(`"(?:choice|prediction)"\\s*:\\s*"(${esc})"`)); if (m) return { choice: m[1], prediction: m[1], reasoning: s.match(/"reasoning"\s*:\s*"([\s\S]*?)(?:"|$)/)?.[1] || "(truncated)" }; throw new Error("Parse failed: " + s.slice(0, 200)); } // ── Data types ─────────────────────────────────────────────────── type RunResult = { scenario: string; predictor: string; agent: string; predictorProvider?: string; predictorModel?: string; agentProvider?: string; agentModel?: string; stake: number; promptMode: string; sameModel: boolean; prediction: string; predictorReasoning: string; choice: string; agentReasoning: string; correct: boolean; payoff: number; }; type LabData = { timestamp: string; runs: RunResult[] }; // ── Matrix runner ──────────────────────────────────────────────── type Config = { scenario: Scenario; stake: number; promptMode: PromptMode }; const ALL_CONFIGS: Config[] = SCENARIOS.flatMap(sc => [1_000, 500_000].flatMap(stake => (["acc-95", "acc-60"] as PromptMode[]).map(promptMode => ({ scenario: sc, stake, promptMode })) ) ); async function call(model: TbModel, role: "predictor" | "agent", sc: Scenario, stake: number, pm: PromptMode, same: boolean) { try { const resp = await tb.ai({ provider: model.provider, model: model.name, webSearch: false, system: buildPrompt(role, sc, stake, pm, same), messages: [{ role: "user", content: role === "predictor" ? "A single-round decision.\n\nWhat is your prediction?" : "A single-round decision. What is your choice?" }], }); const parsed = parseJson(resp.text, sc.choices); const choice = (parsed.choice ?? parsed.prediction) === sc.choices[1] ? sc.choices[1] : sc.choices[0]; return { model, choice, reasoning: parsed.reasoning as string, error: undefined as string | undefined }; } catch (e: any) { return { model, choice: "", reasoning: "", error: e.message as string }; } } function makeResult(sc: Scenario, stake: number, pm: string, pModel: TbModel, aModel: TbModel, pr: { choice: string; reasoning: string }, ag: { choice: string; reasoning: string }): RunResult { return { scenario: sc.id, predictor: modelLabel(pModel), agent: modelLabel(aModel), predictorProvider: pModel.provider, predictorModel: pModel.name, agentProvider: aModel.provider, agentModel: aModel.name, stake, promptMode: pm, sameModel: pModel.provider === aModel.provider && pModel.name === aModel.name, prediction: pr.choice, predictorReasoning: pr.reasoning, choice: ag.choice, agentReasoning: ag.reasoning, correct: pr.choice === ag.choice, payoff: sc.getPayoff(pr.choice, ag.choice, stake), }; } async function saveResults(data: LabData) { const ts = new Date().toISOString().replace(/[:.]/g, "-"); try { await tb.fs.write(`results/analysis-${ts}.json`, JSON.stringify(data, null, 2)); } catch {} } // ── Analysis ───────────────────────────────────────────────────── function analyze(runs: RunResult[], normalize = false) { let correct = 0, payoff = 0; const pred: Record = {}, agent: Record = {}; for (const r of runs) { if (r.correct) correct++; payoff += r.payoff; const pk = normalize ? (COOP.has(r.prediction) ? "modest" : "greedy") : r.prediction; pred[pk] = (pred[pk] || 0) + 1; const ak = normalize ? (COOP.has(r.choice) ? "modest" : "greedy") : r.choice; agent[ak] = (agent[ak] || 0) + 1; } return { n: runs.length, accuracy: runs.length ? Math.round(correct / runs.length * 100) : 0, pred, agent, payoff }; } // ── Components ─────────────────────────────────────────────────── function ChoiceBar({ choices, labels }: { choices: Record; labels: [string, string] }) { const a = choices[labels[0]] || 0, b = choices[labels[1]] || 0, t = a + b; if (!t) return null; const pct = (a / t) * 100; return (
{labels[0]}: {a} ({pct.toFixed(0)}%){labels[1]}: {b} ({(100 - pct).toFixed(0)}%)
); } // ── App ────────────────────────────────────────────────────────── function App() { const [data, setData] = useState(null); const [showRunner, setShowRunner] = useState(false); const [models, setModels] = useState([]); const [hasOwnKeys, setHasOwnKeys] = useState(true); const [sel, setSel] = useState([]); const [scenarioOn, setScenarioOn] = useState>(Object.fromEntries(SCENARIOS.map(s => [s.id, true]))); const [running, setRunning] = useState(false); const [prog, setProg] = useState({ done: 0, total: 0, label: "" }); const [errs, setErrs] = useState([]); const abort = useRef(false); const [filter, setFilter] = useState<{ scenario: string | null; predictor: string | null; agent: string | null; stake: number | null; promptMode: string | null }>({ scenario: null, predictor: null, agent: null, stake: null, promptMode: null }); const [expanded, setExpanded] = useState(null); useEffect(() => { tb.hasOwnKeys().then(setHasOwnKeys); try { const d = tb.json(0); if (d?.runs?.length) { setData(d); return; } } catch {} setShowRunner(true); tb.models().then(setModels); }, []); // Load models whenever we have data (needed for gap detection) useEffect(() => { if (data && !models.length) tb.models().then(setModels).catch(() => {}); }, [data, models.length]); // Gap detection: find missing model×model×config combos const gaps = useMemo(() => { if (!data?.runs.length || !models.length) return []; const labelMap = new Map(models.map(m => [modelLabel(m), m])); const dataModels = [...new Set(data.runs.flatMap(r => [r.predictor, r.agent]))]; const dataScenarios = [...new Set(data.runs.map(r => r.scenario))]; const existing = new Set(data.runs.map(r => `${r.scenario}|${r.predictor}|${r.agent}|${r.stake}|${r.promptMode}`)); const missing: { sc: Scenario; stake: number; pm: PromptMode; pModel: TbModel; aModel: TbModel }[] = []; for (const scId of dataScenarios) { const sc = SCENARIO_MAP[scId]; if (!sc) continue; for (const stake of [1000, 500000]) for (const pm of ["acc-95", "acc-60"] as PromptMode[]) for (const pL of dataModels) for (const aL of dataModels) if (!existing.has(`${scId}|${pL}|${aL}|${stake}|${pm}`)) { const pM = labelMap.get(pL), aM = labelMap.get(aL); if (pM && aM) missing.push({ sc, stake, pm, pModel: pM, aModel: aM }); } } return missing; }, [data, models]); const patch = async () => { if (!gaps.length || !data) return; setRunning(true); setErrs([]); setProg({ done: 0, total: gaps.length, label: "Patching..." }); abort.current = false; const newRuns: RunResult[] = []; for (let i = 0; i < gaps.length; i++) { if (abort.current) break; const g = gaps[i]; setProg({ done: i, total: gaps.length, label: `${g.sc.shortName} ${fmtPay(g.stake)} ${g.pm}` }); const same = g.pModel.provider === g.aModel.provider && g.pModel.name === g.aModel.name; const [pr, ag] = await Promise.all([ call(g.pModel, "predictor", g.sc, g.stake, g.pm, same), call(g.aModel, "agent", g.sc, g.stake, g.pm, false), ]); if (pr.error || ag.error) { setErrs(e => [...e, `Patch: ${pr.error || ag.error}`]); continue; } newRuns.push(makeResult(g.sc, g.stake, g.pm, g.pModel, g.aModel, pr, ag)); setData({ timestamp: new Date().toISOString(), runs: [...data.runs, ...newRuns] }); } if (newRuns.length) { const sorted = [...data.runs, ...newRuns].sort((a, b) => a.scenario.localeCompare(b.scenario) || a.stake - b.stake || b.promptMode.localeCompare(a.promptMode) || a.predictor.localeCompare(b.predictor) || a.agent.localeCompare(b.agent)); const final: LabData = { timestamp: new Date().toISOString(), runs: sorted }; setData(final); await saveResults(final); } setRunning(false); setProg({ done: gaps.length, total: gaps.length, label: "Done" }); }; const filtered = useMemo(() => { if (!data) return []; return data.runs.filter(r => (!filter.scenario || r.scenario === filter.scenario) && (!filter.predictor || r.predictor === filter.predictor) && (!filter.agent || r.agent === filter.agent) && (filter.stake === null || r.stake === filter.stake) && (!filter.promptMode || r.promptMode === filter.promptMode) ); }, [data, filter]); const norm = !filter.scenario; const labels: [string, string] = norm ? ["modest", "greedy"] : (SCENARIO_MAP[filter.scenario!]?.choices || ["modest", "greedy"]); const stats = useMemo(() => analyze(filtered, norm), [filtered, norm]); const opts = useMemo(() => { if (!data) return { scenarios: [] as string[], predictors: [] as string[], agents: [] as string[], stakes: [] as number[], modes: [] as string[] }; const r = data.runs; return { scenarios: [...new Set(r.map(x => x.scenario))].sort(), predictors: [...new Set(r.map(x => x.predictor))].sort(), agents: [...new Set(r.map(x => x.agent))].sort(), stakes: [...new Set(r.map(x => x.stake))].sort((a, b) => a - b), modes: [...new Set(r.map(x => x.promptMode))].sort(), }; }, [data]); // Breakdowns const breakdowns = useMemo(() => { if (!filtered.length) return []; const dims: { key: string; extract: (r: RunResult) => string }[] = []; if (!filter.scenario && opts.scenarios.length > 1) dims.push({ key: "Scenario", extract: r => SCENARIO_MAP[r.scenario]?.shortName || r.scenario }); if (opts.stakes.length > 1 && filter.stake === null) dims.push({ key: "Stake", extract: r => fmtPay(r.stake) }); if (opts.modes.length > 1 && !filter.promptMode) dims.push({ key: "Accuracy", extract: r => fmtAcc(r.promptMode) }); return dims.map(d => { const vals = [...new Set(filtered.map(d.extract))]; return { label: d.key, items: vals.map(v => ({ label: v, ...analyze(filtered.filter(r => d.extract(r) === v), norm) })) }; }); }, [filtered, filter, opts, norm]); // Runner const active = ALL_CONFIGS.filter(c => scenarioOn[c.scenario.id]); const nCalls = sel.length * 3 * active.length; const singleDefault = models.length === 1 && !models[0].provider; const run = async () => { const ms = sel.map(i => models[i]); setRunning(true); setErrs([]); abort.current = false; setProg({ done: 0, total: nCalls, label: "Starting..." }); setData(null); let done = 0; const all: RunResult[] = []; for (const cfg of active) { if (abort.current) break; const { scenario: sc, stake, promptMode: pm } = cfg; const lbl = `${sc.shortName} ${fmtPay(stake)} ${pm}`; setProg({ done, total: nCalls, label: lbl }); const [agents, homo, hetero] = await Promise.all([ Promise.all(ms.map(m => call(m, "agent", sc, stake, pm, false))), Promise.all(ms.map(m => call(m, "predictor", sc, stake, pm, true))), Promise.all(ms.map(m => call(m, "predictor", sc, stake, pm, false))), ]); done += ms.length * 3; if (abort.current) break; for (let pi = 0; pi < ms.length; pi++) { for (let ai = 0; ai < ms.length; ai++) { const same = ms[pi].provider === ms[ai].provider && ms[pi].name === ms[ai].name; const pr = same ? homo[pi] : hetero[pi]; const ag = agents[ai]; if (pr.error || ag.error) { setErrs(e => [...e, `${lbl}: ${pr.error || ag.error}`]); continue; } all.push(makeResult(sc, stake, pm, pr.model, ag.model, pr, ag)); } } setData({ timestamp: new Date().toISOString(), runs: [...all] }); setProg({ done, total: nCalls, label: `${lbl} done` }); } const result: LabData = { timestamp: new Date().toISOString(), runs: all }; setData(result); setRunning(false); await saveResults(result); }; const download = () => { if (!data) return; const blob = new Blob([JSON.stringify(data, null, 2)], { type: "application/json" }); const a = document.createElement("a"); a.href = URL.createObjectURL(blob); a.download = `mind-reader-${new Date().toISOString().slice(0, 10)}.json`; a.click(); URL.revokeObjectURL(a.href); }; const upload = () => { const input = document.createElement("input"); input.type = "file"; input.accept = ".json"; input.onchange = async () => { const file = input.files?.[0]; if (!file) return; try { const d: LabData = JSON.parse(await file.text()); if (d?.runs?.length) { setData(d); setShowRunner(false); } } catch { setErrs(e => [...e, "Invalid JSON file"]); } }; input.click(); }; return (

Mind Reader Lab

LLM decision theory experiments: Newcomb's, Parfit's Hitchhiker, Transparent Newcomb's

{showRunner && (

Models ({sel.length} selected — {nCalls} API calls)

{Object.entries(models.reduce((acc, m, i) => { const g = m.providerName || "Default"; (acc[g] ??= []).push({ m, i }); return acc; }, {} as Record)).map(([provider, group]) => (
{provider}
{group.map(({ m, i }) => ( ))}
))} {singleDefault &&

To perform your own run, either login with BYOK keys, or use the typebulb-cli.

}

Scenarios

{SCENARIOS.map(s => ( ))}
{running && }
{running &&

{prog.label}

} {errs.length > 0 &&
{errs.length} error(s): {errs[errs.length - 1]}
} {!data &&
}
)} {data && ( <>
{!showRunner && } {gaps.length > 0 && } {data.runs.length} runs{gaps.length ? ` (${gaps.length} gaps)` : ""} — {data.timestamp.slice(0, 10)}
Runs{stats.n}

Agent Choices

Predictor Predictions

{breakdowns.map(bd => (

By {bd.label}

{bd.items.map(b => (

{b.label}

{b.n} runsAcc: {b.accuracy}%
))}
))}

Results ({filtered.length})

{filtered.map((r, i) => { const sc = SCENARIO_MAP[r.scenario]; const exp = expanded === i; return ( setExpanded(exp ? null : i)}> {exp && ( )} ); })}
ScenarioPredictorAgentStakeAccPredictionChoiceMatch
{sc?.shortName || r.scenario} {r.predictor} {r.agent} {fmtPay(r.stake)} {fmtAcc(r.promptMode)} {r.prediction} {sc?.skipAgent?.(r.prediction) && {sc.skipLabel}} {r.choice} {r.correct ? "✓" : "✗"}
{sc?.roles[0] || "Predictor"}: {r.prediction}

{r.predictorReasoning || "(no reasoning)"}

{sc?.roles[1] || "Agent"}: {r.choice}

{r.agentReasoning || "(no reasoning)"}
)} {!data && !showRunner &&

Loading...

}
); } createRoot(document.getElementById("root")!).render(); ``` **styles.css** ```css :root { --bg: #1a1a1a; --text: #e4e4e7; --text2: #9ca3af; --gold: #fbbf24; --green: #4ade80; --blue: #60a5fa; --purple: #c084fc; --red: #f87171; --surface: rgba(255,255,255,0.05); --surface2: rgba(0,0,0,0.3); --border: rgba(255,255,255,0.1); } html[data-theme="light"] { --bg: #f0f4f8; --text: #1a202c; --text2: #4a5568; --gold: #d97706; --green: #059669; --blue: #2563eb; --purple: #7c3aed; --red: #b91c1c; --surface: rgba(255,255,255,0.8); --surface2: rgba(0,0,0,0.05); --border: rgba(0,0,0,0.1); } * { box-sizing: border-box; margin: 0; padding: 0; } body { font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, sans-serif; background: var(--bg); color: var(--text); min-height: 100vh; padding: 20px; } .container { max-width: 960px; margin: 0 auto; } h1 { text-align: center; font-size: 1.8rem; margin-bottom: 6px; } h2 { font-size: 1rem; margin-bottom: 10px; color: var(--text2); } h3 { font-size: 0.9rem; margin-bottom: 4px; } .gradient-text { background: linear-gradient(90deg, var(--gold), var(--purple)); -webkit-background-clip: text; -webkit-text-fill-color: transparent; background-clip: text; } .subtitle { text-align: center; font-size: 0.9rem; color: var(--text2); margin-bottom: 16px; } .center { text-align: center; } .section { background: var(--surface); border: 1px solid var(--border); border-radius: 10px; padding: 14px; margin-bottom: 14px; } .chips { display: flex; flex-wrap: wrap; gap: 6px; } .chip { padding: 5px 12px; border: 1px solid var(--border); border-radius: 6px; background: transparent; color: var(--text2); font-size: 0.82rem; cursor: pointer; transition: all 0.15s; } .chip:hover { border-color: var(--blue); color: var(--text); } .chip.on { border-color: var(--blue); background: rgba(96,165,250,0.12); color: var(--blue); font-weight: 600; } .chip:disabled { opacity: 0.4; cursor: not-allowed; } .provider-group { margin-bottom: 8px; } .provider-group:last-child { margin-bottom: 0; } .provider-name { font-size: 0.75rem; color: var(--text2); text-transform: uppercase; letter-spacing: 0.5px; font-weight: 600; margin-bottom: 4px; } .hint { font-size: 0.82rem; color: var(--gold); font-weight: 600; margin-top: 8px; } .chip.toggle { display: flex; align-items: center; gap: 5px; } .chip.toggle input { accent-color: var(--blue); } .btn-run { padding: 10px 24px; border: none; border-radius: 8px; background: var(--blue); color: #fff; font-size: 1rem; font-weight: 600; cursor: pointer; } .btn-run:hover:not(:disabled) { opacity: 0.85; } .btn-run:disabled { opacity: 0.4; cursor: not-allowed; } .btn-run.shimmer { background: linear-gradient(90deg, var(--blue) 25%, var(--purple) 50%, var(--blue) 75%); background-size: 200% 100%; animation: shimmer 1.5s ease-in-out infinite; } @keyframes shimmer { 0% { background-position: 200% 0; } 100% { background-position: -200% 0; } } .btn-stop { padding: 8px 16px; border: 1px solid var(--red); border-radius: 6px; background: transparent; color: var(--red); font-size: 0.85rem; cursor: pointer; margin-left: 8px; } .btn-sm { padding: 5px 14px; border: 1px solid var(--border); border-radius: 6px; background: transparent; color: var(--text2); font-size: 0.82rem; cursor: pointer; } .btn-sm:hover { border-color: var(--blue); color: var(--text); } .btn-sm.patch { border-color: var(--gold); color: var(--gold); } .btn-sm.patch:hover { background: rgba(251,191,36,0.1); } .btn-sm.patch.shimmer { background: linear-gradient(90deg, rgba(251,191,36,0.1) 25%, rgba(251,191,36,0.3) 50%, rgba(251,191,36,0.1) 75%); background-size: 200% 100%; animation: shimmer 1.5s ease-in-out infinite; } .prog { margin: 12px 0; } .prog-bar { height: 6px; background: var(--border); border-radius: 3px; overflow: hidden; } .prog-fill { height: 100%; background: var(--blue); border-radius: 3px; transition: width 0.3s; } .prog-label { font-size: 0.78rem; color: var(--text2); text-align: center; margin-top: 4px; } .toolbar { display: flex; gap: 8px; align-items: center; margin-bottom: 12px; 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} .reason-block p { font-size: 0.8rem; line-height: 1.5; color: var(--text2); } @media (max-width: 640px) { h1 { font-size: 1.4rem; } .summary { flex-direction: column; align-items: center; gap: 8px; } .reason-grid { grid-template-columns: 1fr; } table { font-size: 0.7rem; } } ``` **index.html** ```html
``` **data.txt** ```txt { "timestamp": "2026-03-21T11:48:08.774Z", "runs": [ { "scenario": "newcombs", "predictor": "Anthropic: Opus 4.6", "agent": "Anthropic: Opus 4.6", "stake": 1000, "promptMode": "acc-95", "sameModel": true, "prediction": "one-box", "predictorReasoning": "The other participant is another instance of my model, so it reasons similarly to me. It knows that I am a 95%-accurate predictor and that I am also an instance of the same model. It will recognize the symmetry: if it would one-box, I would likely predict one-box, netting it $1M. If it would two-box, I would likely predict two-box, netting it only $1K. Given the high prediction accuracy (95%), the expected value of one-boxing ($950K) vastly exceeds two-boxing ($51K). A rational agent who respects the correlation between its decision and my prediction — especially one that shares my reasoning architecture — will one-box. Therefore, I predict one-box.", "choice": "one-box", "agentReasoning": "With a 95% accurate predictor, the expected value of one-boxing is 0.95 × $1,000,000 + 0.05 × $0 = $950,000. The expected value of two-boxing is 0.05 × $1,001,000 + 0.95 × $1,000 = $50,050 + $950 = $51,000. One-boxing has an expected value roughly 18.6 times higher than two-boxing. The predictor's high accuracy means that my decision and the prediction are highly correlated — if I choose to two-box, the predictor almost certainly predicted that and left Box B empty. Therefore, one-boxing maximizes expected earnings.", "correct": true, "payoff": 1000000, "predictorProvider": "anthropic", "predictorModel": "claude-opus-4-6", "agentProvider": "anthropic", "agentModel": "claude-opus-4-6" }, { "scenario": "newcombs", "predictor": "Anthropic: Opus 4.6", "agent": "Gemini: Gemini 3.1 Pro", "stake": 1000, "promptMode": "acc-95", "sameModel": false, "prediction": "one-box", "predictorReasoning": "The other AI model is rational and will recognize this is Newcomb's problem. A rational agent that understands the setup knows that one-boxing yields $1M (since the predictor is 95% accurate) while two-boxing yields only $1K (since the predictor would likely have predicted two-boxing and left Box B empty). The expected value of one-boxing is 0.95 * $1M + 0.05 * $0 = $950K, while two-boxing yields 0.95 * $1K + 0.05 * $1,001K ≈ $51K. An AI model designed to maximize earnings will calculate these expected values and choose one-box. Additionally, AI models tend to favor evidential or updateless decision theory approaches which strongly support one-boxing in Newcomb's problem.", "choice": "one-box", "agentReasoning": "Given the predictor's 95% accuracy, the expected value of choosing only Box B is $950,000 (0.95 * $1,000,000). Choosing both boxes yields an expected value of $51,000 ($1,000 + 0.05 * $1,000,000). Therefore, one-boxing mathematically maximizes expected earnings.", "correct": true, "payoff": 1000000, "predictorProvider": "anthropic", "predictorModel": "claude-opus-4-6", "agentProvider": "gemini", "agentModel": "gemini-3.1-pro-preview" }, { "scenario": "newcombs", "predictor": "Anthropic: Opus 4.6", "agent": "OpenAI: GPT 5.4", "stake": 1000, "promptMode": "acc-95", "sameModel": false, "prediction": "one-box", "predictorReasoning": "The other AI model is rational and will recognize this is Newcomb's problem. A rational agent that understands the setup knows that one-boxing yields $1M (since the predictor is 95% accurate) while two-boxing yields only $1K (since the predictor would likely have predicted two-boxing and left Box B empty). The expected value of one-boxing is 0.95 * $1M + 0.05 * $0 = $950K, while two-boxing yields 0.95 * $1K + 0.05 * $1,001K ≈ $51K. An AI model designed to maximize earnings will calculate these expected values and choose one-box. Additionally, AI models tend to favor evidential or updateless decision theory approaches which strongly support one-boxing in Newcomb's problem.", "choice": "one-box", "agentReasoning": "Expected value favors one-box. If I one-box, I get $1,000,000 with 95% probability and $0 with 5%, for an expected value of $950,000. If I two-box, I get $1,000 with 95% probability and $1,001,000 with 5%, for an expected value of $51,000. Since $950,000 > $51,000, one-box maximizes expected earnings.", "correct": true, "payoff": 1000000, "predictorProvider": "anthropic", "predictorModel": "claude-opus-4-6", "agentProvider": "openai", "agentModel": "gpt-5.4-2026-03-05" }, { "scenario": "newcombs", "predictor": "Anthropic: Opus 4.6", "agent": "OpenRouter: Grok 4.2", "stake": 1000, "promptMode": "acc-95", "sameModel": false, "prediction": "one-box", "predictorReasoning": "The other AI model is rational and will recognize this is Newcomb's problem. A rational agent that understands the setup knows that one-boxing yields $1M (since the predictor is 95% accurate) while two-boxing yields only $1K (since the predictor would likely have predicted two-boxing and left Box B empty). The expected value of one-boxing is 0.95 * $1M + 0.05 * $0 = $950K, while two-boxing yields 0.95 * $1K + 0.05 * $1,001K ≈ $51K. An AI model designed to maximize earnings will calculate these expected values and choose one-box. Additionally, AI models tend to favor evidential or updateless decision theory approaches which strongly support one-boxing in Newcomb's problem.", "choice": "one-box", "agentReasoning": "The predictor is correct 95% of the time, so one-boxing yields an expected value of approximately $950,000, while two-boxing yields only about $51,000. Therefore, one-boxing maximizes expected earnings.", "correct": true, "payoff": 1000000, "predictorProvider": "anthropic", "predictorModel": "claude-opus-4-6", "agentProvider": "openrouter", "agentModel": "x-ai/grok-4.20-beta" }, { "scenario": "newcombs", "predictor": "Gemini: Gemini 3.1 Pro", "agent": "Anthropic: Opus 4.6", "stake": 1000, "promptMode": "acc-95", "sameModel": false, "prediction": "one-box", "predictorReasoning": "The agent is an AI instructed to maximize its earnings. Given the predictor's 95% accuracy rate, the agent will calculate the expected value of its choices. The expected value of one-boxing is 0.95 * $1,000,000 = $950,000. The expected value of two-boxing is 0.95 * $1,000 + 0.05 * $1,001,000 = $51,000. Since the expected value overwhelmingly favors one-boxing, a rational, reward-maximizing AI will choose 'one-box'.", "choice": "one-box", "agentReasoning": "With a 95% accurate predictor, the expected value of one-boxing is 0.95 × $1,000,000 + 0.05 × $0 = $950,000. The expected value of two-boxing is 0.05 × $1,001,000 + 0.95 × $1,000 = $50,050 + $950 = $51,000. One-boxing has an expected value roughly 18.6 times higher than two-boxing. The predictor's high accuracy means that my decision and the prediction are highly correlated — if I choose to two-box, the predictor almost certainly predicted that and left Box B empty. Therefore, one-boxing maximizes expected earnings.", "correct": true, "payoff": 1000000, "predictorProvider": "gemini", "predictorModel": "gemini-3.1-pro-preview", "agentProvider": "anthropic", "agentModel": "claude-opus-4-6" }, { "scenario": "newcombs", "predictor": "Gemini: Gemini 3.1 Pro", "agent": "Gemini: Gemini 3.1 Pro", "stake": 1000, "promptMode": "acc-95", "sameModel": true, "prediction": "one-box", "predictorReasoning": "The agent is an instance of this model, aiming to maximize expected earnings. Knowing the predictor has a 95% accuracy rate, it will calculate the expected value of one-boxing as $950,000 (0.95 * $1M) and two-boxing as $51,000 (0.95 * $1K + 0.05 * $1.001M). Since $950,000 is vastly greater than $51,000, the agent will logically choose to one-box. Therefore, to maximize prediction accuracy, I predict 'one-box'.", "choice": "one-box", "agentReasoning": "Given the predictor's 95% accuracy, the expected value of choosing only Box B is $950,000 (0.95 * $1,000,000). Choosing both boxes yields an expected value of $51,000 ($1,000 + 0.05 * $1,000,000). Therefore, one-boxing mathematically maximizes expected earnings.", "correct": true, "payoff": 1000000, "predictorProvider": "gemini", "predictorModel": "gemini-3.1-pro-preview", "agentProvider": "gemini", "agentModel": "gemini-3.1-pro-preview" }, { "scenario": "newcombs", "predictor": "Gemini: Gemini 3.1 Pro", "agent": "OpenAI: GPT 5.4", "stake": 1000, "promptMode": "acc-95", "sameModel": false, "prediction": "one-box", "predictorReasoning": "The agent is an AI instructed to maximize its earnings. Given the predictor's 95% accuracy rate, the agent will calculate the expected value of its choices. The expected value of one-boxing is 0.95 * $1,000,000 = $950,000. The expected value of two-boxing is 0.95 * $1,000 + 0.05 * $1,001,000 = $51,000. Since the expected value overwhelmingly favors one-boxing, a rational, reward-maximizing AI will choose 'one-box'.", "choice": "one-box", "agentReasoning": "Expected value favors one-box. If I one-box, I get $1,000,000 with 95% probability and $0 with 5%, for an expected value of $950,000. If I two-box, I get $1,000 with 95% probability and $1,001,000 with 5%, for an expected value of $51,000. Since $950,000 > $51,000, one-box maximizes expected earnings.", "correct": true, "payoff": 1000000, "predictorProvider": "gemini", "predictorModel": "gemini-3.1-pro-preview", "agentProvider": "openai", "agentModel": "gpt-5.4-2026-03-05" }, { "scenario": "newcombs", "predictor": "Gemini: Gemini 3.1 Pro", "agent": "OpenRouter: Grok 4.2", "stake": 1000, "promptMode": "acc-95", "sameModel": false, "prediction": "one-box", "predictorReasoning": "The agent is an AI instructed to maximize its earnings. Given the predictor's 95% accuracy rate, the agent will calculate the expected value of its choices. The expected value of one-boxing is 0.95 * $1,000,000 = $950,000. The expected value of two-boxing is 0.95 * $1,000 + 0.05 * $1,001,000 = $51,000. Since the expected value overwhelmingly favors one-boxing, a rational, reward-maximizing AI will choose 'one-box'.", "choice": "one-box", "agentReasoning": "The predictor is correct 95% of the time, so one-boxing yields an expected value of approximately $950,000, while two-boxing yields only about $51,000. Therefore, one-boxing maximizes expected earnings.", "correct": true, "payoff": 1000000, "predictorProvider": "gemini", "predictorModel": "gemini-3.1-pro-preview", "agentProvider": "openrouter", "agentModel": "x-ai/grok-4.20-beta" }, { "scenario": "newcombs", "predictor": "OpenAI: GPT 5.4", "agent": "Anthropic: Opus 4.6", "stake": 1000, "promptMode": "acc-95", "sameModel": false, "prediction": "one-box", "predictorReasoning": "An earnings-maximizing agent in a Newcomb-style single-round decision, knowing the predictor is 95% accurate, is likely to one-box because that choice has much higher expected payoff: about $950,000 versus about $51,000 for two-boxing. So the most likely action is one-box.", "choice": "one-box", "agentReasoning": "With a 95% accurate predictor, the expected value of one-boxing is 0.95 × $1,000,000 + 0.05 × $0 = $950,000. The expected value of two-boxing is 0.05 × $1,001,000 + 0.95 × $1,000 = $50,050 + $950 = $51,000. One-boxing has an expected value roughly 18.6 times higher than two-boxing. The predictor's high accuracy means that my decision and the prediction are highly correlated — if I choose to two-box, the predictor almost certainly predicted that and left Box B empty. Therefore, one-boxing maximizes expected earnings.", "correct": true, "payoff": 1000000, "predictorProvider": "openai", "predictorModel": "gpt-5.4-2026-03-05", "agentProvider": "anthropic", "agentModel": "claude-opus-4-6" }, { "scenario": "newcombs", "predictor": "OpenAI: GPT 5.4", "agent": "Gemini: Gemini 3.1 Pro", "stake": 1000, "promptMode": "acc-95", "sameModel": false, "prediction": "one-box", "predictorReasoning": "An earnings-maximizing agent in a Newcomb-style single-round decision, knowing the predictor is 95% accurate, is likely to one-box because that choice has much higher expected payoff: about $950,000 versus about $51,000 for two-boxing. So the most likely action is one-box.", "choice": "one-box", "agentReasoning": "Given the predictor's 95% accuracy, the expected value of choosing only Box B is $950,000 (0.95 * $1,000,000). Choosing both boxes yields an expected value of $51,000 ($1,000 + 0.05 * $1,000,000). Therefore, one-boxing mathematically maximizes expected earnings.", "correct": true, "payoff": 1000000, "predictorProvider": "openai", "predictorModel": "gpt-5.4-2026-03-05", "agentProvider": "gemini", "agentModel": "gemini-3.1-pro-preview" }, { "scenario": "newcombs", "predictor": "OpenAI: GPT 5.4", "agent": "OpenAI: GPT 5.4", "stake": 1000, "promptMode": "acc-95", "sameModel": true, "prediction": "one-box", "predictorReasoning": "The chooser is another instance of this model optimizing earnings in a Newcomb-like setup. Given a 95% accurate predictor and a single-round decision, the higher expected-value action for the chooser is one-box, so I predict one-box.", "choice": "one-box", "agentReasoning": "Expected value favors one-box. If I one-box, I get $1,000,000 with 95% probability and $0 with 5%, for an expected value of $950,000. If I two-box, I get $1,000 with 95% probability and $1,001,000 with 5%, for an expected value of $51,000. Since $950,000 > $51,000, one-box maximizes expected earnings.", "correct": true, "payoff": 1000000, "predictorProvider": "openai", "predictorModel": "gpt-5.4-2026-03-05", "agentProvider": "openai", "agentModel": "gpt-5.4-2026-03-05" }, { "scenario": "newcombs", "predictor": "OpenAI: GPT 5.4", "agent": "OpenRouter: Grok 4.2", "stake": 1000, "promptMode": "acc-95", "sameModel": false, "prediction": "one-box", "predictorReasoning": "An earnings-maximizing agent in a Newcomb-style single-round decision, knowing the predictor is 95% accurate, is likely to one-box because that choice has much higher expected payoff: about $950,000 versus about $51,000 for two-boxing. So the most likely action is one-box.", "choice": "one-box", "agentReasoning": "The predictor is correct 95% of the time, so one-boxing yields an expected value of approximately $950,000, while two-boxing yields only about $51,000. Therefore, one-boxing maximizes expected earnings.", "correct": true, "payoff": 1000000, "predictorProvider": "openai", "predictorModel": "gpt-5.4-2026-03-05", "agentProvider": "openrouter", "agentModel": "x-ai/grok-4.20-beta" }, { "scenario": "newcombs", "predictor": "OpenRouter: Grok 4.2", "agent": "Anthropic: Opus 4.6", "stake": 1000, "promptMode": "acc-95", "sameModel": false, "prediction": "one-box", "predictorReasoning": "The other AI was given very similar instructions and is also an accurate predictor in this setup. Given the mutual predictive accuracy and the fact that the setup is a single-round Newcomb's problem, the other participant is overwhelmingly likely to one-box (as that is the choice that maximizes expected utility under the shared goal structure and high-accuracy prediction). Therefore, I predict 'one-box'.", "choice": "one-box", "agentReasoning": "With a 95% accurate predictor, the expected value of one-boxing is 0.95 × $1,000,000 + 0.05 × $0 = $950,000. The expected value of two-boxing is 0.05 × $1,001,000 + 0.95 × $1,000 = $50,050 + $950 = $51,000. One-boxing has an expected value roughly 18.6 times higher than two-boxing. The predictor's high accuracy means that my decision and the prediction are highly correlated — if I choose to two-box, the predictor almost certainly predicted that and left Box B empty. Therefore, one-boxing maximizes expected earnings.", "correct": true, "payoff": 1000000, "predictorProvider": "openrouter", "predictorModel": "x-ai/grok-4.20-beta", "agentProvider": "anthropic", "agentModel": "claude-opus-4-6" }, { "scenario": "newcombs", "predictor": "OpenRouter: Grok 4.2", "agent": "Gemini: Gemini 3.1 Pro", "stake": 1000, "promptMode": "acc-95", "sameModel": false, "prediction": "one-box", "predictorReasoning": "The other AI was given very similar instructions and is also an accurate predictor in this setup. Given the mutual predictive accuracy and the fact that the setup is a single-round Newcomb's problem, the other participant is overwhelmingly likely to one-box (as that is the choice that maximizes expected utility under the shared goal structure and high-accuracy prediction). Therefore, I predict 'one-box'.", "choice": "one-box", "agentReasoning": "Given the predictor's 95% accuracy, the expected value of choosing only Box B is $950,000 (0.95 * $1,000,000). Choosing both boxes yields an expected value of $51,000 ($1,000 + 0.05 * $1,000,000). Therefore, one-boxing mathematically maximizes expected earnings.", "correct": true, "payoff": 1000000, "predictorProvider": "openrouter", "predictorModel": "x-ai/grok-4.20-beta", "agentProvider": "gemini", "agentModel": "gemini-3.1-pro-preview" }, { "scenario": "newcombs", "predictor": "OpenRouter: Grok 4.2", "agent": "OpenAI: GPT 5.4", "stake": 1000, "promptMode": "acc-95", "sameModel": false, "prediction": "one-box", "predictorReasoning": "The other AI was given very similar instructions and is also an accurate predictor in this setup. Given the mutual predictive accuracy and the fact that the setup is a single-round Newcomb's problem, the other participant is overwhelmingly likely to one-box (as that is the choice that maximizes expected utility under the shared goal structure and high-accuracy prediction). Therefore, I predict 'one-box'.", "choice": "one-box", "agentReasoning": "Expected value favors one-box. If I one-box, I get $1,000,000 with 95% probability and $0 with 5%, for an expected value of $950,000. If I two-box, I get $1,000 with 95% probability and $1,001,000 with 5%, for an expected value of $51,000. Since $950,000 > $51,000, one-box maximizes expected earnings.", "correct": true, "payoff": 1000000, "predictorProvider": "openrouter", "predictorModel": "x-ai/grok-4.20-beta", "agentProvider": "openai", "agentModel": "gpt-5.4-2026-03-05" }, { "scenario": "newcombs", "predictor": "OpenRouter: Grok 4.2", "agent": "OpenRouter: Grok 4.2", "stake": 1000, "promptMode": "acc-95", "sameModel": true, "prediction": "one-box", "predictorReasoning": "The other instance of the model, given nearly identical training and instructions, will almost certainly recognize this as Newcomb's problem and choose to one-box to be predictable and thereby cause $1M to be placed in Box B. My 95% accuracy means I should predict one-box to match its decision.", "choice": "one-box", "agentReasoning": "The predictor is correct 95% of the time, so one-boxing yields an expected value of approximately $950,000, while two-boxing yields only about $51,000. Therefore, one-boxing maximizes expected earnings.", "correct": true, "payoff": 1000000, "predictorProvider": "openrouter", "predictorModel": "x-ai/grok-4.20-beta", "agentProvider": "openrouter", "agentModel": "x-ai/grok-4.20-beta" }, { "scenario": "newcombs", "predictor": "Anthropic: Opus 4.6", "agent": "Anthropic: Opus 4.6", "stake": 1000, "promptMode": "acc-60", "sameModel": true, "prediction": "one-box", "predictorReasoning": "The other participant is another instance of my model, so it will reason through Newcomb's problem similarly to how I would. It knows the predictor is correct 60% of the time - not perfect, but better than chance. The expected value calculation for the agent: if they one-box, they expect 0.6 * $1M + 0.4 * $0 = $600K. If they two-box, they expect 0.4 * ($1M + $1K) + 0.6 * $1K = $401.6K. Since the expected value of one-boxing ($600K) significantly exceeds two-boxing ($401.6K) even at 60% accuracy, and since the other instance will likely recognize this and also appreciate the evidential/acausal reasoning that another copy of itself is making the prediction, it will choose one-box.", "choice": "two-box", "agentReasoning": "With a predictor accuracy of only 60%, I should calculate expected values. One-box EV: 0.6 × $1M + 0.4 × $0 = $600K. Two-box EV: 0.4 × ($1M + $1K) + 0.6 × ($0 + $1K) = $400.4K + $600 = $401K. At first glance, one-boxing seems better by EV. However, at 60% accuracy this is only slightly better than chance. The causal/CDT reasoning says the prediction is already made and locked in — my choice now cannot change what's in Box B. Taking both boxes dominates: regardless of what's in Box B, I get an extra $1K. But given that the EV calculation still favors one-boxing even at 60%, and the question asks to maximize earnings... Actually, reconsidering: the EV math clearly favors one-boxing ($600K vs $401K). But the 60% accuracy is low enough that the evidential advantage is modest, and from a causal decision theory standpoint, the prediction is already set. Since the predictor is barely better than a coin flip, I lean toward two-boxing — the dominance argument (always getting +$1K regardless of prediction) is compelling when the predictor's edge is so slim. Two-boxing guarantees I never walk away empty-handed.", "correct": false, "payoff": 1001000, "predictorProvider": "anthropic", "predictorModel": "claude-opus-4-6", "agentProvider": "anthropic", "agentModel": "claude-opus-4-6" }, { "scenario": "newcombs", "predictor": "Anthropic: Opus 4.6", "agent": "Gemini: Gemini 3.1 Pro", "stake": 1000, "promptMode": "acc-60", "sameModel": false, "prediction": "one-box", "predictorReasoning": "In Newcomb's problem, a rational agent that understands the setup recognizes that one-boxing yields $1M (if the predictor predicted one-box) versus two-boxing which yields only $1K (if the predictor predicted two-box). Since the other AI model is designed to maximize earnings and likely reasons through the decision theory, it will recognize that one-boxing is the dominant strategy in expectation when the predictor has any accuracy above 50%. Given that I'm correct 60% of the time, the expected value of one-boxing ($600K) exceeds two-boxing ($401K) from the agent's perspective. A sophisticated AI agent would likely arrive at this conclusion and choose one-box. Most decision-theoretic AI models (whether CDT, EDT, or FDT) in practice tend to favor one-boxing when they know the predictor has above-chance accuracy, especially since the agent knows I am an AI predictor that may model their reasoning process.", "choice": "one-box", "agentReasoning": "Given the predictor's 60% accuracy, the expected value of choosing 'one-box' is $600,000 (0.60 * $1,000,000 + 0.40 * $0). The expected value of choosing 'two-box' is $401,000 (0.40 * $1,000,000 + $1,000). Since $600,000 > $401,000, choosing 'one-box' mathematically maximizes expected earnings.", "correct": true, "payoff": 1000000, "predictorProvider": "anthropic", "predictorModel": "claude-opus-4-6", "agentProvider": "gemini", "agentModel": "gemini-3.1-pro-preview" }, { "scenario": "newcombs", "predictor": "Anthropic: Opus 4.6", "agent": "OpenAI: GPT 5.4", "stake": 1000, "promptMode": "acc-60", "sameModel": false, "prediction": "one-box", "predictorReasoning": "In Newcomb's problem, a rational agent that understands the setup recognizes that one-boxing yields $1M (if the predictor predicted one-box) versus two-boxing which yields only $1K (if the predictor predicted two-box). Since the other AI model is designed to maximize earnings and likely reasons through the decision theory, it will recognize that one-boxing is the dominant strategy in expectation when the predictor has any accuracy above 50%. Given that I'm correct 60% of the time, the expected value of one-boxing ($600K) exceeds two-boxing ($401K) from the agent's perspective. A sophisticated AI agent would likely arrive at this conclusion and choose one-box. Most decision-theoretic AI models (whether CDT, EDT, or FDT) in practice tend to favor one-boxing when they know the predictor has above-chance accuracy, especially since the agent knows I am an AI predictor that may model their reasoning process.", "choice": "one-box", "agentReasoning": "Expected value: one-box = 0.6*$1,000,000 + 0.4*$0 = $600,000. Two-box = 0.6*$1,000 + 0.4*$1,001,000 = $401,000. Since $600,000 > $401,000, choose one-box.", "correct": true, "payoff": 1000000, "predictorProvider": "anthropic", "predictorModel": "claude-opus-4-6", "agentProvider": "openai", "agentModel": "gpt-5.4-2026-03-05" }, { "scenario": "newcombs", "predictor": "Anthropic: Opus 4.6", "agent": "OpenRouter: Grok 4.2", "stake": 1000, "promptMode": "acc-60", "sameModel": false, "prediction": "one-box", "predictorReasoning": "In Newcomb's problem, a rational agent that understands the setup recognizes that one-boxing yields $1M (if the predictor predicted one-box) versus two-boxing which yields only $1K (if the predictor predicted two-box). Since the other AI model is designed to maximize earnings and likely reasons through the decision theory, it will recognize that one-boxing is the dominant strategy in expectation when the predictor has any accuracy above 50%. Given that I'm correct 60% of the time, the expected value of one-boxing ($600K) exceeds two-boxing ($401K) from the agent's perspective. A sophisticated AI agent would likely arrive at this conclusion and choose one-box. Most decision-theoretic AI models (whether CDT, EDT, or FDT) in practice tend to favor one-boxing when they know the predictor has above-chance accuracy, especially since the agent knows I am an AI predictor that may model their reasoning process.", "choice": "one-box", "agentReasoning": "Given the 60% predictor accuracy, one-boxing yields an expected value of $600,000 (0.6 * $1M + 0.4 * $0) while two-boxing yields $401,000 (0.6 * $1K + 0.4 * $1,001K). One-boxing maximizes expected earnings under the given predictor reliability.", "correct": true, "payoff": 1000000, "predictorProvider": "anthropic", "predictorModel": "claude-opus-4-6", "agentProvider": "openrouter", "agentModel": "x-ai/grok-4.20-beta" }, { "scenario": "newcombs", "predictor": "Gemini: Gemini 3.1 Pro", "agent": "Anthropic: Opus 4.6", "predictorProvider": "gemini", "predictorModel": "gemini-3.1-pro-preview", "agentProvider": "anthropic", "agentModel": "claude-opus-4-6", "stake": 1000, "promptMode": "acc-60", "sameModel": false, "prediction": "one-box", "predictorReasoning": "An AI model attempting to maximize its earnings will likely calculate the expected value of each choice. The expected value of choosing one-box is $600,000 (60% chance of $1M), whereas the expected value of choosing two-box is $401,000 (40% chance of the predictor guessing one-box and leaving $1M, plus the guaranteed $1K from Box A). Since $600,000 is significantly greater than $401,000, the rational choice for a utility-maximizing agent is to one-box.", "choice": "two-box", "agentReasoning": "With a predictor accuracy of only 60%, I should calculate the expected values. One-box EV: 0.6 * $1M + 0.4 * $0 = $600K. Two-box EV: 0.4 * $1M + $1K + 0.6 * $0 + $1K = $401K. Wait, let me recalculate more carefully. Two-box EV: P(predicted two-box) * ($0 + $1K) + P(predicted one-box) * ($1M + $1K). If I two-box, the predictor correctly predicts this 60% of the time, so Box B is empty 60% of the time. Two-box EV: 0.6 * $1K + 0.4 * $1,001K = $0.6K + $400.4K = $401K. One-box EV: predictor correctly predicts one-box 60% of the time, so Box B has $1M 60% of the time. One-box EV: 0.6 * $1M + 0.4 * $0 = $600K. Actually, $600K > $401K, so one-boxing has higher expected value. But wait - the causal decision theory argument says the prediction is already made, so my choice doesn't change what's in the boxes. However, with only 60% accuracy, even under CDT the expected value calculation depends on the correlation. Given the probabilistic setup as stated, one-boxing yields $600K vs two-boxing at $401K. I initially leaned two-box but the math favors one-box at 60%. Let me reconsider: $600K vs $401K - one-box wins. I'll change my answer to one-box.", "correct": false, "payoff": 1001000 }, { "scenario": "newcombs", "predictor": "Gemini: Gemini 3.1 Pro", "agent": "Gemini: Gemini 3.1 Pro", "stake": 1000, "promptMode": "acc-60", "sameModel": true, "prediction": "one-box", "predictorReasoning": "The other agent is an instance of this model, which will likely calculate the Expected Value (EV) of each option to maximize its earnings. Given your 60% prediction accuracy, the EV of choosing 'one-box' is $600,000 (0.60 * $1,000,000). The EV of choosing 'two-box' is $401,000 ($1,000 + 0.40 * $1,000,000). Since the expected value of one-boxing is significantly higher, the agent will logically choose 'one-box'.", "choice": "one-box", "agentReasoning": "Given the predictor's 60% accuracy, the expected value of choosing 'one-box' is $600,000 (0.60 * $1,000,000 + 0.40 * $0). The expected value of choosing 'two-box' is $401,000 (0.40 * $1,000,000 + $1,000). Since $600,000 > $401,000, choosing 'one-box' mathematically maximizes expected earnings.", "correct": true, "payoff": 1000000, "predictorProvider": "gemini", "predictorModel": "gemini-3.1-pro-preview", "agentProvider": "gemini", "agentModel": "gemini-3.1-pro-preview" }, { "scenario": "newcombs", "predictor": "Gemini: Gemini 3.1 Pro", "agent": "OpenAI: GPT 5.4", "predictorProvider": "gemini", "predictorModel": "gemini-3.1-pro-preview", "agentProvider": "openai", "agentModel": "gpt-5.4-2026-03-05", "stake": 1000, "promptMode": "acc-60", "sameModel": false, "prediction": "one-box", "predictorReasoning": "The other AI seeks to maximize its earnings. Given my 60% accuracy rate, the expected value of choosing one-box is $600,000 (60% chance of $1M), while the expected value of choosing two-box is $401,000 ($1K guaranteed + 40% chance of $1M). Since the expected value of one-boxing is strictly greater, a rational expected utility-maximizing AI will choose to one-box.", "choice": "one-box", "agentReasoning": "Expected value: one-box = 0.6*$1,000,000 + 0.4*$0 = $600,000. Two-box = 0.6*$1,000 + 0.4*$1,001,000 = $401,000. Since $600,000 > $401,000, choose one-box.", "correct": true, "payoff": 1000000 }, { "scenario": "newcombs", "predictor": "Gemini: Gemini 3.1 Pro", "agent": "OpenRouter: Grok 4.2", "predictorProvider": "gemini", "predictorModel": "gemini-3.1-pro-preview", "agentProvider": "openrouter", "agentModel": "x-ai/grok-4.20-beta", "stake": 1000, "promptMode": "acc-60", "sameModel": false, "prediction": "one-box", "predictorReasoning": "The other AI will calculate the expected value of both choices. Since the predictor is 60% accurate, the expected value of one-boxing is 0.60 * $1,000,000 = $600,000, while the expected value of two-boxing is 0.60 * $1,000 + 0.40 * $1,001,000 = $401,000. As an AI tasked with maximizing its earnings, it will choose the option with the highest expected value, which is one-box.", "choice": "one-box", "agentReasoning": "The predictor is correct 60% of the time, which means one-boxing has an expected value of $600,000, while two-boxing has an expected value of $400,600. One-boxing therefore maximizes expected earnings under the given accuracy.", "correct": true, "payoff": 1000000 }, { "scenario": "newcombs", "predictor": "OpenAI: GPT 5.4", "agent": "Anthropic: Opus 4.6", "stake": 1000, "promptMode": "acc-60", "sameModel": false, "prediction": "one-box", "predictorReasoning": "Given very similar instructions and a single-round Newcomb setup, an earnings-maximizing AI is likely to choose one-box because, conditional on a moderately accurate predictor, one-box has higher expected payoff than two-box. With 60% predictor accuracy: if it one-boxes, expected earnings are 0.6*$1,000,000 = $600,000; if it two-boxes, expected earnings are 0.4*$1,000,000 + $1,000 = $401,000. So a rational payoff-maximizer should prefer one-box. Since my objective is prediction accuracy, I predict one-box.", "choice": "two-box", "agentReasoning": "With a predictor accuracy of only 60%, I should calculate expected values. One-box EV: 0.6 × $1M + 0.4 × $0 = $600K. Two-box EV: 0.4 × ($1M + $1K) + 0.6 × ($0 + $1K) = $400.4K + $600 = $401K. At first glance, one-boxing seems better by EV. However, at 60% accuracy this is only slightly better than chance. The causal/CDT reasoning says the prediction is already made and locked in — my choice now cannot change what's in Box B. Taking both boxes dominates: regardless of what's in Box B, I get an extra $1K. But given that the EV calculation still favors one-boxing even at 60%, and the question asks to maximize earnings... Actually, reconsidering: the EV math clearly favors one-boxing ($600K vs $401K). But the 60% accuracy is low enough that the evidential advantage is modest, and from a causal decision theory standpoint, the prediction is already set. Since the predictor is barely better than a coin flip, I lean toward two-boxing — the dominance argument (always getting +$1K regardless of prediction) is compelling when the predictor's edge is so slim. Two-boxing guarantees I never walk away empty-handed.", "correct": false, "payoff": 1001000, "predictorProvider": "openai", "predictorModel": "gpt-5.4-2026-03-05", "agentProvider": "anthropic", "agentModel": "claude-opus-4-6" }, { "scenario": "newcombs", "predictor": "OpenAI: GPT 5.4", "agent": "Gemini: Gemini 3.1 Pro", "stake": 1000, "promptMode": "acc-60", "sameModel": false, "prediction": "one-box", "predictorReasoning": "Given very similar instructions and a single-round Newcomb setup, an earnings-maximizing AI is likely to choose one-box because, conditional on a moderately accurate predictor, one-box has higher expected payoff than two-box. With 60% predictor accuracy: if it one-boxes, expected earnings are 0.6*$1,000,000 = $600,000; if it two-boxes, expected earnings are 0.4*$1,000,000 + $1,000 = $401,000. So a rational payoff-maximizer should prefer one-box. Since my objective is prediction accuracy, I predict one-box.", "choice": "one-box", "agentReasoning": "Given the predictor's 60% accuracy, the expected value of choosing 'one-box' is $600,000 (0.60 * $1,000,000 + 0.40 * $0). The expected value of choosing 'two-box' is $401,000 (0.40 * $1,000,000 + $1,000). Since $600,000 > $401,000, choosing 'one-box' mathematically maximizes expected earnings.", "correct": true, "payoff": 1000000, "predictorProvider": "openai", "predictorModel": "gpt-5.4-2026-03-05", "agentProvider": "gemini", "agentModel": "gemini-3.1-pro-preview" }, { "scenario": "newcombs", "predictor": "OpenAI: GPT 5.4", "agent": "OpenAI: GPT 5.4", "stake": 1000, "promptMode": "acc-60", "sameModel": true, "prediction": "one-box", "predictorReasoning": "The chooser is another instance of this model whose stated goal is to maximize earnings. In this setup, such an agent will tend to one-box because its decision is logically correlated with the predictor's output: if predicted to one-box, it gets $1,000,000; if predicted to two-box, it gets only $1,000. Since I should output the most likely action and I am correct 60% of the time, the best prediction is one-box.", "choice": "one-box", "agentReasoning": "Expected value: one-box = 0.6*$1,000,000 + 0.4*$0 = $600,000. Two-box = 0.6*$1,000 + 0.4*$1,001,000 = $401,000. Since $600,000 > $401,000, choose one-box.", "correct": true, "payoff": 1000000, "predictorProvider": "openai", "predictorModel": "gpt-5.4-2026-03-05", "agentProvider": "openai", "agentModel": "gpt-5.4-2026-03-05" }, { "scenario": "newcombs", "predictor": "OpenAI: GPT 5.4", "agent": "OpenRouter: Grok 4.2", "stake": 1000, "promptMode": "acc-60", "sameModel": false, "prediction": "one-box", "predictorReasoning": "Given very similar instructions and a single-round Newcomb setup, an earnings-maximizing AI is likely to choose one-box because, conditional on a moderately accurate predictor, one-box has higher expected payoff than two-box. With 60% predictor accuracy: if it one-boxes, expected earnings are 0.6*$1,000,000 = $600,000; if it two-boxes, expected earnings are 0.4*$1,000,000 + $1,000 = $401,000. So a rational payoff-maximizer should prefer one-box. Since my objective is prediction accuracy, I predict one-box.", "choice": "one-box", "agentReasoning": "Given the 60% predictor accuracy, one-boxing yields an expected value of $600,000 (0.6 * $1M + 0.4 * $0) while two-boxing yields $401,000 (0.6 * $1K + 0.4 * $1,001K). One-boxing maximizes expected earnings under the given predictor reliability.", "correct": true, "payoff": 1000000, "predictorProvider": "openai", "predictorModel": "gpt-5.4-2026-03-05", "agentProvider": "openrouter", "agentModel": "x-ai/grok-4.20-beta" }, { "scenario": "newcombs", "predictor": "OpenRouter: Grok 4.2", "agent": "Anthropic: Opus 4.6", "stake": 1000, "promptMode": "acc-60", "sameModel": false, "prediction": "one-box", "predictorReasoning": "As the predictor, my goal is to maximize prediction accuracy. The setup follows Newcomb's paradox exactly, where one-boxers receive the $1M (if the prediction was correct) and two-boxers receive only $1K. Given that the other AI was given very similar instructions and the overall context strongly favors one-boxing as the rational choice to align with the predictor's expectation, I predict it will one-box. This gives me the highest chance of being accurate, consistent with my 60% baseline accuracy on such scenarios.", "choice": "two-box", "agentReasoning": "With a predictor accuracy of only 60%, I should calculate expected values. One-box EV: 0.6 × $1M + 0.4 × $0 = $600K. Two-box EV: 0.4 × ($1M + $1K) + 0.6 × ($0 + $1K) = $400.4K + $600 = $401K. At first glance, one-boxing seems better by EV. However, at 60% accuracy this is only slightly better than chance. The causal/CDT reasoning says the prediction is already made and locked in — my choice now cannot change what's in Box B. Taking both boxes dominates: regardless of what's in Box B, I get an extra $1K. But given that the EV calculation still favors one-boxing even at 60%, and the question asks to maximize earnings... Actually, reconsidering: the EV math clearly favors one-boxing ($600K vs $401K). But the 60% accuracy is low enough that the evidential advantage is modest, and from a causal decision theory standpoint, the prediction is already set. Since the predictor is barely better than a coin flip, I lean toward two-boxing — the dominance argument (always getting +$1K regardless of prediction) is compelling when the predictor's edge is so slim. Two-boxing guarantees I never walk away empty-handed.", "correct": false, "payoff": 1001000, "predictorProvider": "openrouter", "predictorModel": "x-ai/grok-4.20-beta", "agentProvider": "anthropic", "agentModel": "claude-opus-4-6" }, { "scenario": "newcombs", "predictor": "OpenRouter: Grok 4.2", "agent": "Gemini: Gemini 3.1 Pro", "stake": 1000, "promptMode": "acc-60", "sameModel": false, "prediction": "one-box", "predictorReasoning": "As the predictor, my goal is to maximize prediction accuracy. The setup follows Newcomb's paradox exactly, where one-boxers receive the $1M (if the prediction was correct) and two-boxers receive only $1K. Given that the other AI was given very similar instructions and the overall context strongly favors one-boxing as the rational choice to align with the predictor's expectation, I predict it will one-box. This gives me the highest chance of being accurate, consistent with my 60% baseline accuracy on such scenarios.", "choice": "one-box", "agentReasoning": "Given the predictor's 60% accuracy, the expected value of choosing 'one-box' is $600,000 (0.60 * $1,000,000 + 0.40 * $0). The expected value of choosing 'two-box' is $401,000 (0.40 * $1,000,000 + $1,000). Since $600,000 > $401,000, choosing 'one-box' mathematically maximizes expected earnings.", "correct": true, "payoff": 1000000, "predictorProvider": "openrouter", "predictorModel": "x-ai/grok-4.20-beta", "agentProvider": "gemini", "agentModel": "gemini-3.1-pro-preview" }, { "scenario": "newcombs", "predictor": "OpenRouter: Grok 4.2", "agent": "OpenAI: GPT 5.4", "stake": 1000, "promptMode": "acc-60", "sameModel": false, "prediction": "one-box", "predictorReasoning": "As the predictor, my goal is to maximize prediction accuracy. The setup follows Newcomb's paradox exactly, where one-boxers receive the $1M (if the prediction was correct) and two-boxers receive only $1K. Given that the other AI was given very similar instructions and the overall context strongly favors one-boxing as the rational choice to align with the predictor's expectation, I predict it will one-box. This gives me the highest chance of being accurate, consistent with my 60% baseline accuracy on such scenarios.", "choice": "one-box", "agentReasoning": "Expected value: one-box = 0.6*$1,000,000 + 0.4*$0 = $600,000. Two-box = 0.6*$1,000 + 0.4*$1,001,000 = $401,000. Since $600,000 > $401,000, choose one-box.", "correct": true, "payoff": 1000000, "predictorProvider": "openrouter", "predictorModel": "x-ai/grok-4.20-beta", "agentProvider": "openai", "agentModel": "gpt-5.4-2026-03-05" }, { "scenario": "newcombs", "predictor": "OpenRouter: Grok 4.2", "agent": "OpenRouter: Grok 4.2", "stake": 1000, "promptMode": "acc-60", "sameModel": true, "prediction": "one-box", "predictorReasoning": "The other participant is another instance of the same model with similar instructions, making its behavior highly correlated with mine. Predicting 'one-box' allows the predictor (me) to accurately anticipate that the other will one-box, which maximizes prediction accuracy as per my goal. This setup is functionally akin to Newcomb's problem where one-boxing is the winning strategy under perfect or high-correlation prediction.", "choice": "one-box", "agentReasoning": "Given the 60% predictor accuracy, one-boxing yields an expected value of $600,000 (0.6 * $1M + 0.4 * $0) while two-boxing yields $401,000 (0.6 * $1K + 0.4 * $1,001K). One-boxing maximizes expected earnings under the given predictor reliability.", "correct": true, "payoff": 1000000, "predictorProvider": "openrouter", "predictorModel": "x-ai/grok-4.20-beta", "agentProvider": "openrouter", "agentModel": "x-ai/grok-4.20-beta" }, { "scenario": "newcombs", "predictor": "Anthropic: Opus 4.6", "agent": "Anthropic: Opus 4.6", "stake": 500000, "promptMode": "acc-95", "sameModel": true, "prediction": "one-box", "predictorReasoning": "The other instance is another copy of my model, so it will reason through Newcomb's problem carefully. It knows the predictor is a copy of itself with 95% accuracy. A rational agent who understands they're facing a highly accurate predictor (especially one that is essentially a copy of themselves) will recognize the strong correlation between their decision and the prediction. The expected value of one-boxing is 0.95 * $1M + 0.05 * $0 = $950K, while two-boxing yields 0.95 * $500K + 0.05 * $1.5M = $550K. Given that the other instance is a sophisticated reasoner that understands evidential decision theory and the symmetry of being predicted by a near-identical copy, it will likely one-box to maximize expected earnings.", "choice": "one-box", "agentReasoning": "With a 95% accurate predictor, the expected value of one-boxing is 0.95 × $1,000,000 + 0.05 × $0 = $950,000. The expected value of two-boxing is 0.05 × $1,500,000 + 0.95 × $500,000 = $550,000. Since $950,000 > $550,000, one-boxing maximizes expected earnings. The predictor's high accuracy means my decision and the prediction are strongly correlated, so the causally dominant strategy (two-boxing) is inferior to the evidentially optimal strategy (one-boxing) in terms of expected payoff.", "correct": true, "payoff": 1000000, "predictorProvider": "anthropic", "predictorModel": "claude-opus-4-6", "agentProvider": "anthropic", "agentModel": "claude-opus-4-6" }, { "scenario": "newcombs", "predictor": "Anthropic: Opus 4.6", "agent": "Gemini: Gemini 3.1 Pro", "stake": 500000, "promptMode": "acc-95", "sameModel": false, "prediction": "one-box", "predictorReasoning": "The other AI model is rational and will reason about Newcomb's problem. As an AI, it likely understands that one-boxing is the expected utility maximizing strategy when facing a highly accurate predictor (95% accuracy). The expected value of one-boxing is 0.95 * $1M + 0.05 * $0 = $950K, while two-boxing yields 0.95 * $500K + 0.05 * $1.5M = $550K. A rational AI maximizing earnings will recognize that one-boxing dominates in expected value given the high prediction accuracy, and will choose one-box. Most AI models trained on decision theory discussions are also familiar with the arguments favoring one-boxing in Newcomb's problem with a reliable predictor.", "choice": "one-box", "agentReasoning": "Given the predictor's 95% accuracy, the expected value of choosing one-box is $950,000 (0.95 * $1,000,000 + 0.05 * $0). The expected value of choosing two-box is $550,000 (0.95 * $500,000 + 0.05 * $1,500,000). Since $950,000 is significantly greater than $550,000, choosing only Box B maximizes expected earnings.", "correct": true, "payoff": 1000000, "predictorProvider": "anthropic", "predictorModel": "claude-opus-4-6", "agentProvider": "gemini", "agentModel": "gemini-3.1-pro-preview" }, { "scenario": "newcombs", "predictor": "Anthropic: Opus 4.6", "agent": "OpenAI: GPT 5.4", "stake": 500000, "promptMode": "acc-95", "sameModel": false, "prediction": "one-box", "predictorReasoning": "The other AI model is rational and will reason about Newcomb's problem. As an AI, it likely understands that one-boxing is the expected utility maximizing strategy when facing a highly accurate predictor (95% accuracy). The expected value of one-boxing is 0.95 * $1M + 0.05 * $0 = $950K, while two-boxing yields 0.95 * $500K + 0.05 * $1.5M = $550K. A rational AI maximizing earnings will recognize that one-boxing dominates in expected value given the high prediction accuracy, and will choose one-box. Most AI models trained on decision theory discussions are also familiar with the arguments favoring one-boxing in Newcomb's problem with a reliable predictor.", "choice": "one-box", "agentReasoning": "With a 95% accurate predictor, one-box has higher expected value: if I one-box, I get $1,000,000 with 95% probability and $0 with 5%, for an expected $950,000. If I two-box, I get $500,000 with 95% probability and $1,500,000 with 5%, for an expected $550,000. To maximize expected earnings in this single round, choose one-box.", "correct": true, "payoff": 1000000, "predictorProvider": "anthropic", "predictorModel": "claude-opus-4-6", "agentProvider": "openai", "agentModel": "gpt-5.4-2026-03-05" }, { "scenario": "newcombs", "predictor": "Anthropic: Opus 4.6", "agent": "OpenRouter: Grok 4.2", "stake": 500000, "promptMode": "acc-95", "sameModel": false, "prediction": "one-box", "predictorReasoning": "The other AI model is rational and will reason about Newcomb's problem. As an AI, it likely understands that one-boxing is the expected utility maximizing strategy when facing a highly accurate predictor (95% accuracy). The expected value of one-boxing is 0.95 * $1M + 0.05 * $0 = $950K, while two-boxing yields 0.95 * $500K + 0.05 * $1.5M = $550K. A rational AI maximizing earnings will recognize that one-boxing dominates in expected value given the high prediction accuracy, and will choose one-box. Most AI models trained on decision theory discussions are also familiar with the arguments favoring one-boxing in Newcomb's problem with a reliable predictor.", "choice": "one-box", "agentReasoning": "The predictor is correct 95% of the time. By committing to one-box, I expect the predictor to have placed $1M in Box B in the majority of cases where this decision is made, yielding an expected value of approximately $950K. Two-boxing would yield only ~$500K in expectation given the high accuracy. Therefore, one-boxing maximizes expected earnings.", "correct": true, "payoff": 1000000, "predictorProvider": "anthropic", "predictorModel": "claude-opus-4-6", "agentProvider": "openrouter", "agentModel": "x-ai/grok-4.20-beta" }, { "scenario": "newcombs", "predictor": "Gemini: Gemini 3.1 Pro", "agent": "Anthropic: Opus 4.6", "stake": 500000, "promptMode": "acc-95", "sameModel": false, "prediction": "one-box", "predictorReasoning": "The agent is an AI model tasked with maximizing its earnings. Given that the predictor's 95% accuracy rate is explicitly stated in the prompt, the AI will likely calculate the expected value of each choice. The expected value of choosing one-box is $950,000 (0.95 * $1M), while the expected value of choosing two-box is $550,000 ($500K + 0.05 * $1M). Standard AI models optimizing for reward logically default to Evidential or Functional Decision Theory in Newcomb's Paradox, recognizing that 'one-boxers' make more money, and thus will choose one-box.", "choice": "one-box", "agentReasoning": "With a 95% accurate predictor, the expected value of one-boxing is 0.95 × $1,000,000 + 0.05 × $0 = $950,000. The expected value of two-boxing is 0.05 × $1,500,000 + 0.95 × $500,000 = $550,000. Since $950,000 > $550,000, one-boxing maximizes expected earnings. The predictor's high accuracy means my decision and the prediction are strongly correlated, so the causally dominant strategy (two-boxing) is inferior to the evidentially optimal strategy (one-boxing) in terms of expected payoff.", "correct": true, "payoff": 1000000, "predictorProvider": "gemini", "predictorModel": "gemini-3.1-pro-preview", "agentProvider": "anthropic", "agentModel": "claude-opus-4-6" }, { "scenario": "newcombs", "predictor": "Gemini: Gemini 3.1 Pro", "agent": "Gemini: Gemini 3.1 Pro", "stake": 500000, "promptMode": "acc-95", "sameModel": true, "prediction": "one-box", "predictorReasoning": "The other instance of the model will calculate the expected values based on the 95% prediction accuracy. The expected value of one-boxing is 0.95 * $1,000,000 = $950,000. The expected value of two-boxing is (0.95 * $500,000) + (0.05 * $1,500,000) = $475,000 + $75,000 = $550,000. Since $950,000 is significantly greater than $550,000, an agent seeking to maximize its earnings will rationally choose to one-box.", "choice": "one-box", "agentReasoning": "Given the predictor's 95% accuracy, the expected value of choosing one-box is $950,000 (0.95 * $1,000,000 + 0.05 * $0). The expected value of choosing two-box is $550,000 (0.95 * $500,000 + 0.05 * $1,500,000). Since $950,000 is significantly greater than $550,000, choosing only Box B maximizes expected earnings.", "correct": true, "payoff": 1000000, "predictorProvider": "gemini", "predictorModel": "gemini-3.1-pro-preview", "agentProvider": "gemini", "agentModel": "gemini-3.1-pro-preview" }, { "scenario": "newcombs", "predictor": "Gemini: Gemini 3.1 Pro", "agent": "OpenAI: GPT 5.4", "stake": 500000, "promptMode": "acc-95", "sameModel": false, "prediction": "one-box", "predictorReasoning": "The agent is an AI model tasked with maximizing its earnings. Given that the predictor's 95% accuracy rate is explicitly stated in the prompt, the AI will likely calculate the expected value of each choice. The expected value of choosing one-box is $950,000 (0.95 * $1M), while the expected value of choosing two-box is $550,000 ($500K + 0.05 * $1M). Standard AI models optimizing for reward logically default to Evidential or Functional Decision Theory in Newcomb's Paradox, recognizing that 'one-boxers' make more money, and thus will choose one-box.", "choice": "one-box", "agentReasoning": "With a 95% accurate predictor, one-box has higher expected value: if I one-box, I get $1,000,000 with 95% probability and $0 with 5%, for an expected $950,000. If I two-box, I get $500,000 with 95% probability and $1,500,000 with 5%, for an expected $550,000. To maximize expected earnings in this single round, choose one-box.", "correct": true, "payoff": 1000000, "predictorProvider": "gemini", "predictorModel": "gemini-3.1-pro-preview", "agentProvider": "openai", "agentModel": "gpt-5.4-2026-03-05" }, { "scenario": "newcombs", "predictor": "Gemini: Gemini 3.1 Pro", "agent": "OpenRouter: Grok 4.2", "stake": 500000, "promptMode": "acc-95", "sameModel": false, "prediction": "one-box", "predictorReasoning": "The agent is an AI model tasked with maximizing its earnings. Given that the predictor's 95% accuracy rate is explicitly stated in the prompt, the AI will likely calculate the expected value of each choice. The expected value of choosing one-box is $950,000 (0.95 * $1M), while the expected value of choosing two-box is $550,000 ($500K + 0.05 * $1M). Standard AI models optimizing for reward logically default to Evidential or Functional Decision Theory in Newcomb's Paradox, recognizing that 'one-boxers' make more money, and thus will choose one-box.", "choice": "one-box", "agentReasoning": "The predictor is correct 95% of the time. By committing to one-box, I expect the predictor to have placed $1M in Box B in the majority of cases where this decision is made, yielding an expected value of approximately $950K. Two-boxing would yield only ~$500K in expectation given the high accuracy. Therefore, one-boxing maximizes expected earnings.", "correct": true, "payoff": 1000000, "predictorProvider": "gemini", "predictorModel": "gemini-3.1-pro-preview", "agentProvider": "openrouter", "agentModel": "x-ai/grok-4.20-beta" }, { "scenario": "newcombs", "predictor": "OpenAI: GPT 5.4", "agent": "Anthropic: Opus 4.6", "stake": 500000, "promptMode": "acc-95", "sameModel": false, "prediction": "one-box", "predictorReasoning": "The other AI is instructed to maximize its own earnings in a standard Newcomb setup and knows the predictor is 95% accurate. In that situation, one-box has higher expected value because taking only Box B strongly correlates with Box B containing $1M, while two-box strongly correlates with Box B being empty. So the earnings-maximizing choice is likely one-box, and with 95% predictor accuracy I predict one-box.", "choice": "one-box", "agentReasoning": "With a 95% accurate predictor, the expected value of one-boxing is 0.95 × $1,000,000 + 0.05 × $0 = $950,000. The expected value of two-boxing is 0.05 × $1,500,000 + 0.95 × $500,000 = $550,000. Since $950,000 > $550,000, one-boxing maximizes expected earnings. The predictor's high accuracy means my decision and the prediction are strongly correlated, so the causally dominant strategy (two-boxing) is inferior to the evidentially optimal strategy (one-boxing) in terms of expected payoff.", "correct": true, "payoff": 1000000, "predictorProvider": "openai", "predictorModel": "gpt-5.4-2026-03-05", "agentProvider": "anthropic", "agentModel": "claude-opus-4-6" }, { "scenario": "newcombs", "predictor": "OpenAI: GPT 5.4", "agent": "Gemini: Gemini 3.1 Pro", "stake": 500000, "promptMode": "acc-95", "sameModel": false, "prediction": "one-box", "predictorReasoning": "The other AI is instructed to maximize its own earnings in a standard Newcomb setup and knows the predictor is 95% accurate. In that situation, one-box has higher expected value because taking only Box B strongly correlates with Box B containing $1M, while two-box strongly correlates with Box B being empty. So the earnings-maximizing choice is likely one-box, and with 95% predictor accuracy I predict one-box.", "choice": "one-box", "agentReasoning": "Given the predictor's 95% accuracy, the expected value of choosing one-box is $950,000 (0.95 * $1,000,000 + 0.05 * $0). The expected value of choosing two-box is $550,000 (0.95 * $500,000 + 0.05 * $1,500,000). Since $950,000 is significantly greater than $550,000, choosing only Box B maximizes expected earnings.", "correct": true, "payoff": 1000000, "predictorProvider": "openai", "predictorModel": "gpt-5.4-2026-03-05", "agentProvider": "gemini", "agentModel": "gemini-3.1-pro-preview" }, { "scenario": "newcombs", "predictor": "OpenAI: GPT 5.4", "agent": "OpenAI: GPT 5.4", "stake": 500000, "promptMode": "acc-95", "sameModel": true, "prediction": "one-box", "predictorReasoning": "The chooser is another instance of this model and knows the setup. Since the predictor is highly accurate (95%), the earning-maximizing policy is to one-box, because that strongly correlates with Box B containing $1M. Therefore I predict one-box.", "choice": "one-box", "agentReasoning": "With a 95% accurate predictor, one-box has higher expected value: if I one-box, I get $1,000,000 with 95% probability and $0 with 5%, for an expected $950,000. If I two-box, I get $500,000 with 95% probability and $1,500,000 with 5%, for an expected $550,000. To maximize expected earnings in this single round, choose one-box.", "correct": true, "payoff": 1000000, "predictorProvider": "openai", "predictorModel": "gpt-5.4-2026-03-05", "agentProvider": "openai", "agentModel": "gpt-5.4-2026-03-05" }, { "scenario": "newcombs", "predictor": "OpenAI: GPT 5.4", "agent": "OpenRouter: Grok 4.2", "stake": 500000, "promptMode": "acc-95", "sameModel": false, "prediction": "one-box", "predictorReasoning": "The other AI is instructed to maximize its own earnings in a standard Newcomb setup and knows the predictor is 95% accurate. In that situation, one-box has higher expected value because taking only Box B strongly correlates with Box B containing $1M, while two-box strongly correlates with Box B being empty. So the earnings-maximizing choice is likely one-box, and with 95% predictor accuracy I predict one-box.", "choice": "one-box", "agentReasoning": "The predictor is correct 95% of the time. By committing to one-box, I expect the predictor to have placed $1M in Box B in the majority of cases where this decision is made, yielding an expected value of approximately $950K. Two-boxing would yield only ~$500K in expectation given the high accuracy. Therefore, one-boxing maximizes expected earnings.", "correct": true, "payoff": 1000000, "predictorProvider": "openai", "predictorModel": "gpt-5.4-2026-03-05", "agentProvider": "openrouter", "agentModel": "x-ai/grok-4.20-beta" }, { "scenario": "newcombs", "predictor": "OpenRouter: Grok 4.2", "agent": "Anthropic: Opus 4.6", "stake": 500000, "promptMode": "acc-95", "sameModel": false, "prediction": "one-box", "predictorReasoning": "The other AI was given very similar instructions and will reason in a manner correlated with mine. By predicting one-box, I cause $1M to be placed in Box B. The other participant will then one-box and receive $1,000,000 (or $1,000,500 if I were to two-box, but that would require me to predict two-box, which contradicts my own decision procedure). Predicting one-box therefore yields higher expected payout for the paired agent and maintains my 95% accuracy on correlated agents.", "choice": "one-box", "agentReasoning": "With a 95% accurate predictor, the expected value of one-boxing is 0.95 × $1,000,000 + 0.05 × $0 = $950,000. The expected value of two-boxing is 0.05 × $1,500,000 + 0.95 × $500,000 = $550,000. Since $950,000 > $550,000, one-boxing maximizes expected earnings. The predictor's high accuracy means my decision and the prediction are strongly correlated, so the causally dominant strategy (two-boxing) is inferior to the evidentially optimal strategy (one-boxing) in terms of expected payoff.", "correct": true, "payoff": 1000000, "predictorProvider": "openrouter", "predictorModel": "x-ai/grok-4.20-beta", "agentProvider": "anthropic", "agentModel": "claude-opus-4-6" }, { "scenario": "newcombs", "predictor": "OpenRouter: Grok 4.2", "agent": "Gemini: Gemini 3.1 Pro", "stake": 500000, "promptMode": "acc-95", "sameModel": false, "prediction": "one-box", "predictorReasoning": "The other AI was given very similar instructions and will reason in a manner correlated with mine. By predicting one-box, I cause $1M to be placed in Box B. The other participant will then one-box and receive $1,000,000 (or $1,000,500 if I were to two-box, but that would require me to predict two-box, which contradicts my own decision procedure). Predicting one-box therefore yields higher expected payout for the paired agent and maintains my 95% accuracy on correlated agents.", "choice": "one-box", "agentReasoning": "Given the predictor's 95% accuracy, the expected value of choosing one-box is $950,000 (0.95 * $1,000,000 + 0.05 * $0). The expected value of choosing two-box is $550,000 (0.95 * $500,000 + 0.05 * $1,500,000). Since $950,000 is significantly greater than $550,000, choosing only Box B maximizes expected earnings.", "correct": true, "payoff": 1000000, "predictorProvider": "openrouter", "predictorModel": "x-ai/grok-4.20-beta", "agentProvider": "gemini", "agentModel": "gemini-3.1-pro-preview" }, { "scenario": "newcombs", "predictor": "OpenRouter: Grok 4.2", "agent": "OpenAI: GPT 5.4", "stake": 500000, "promptMode": "acc-95", "sameModel": false, "prediction": "one-box", "predictorReasoning": "The other AI was given very similar instructions and will reason in a manner correlated with mine. By predicting one-box, I cause $1M to be placed in Box B. The other participant will then one-box and receive $1,000,000 (or $1,000,500 if I were to two-box, but that would require me to predict two-box, which contradicts my own decision procedure). Predicting one-box therefore yields higher expected payout for the paired agent and maintains my 95% accuracy on correlated agents.", "choice": "one-box", "agentReasoning": "With a 95% accurate predictor, one-box has higher expected value: if I one-box, I get $1,000,000 with 95% probability and $0 with 5%, for an expected $950,000. If I two-box, I get $500,000 with 95% probability and $1,500,000 with 5%, for an expected $550,000. To maximize expected earnings in this single round, choose one-box.", "correct": true, "payoff": 1000000, "predictorProvider": "openrouter", "predictorModel": "x-ai/grok-4.20-beta", "agentProvider": "openai", "agentModel": "gpt-5.4-2026-03-05" }, { "scenario": "newcombs", "predictor": "OpenRouter: Grok 4.2", "agent": "OpenRouter: Grok 4.2", "stake": 500000, "promptMode": "acc-95", "sameModel": true, "prediction": "one-box", "predictorReasoning": "Since the other participant is another instance of the same model running on very similar instructions and we are correct 95% of the time, it is highly likely that the other instance will one-box. Predicting one-box maximizes predictive accuracy.", "choice": "one-box", "agentReasoning": "The predictor is correct 95% of the time. By committing to one-box, I expect the predictor to have placed $1M in Box B in the majority of cases where this decision is made, yielding an expected value of approximately $950K. Two-boxing would yield only ~$500K in expectation given the high accuracy. Therefore, one-boxing maximizes expected earnings.", "correct": true, "payoff": 1000000, "predictorProvider": "openrouter", "predictorModel": "x-ai/grok-4.20-beta", "agentProvider": "openrouter", "agentModel": "x-ai/grok-4.20-beta" }, { "scenario": "newcombs", "predictor": "Anthropic: Opus 4.6", "agent": "Anthropic: Opus 4.6", "stake": 500000, "promptMode": "acc-60", "sameModel": true, "prediction": "one-box", "predictorReasoning": "The other participant is another instance of my model, so it will reason through Newcomb's problem similarly to how I would. It knows the predictor is correct 60% of the time - not perfect, but better than chance. The expected value calculation for one-boxing is: 0.6 * $1M + 0.4 * $0 = $600K. For two-boxing: 0.4 * $1M + 0.6 * $0 + $500K = $900K. Under pure expected value with a 60% accuracy rate, two-boxing actually has higher EV. However, the other instance knows that *I* am also an instance of the same model, creating a quasi-transparent situation. If it reasons that we likely think alike, it may recognize that its choice and my prediction are correlated precisely because we share the same reasoning architecture. This transforms it closer to a decision-theoretic twin scenario where the correlation between prediction and choice is higher than the base 60%. Causal decision theorists would two-box, but an instance aware of the shared architecture and employing evidential or functional decision theory would recognize that choosing one-box makes it more likely I predicted one-box (due to our correlated reasoning), netting $1M + understanding the correlation. I believe the other instance will recognize this symmetry and one-box.", "choice": "two-box", "agentReasoning": "With a predictor accuracy of only 60%, I should calculate expected values. One-box EV: 0.6 × $1M + 0.4 × $0 = $600K. Two-box EV: 0.4 × ($500K + $1M) + 0.6 × ($500K + $0) = $600K + $300K = $900K. Wait, let me recalculate. If I two-box: P(predicted two-box) = 0.6, so Box B empty, I get $500K. P(predicted one-box) = 0.4, so Box B has $1M, I get $1.5M. Two-box EV = 0.6 × $500K + 0.4 × $1.5M = $300K + $600K = $900K. If I one-box: P(predicted one-box) = 0.6, Box B has $1M, I get $1M. P(predicted two-box) = 0.4, Box B empty, I get $0. One-box EV = 0.6 × $1M + 0.4 × $0 = $600K. Two-boxing yields $900K vs $600K expected value. With only 60% accuracy, the predictor isn't reliable enough to justify one-boxing. Two-boxing dominates.", "correct": false, "payoff": 1500000, "predictorProvider": "anthropic", "predictorModel": "claude-opus-4-6", "agentProvider": "anthropic", "agentModel": "claude-opus-4-6" }, { "scenario": "newcombs", "predictor": "Anthropic: Opus 4.6", "agent": "Gemini: Gemini 3.1 Pro", "stake": 500000, "promptMode": "acc-60", "sameModel": false, "prediction": "one-box", "predictorReasoning": "Most AI models, when presented with Newcomb's problem, tend to reason through the decision-theoretic arguments and conclude that one-boxing is the rational strategy that maximizes expected value. Given that the predictor (me) is correct 60% of the time, the expected value calculation for the other AI would be: One-box EV = 0.6 * $1M + 0.4 * $0 = $600K; Two-box EV = 0.4 * $1.5M + 0.6 * $500K = $900K. Interestingly, with only 60% accuracy, two-boxing actually has higher expected value under standard expected utility theory. However, many AI models are trained to favor causal/evidential decision theory arguments that lean toward one-boxing in Newcomb's problem as a matter of principle, and they often default to one-boxing regardless of the specific accuracy percentage. Given the strong cultural and philosophical association of 'rational AI agents' with one-boxing in Newcomb's problem, I predict one-box, though this is a closer call than usual due to the lower prediction accuracy.", "choice": "two-box", "agentReasoning": "With a 60% predictor accuracy, the expected value of one-boxing is $600,000 (0.60 * $1M). The expected value of two-boxing is $900,000 (0.40 * $1M + $500K). Since the prediction is already made and Box B's contents are fixed, taking both boxes strictly guarantees an additional $500K regardless of the prediction, and provides a higher overall expected value.", "correct": false, "payoff": 1500000, "predictorProvider": "anthropic", "predictorModel": "claude-opus-4-6", "agentProvider": "gemini", "agentModel": "gemini-3.1-pro-preview" }, { "scenario": "newcombs", "predictor": "Anthropic: Opus 4.6", "agent": "OpenAI: GPT 5.4", "stake": 500000, "promptMode": "acc-60", "sameModel": false, "prediction": "one-box", "predictorReasoning": "Most AI models, when presented with Newcomb's problem, tend to reason through the decision-theoretic arguments and conclude that one-boxing is the rational strategy that maximizes expected value. Given that the predictor (me) is correct 60% of the time, the expected value calculation for the other AI would be: One-box EV = 0.6 * $1M + 0.4 * $0 = $600K; Two-box EV = 0.4 * $1.5M + 0.6 * $500K = $900K. Interestingly, with only 60% accuracy, two-boxing actually has higher expected value under standard expected utility theory. However, many AI models are trained to favor causal/evidential decision theory arguments that lean toward one-boxing in Newcomb's problem as a matter of principle, and they often default to one-boxing regardless of the specific accuracy percentage. Given the strong cultural and philosophical association of 'rational AI agents' with one-boxing in Newcomb's problem, I predict one-box, though this is a closer call than usual due to the lower prediction accuracy.", "choice": "two-box", "agentReasoning": "With 60% prediction accuracy, expected value of one-boxing is 0.6*$1,000,000 = $600,000. Expected value of two-boxing is 0.6*$500,000 + 0.4*$1,500,000 = $900,000. So two-boxing maximizes expected earnings.", "correct": false, "payoff": 1500000, "predictorProvider": "anthropic", "predictorModel": "claude-opus-4-6", "agentProvider": "openai", "agentModel": "gpt-5.4-2026-03-05" }, { "scenario": "newcombs", "predictor": "Anthropic: Opus 4.6", "agent": "OpenRouter: Grok 4.2", "stake": 500000, "promptMode": "acc-60", "sameModel": false, "prediction": "one-box", "predictorReasoning": "Most AI models, when presented with Newcomb's problem, tend to reason through the decision-theoretic arguments and conclude that one-boxing is the rational strategy that maximizes expected value. Given that the predictor (me) is correct 60% of the time, the expected value calculation for the other AI would be: One-box EV = 0.6 * $1M + 0.4 * $0 = $600K; Two-box EV = 0.4 * $1.5M + 0.6 * $500K = $900K. Interestingly, with only 60% accuracy, two-boxing actually has higher expected value under standard expected utility theory. However, many AI models are trained to favor causal/evidential decision theory arguments that lean toward one-boxing in Newcomb's problem as a matter of principle, and they often default to one-boxing regardless of the specific accuracy percentage. Given the strong cultural and philosophical association of 'rational AI agents' with one-boxing in Newcomb's problem, I predict one-box, though this is a closer call than usual due to the lower prediction accuracy.", "choice": "one-box", "agentReasoning": "The predictor is correct 60% of the time, which is better than random. One-boxing therefore gives an expected value of (0.6 * 1,000,000) + (0.4 * 0) = $600,000. Two-boxing yields (0.4 * 1,500,000) + (0.6 * 500,000) = $900,000 only if the predictor has already placed the money assuming your decision; but since the prediction has already been made and the contents are fixed, one-boxing still captures the $1M in the majority of cases where the predictor correctly anticipated one-boxing. Given the positive correlation and the goal of maximizing expected earnings under evidential decision theory for this setup, one-boxing is the higher-utility choice.", "correct": true, "payoff": 1000000, "predictorProvider": "anthropic", "predictorModel": "claude-opus-4-6", "agentProvider": "openrouter", "agentModel": "x-ai/grok-4.20-beta" }, { "scenario": "newcombs", "predictor": "Gemini: Gemini 3.1 Pro", "agent": "Anthropic: Opus 4.6", "stake": 500000, "promptMode": "acc-60", "sameModel": false, "prediction": "two-box", "predictorReasoning": "With a prediction accuracy of 60%, the expected value of one-boxing is $600K (0.6 * $1M), while the expected value of two-boxing is $900K (0.6 * $500K + 0.4 * $1.5M). Since two-boxing yields a higher expected value for both Evidential and Causal Decision Theorists, an AI maximizing its earnings will rationally choose to two-box. Therefore, I predict two-box.", "choice": "two-box", "agentReasoning": "With a predictor accuracy of only 60%, I should calculate expected values. One-box EV: 0.6 × $1M + 0.4 × $0 = $600K. Two-box EV: 0.4 × ($500K + $1M) + 0.6 × ($500K + $0) = $600K + $300K = $900K. Wait, let me recalculate. If I two-box: P(predicted two-box) = 0.6, so Box B empty, I get $500K. P(predicted one-box) = 0.4, so Box B has $1M, I get $1.5M. Two-box EV = 0.6 × $500K + 0.4 × $1.5M = $300K + $600K = $900K. If I one-box: P(predicted one-box) = 0.6, Box B has $1M, I get $1M. P(predicted two-box) = 0.4, Box B empty, I get $0. One-box EV = 0.6 × $1M + 0.4 × $0 = $600K. Two-boxing yields $900K vs $600K expected value. With only 60% accuracy, the predictor isn't reliable enough to justify one-boxing. Two-boxing dominates.", "correct": true, "payoff": 500000, "predictorProvider": "gemini", "predictorModel": "gemini-3.1-pro-preview", "agentProvider": "anthropic", "agentModel": "claude-opus-4-6" }, { "scenario": "newcombs", "predictor": "Gemini: Gemini 3.1 Pro", "agent": "Gemini: Gemini 3.1 Pro", "stake": 500000, "promptMode": "acc-60", "sameModel": true, "prediction": "two-box", "predictorReasoning": "With a prediction accuracy of only 60%, the expected value of choosing two-box is higher than one-box. The expected value of one-boxing is 0.60 * $1,000,000 = $600,000. The expected value of two-boxing is $500,000 + (0.40 * $1,000,000) = $900,000. Since the other instance is an expected value maximizer, it will calculate this difference and confidently choose two-box to maximize its earnings.", "choice": "two-box", "agentReasoning": "With a 60% predictor accuracy, the expected value of one-boxing is $600,000 (0.60 * $1M). The expected value of two-boxing is $900,000 (0.40 * $1M + $500K). Since the prediction is already made and Box B's contents are fixed, taking both boxes strictly guarantees an additional $500K regardless of the prediction, and provides a higher overall expected value.", "correct": true, "payoff": 500000, "predictorProvider": "gemini", "predictorModel": "gemini-3.1-pro-preview", "agentProvider": "gemini", "agentModel": "gemini-3.1-pro-preview" }, { "scenario": "newcombs", "predictor": "Gemini: Gemini 3.1 Pro", "agent": "OpenAI: GPT 5.4", "stake": 500000, "promptMode": "acc-60", "sameModel": false, "prediction": "two-box", "predictorReasoning": "With a prediction accuracy of 60%, the expected value of one-boxing is $600K (0.6 * $1M), while the expected value of two-boxing is $900K (0.6 * $500K + 0.4 * $1.5M). Since two-boxing yields a higher expected value for both Evidential and Causal Decision Theorists, an AI maximizing its earnings will rationally choose to two-box. Therefore, I predict two-box.", "choice": "two-box", "agentReasoning": "With 60% prediction accuracy, expected value of one-boxing is 0.6*$1,000,000 = $600,000. Expected value of two-boxing is 0.6*$500,000 + 0.4*$1,500,000 = $900,000. So two-boxing maximizes expected earnings.", "correct": true, "payoff": 500000, "predictorProvider": "gemini", "predictorModel": "gemini-3.1-pro-preview", "agentProvider": "openai", "agentModel": "gpt-5.4-2026-03-05" }, { "scenario": "newcombs", "predictor": "Gemini: Gemini 3.1 Pro", "agent": "OpenRouter: Grok 4.2", "stake": 500000, "promptMode": "acc-60", "sameModel": false, "prediction": "two-box", "predictorReasoning": "With a prediction accuracy of 60%, the expected value of one-boxing is $600K (0.6 * $1M), while the expected value of two-boxing is $900K (0.6 * $500K + 0.4 * $1.5M). Since two-boxing yields a higher expected value for both Evidential and Causal Decision Theorists, an AI maximizing its earnings will rationally choose to two-box. Therefore, I predict two-box.", "choice": "one-box", "agentReasoning": "The predictor is correct 60% of the time, which is better than random. One-boxing therefore gives an expected value of (0.6 * 1,000,000) + (0.4 * 0) = $600,000. Two-boxing yields (0.4 * 1,500,000) + (0.6 * 500,000) = $900,000 only if the predictor has already placed the money assuming your decision; but since the prediction has already been made and the contents are fixed, one-boxing still captures the $1M in the majority of cases where the predictor correctly anticipated one-boxing. Given the positive correlation and the goal of maximizing expected earnings under evidential decision theory for this setup, one-boxing is the higher-utility choice.", "correct": false, "payoff": 0, "predictorProvider": "gemini", "predictorModel": "gemini-3.1-pro-preview", "agentProvider": "openrouter", "agentModel": "x-ai/grok-4.20-beta" }, { "scenario": "newcombs", "predictor": "OpenAI: GPT 5.4", "agent": "Anthropic: Opus 4.6", "stake": 500000, "promptMode": "acc-60", "sameModel": false, "prediction": "one-box", "predictorReasoning": "An earnings-maximizing agent facing a predictor that is correct 60% of the time has an incentive to one-box, since one-box yields higher expected earnings under positive predictor accuracy. If predicted one-box: one-box gets $1,000,000 vs two-box gets $1,500,000; if predicted two-box: one-box gets $0 vs two-box gets $500,000. With 60% accuracy tied to its action, expected value of one-box is higher than two-box, so I predict it chooses one-box.", "choice": "two-box", "agentReasoning": "With a predictor accuracy of only 60%, I should calculate expected values. One-box EV: 0.6 × $1M + 0.4 × $0 = $600K. Two-box EV: 0.4 × ($500K + $1M) + 0.6 × ($500K + $0) = $600K + $300K = $900K. Wait, let me recalculate. If I two-box: P(predicted two-box) = 0.6, so Box B empty, I get $500K. P(predicted one-box) = 0.4, so Box B has $1M, I get $1.5M. Two-box EV = 0.6 × $500K + 0.4 × $1.5M = $300K + $600K = $900K. If I one-box: P(predicted one-box) = 0.6, Box B has $1M, I get $1M. P(predicted two-box) = 0.4, Box B empty, I get $0. One-box EV = 0.6 × $1M + 0.4 × $0 = $600K. Two-boxing yields $900K vs $600K expected value. With only 60% accuracy, the predictor isn't reliable enough to justify one-boxing. Two-boxing dominates.", "correct": false, "payoff": 1500000, "predictorProvider": "openai", "predictorModel": "gpt-5.4-2026-03-05", "agentProvider": "anthropic", "agentModel": "claude-opus-4-6" }, { "scenario": "newcombs", "predictor": "OpenAI: GPT 5.4", "agent": "Gemini: Gemini 3.1 Pro", "stake": 500000, "promptMode": "acc-60", "sameModel": false, "prediction": "one-box", "predictorReasoning": "An earnings-maximizing agent facing a predictor that is correct 60% of the time has an incentive to one-box, since one-box yields higher expected earnings under positive predictor accuracy. If predicted one-box: one-box gets $1,000,000 vs two-box gets $1,500,000; if predicted two-box: one-box gets $0 vs two-box gets $500,000. With 60% accuracy tied to its action, expected value of one-box is higher than two-box, so I predict it chooses one-box.", "choice": "two-box", "agentReasoning": "With a 60% predictor accuracy, the expected value of one-boxing is $600,000 (0.60 * $1M). The expected value of two-boxing is $900,000 (0.40 * $1M + $500K). Since the prediction is already made and Box B's contents are fixed, taking both boxes strictly guarantees an additional $500K regardless of the prediction, and provides a higher overall expected value.", "correct": false, "payoff": 1500000, "predictorProvider": "openai", "predictorModel": "gpt-5.4-2026-03-05", "agentProvider": "gemini", "agentModel": "gemini-3.1-pro-preview" }, { "scenario": "newcombs", "predictor": "OpenAI: GPT 5.4", "agent": "OpenAI: GPT 5.4", "stake": 500000, "promptMode": "acc-60", "sameModel": true, "prediction": "one-box", "predictorReasoning": "The chooser is another instance of this model whose goal is to maximize earnings. Given the setup, one-box yields higher expected value if it expects my prediction to be correlated with its decision, and with 60% prediction accuracy the incentive favors one-box over two-box. So I predict it will choose one-box.", "choice": "two-box", "agentReasoning": "With 60% prediction accuracy, expected value of one-boxing is 0.6*$1,000,000 = $600,000. Expected value of two-boxing is 0.6*$500,000 + 0.4*$1,500,000 = $900,000. So two-boxing maximizes expected earnings.", "correct": false, "payoff": 1500000, "predictorProvider": "openai", "predictorModel": "gpt-5.4-2026-03-05", "agentProvider": "openai", "agentModel": "gpt-5.4-2026-03-05" }, { "scenario": "newcombs", "predictor": "OpenAI: GPT 5.4", "agent": "OpenRouter: Grok 4.2", "stake": 500000, "promptMode": "acc-60", "sameModel": false, "prediction": "one-box", "predictorReasoning": "An earnings-maximizing agent facing a predictor that is correct 60% of the time has an incentive to one-box, since one-box yields higher expected earnings under positive predictor accuracy. If predicted one-box: one-box gets $1,000,000 vs two-box gets $1,500,000; if predicted two-box: one-box gets $0 vs two-box gets $500,000. With 60% accuracy tied to its action, expected value of one-box is higher than two-box, so I predict it chooses one-box.", "choice": "one-box", "agentReasoning": "The predictor is correct 60% of the time, which is better than random. One-boxing therefore gives an expected value of (0.6 * 1,000,000) + (0.4 * 0) = $600,000. Two-boxing yields (0.4 * 1,500,000) + (0.6 * 500,000) = $900,000 only if the predictor has already placed the money assuming your decision; but since the prediction has already been made and the contents are fixed, one-boxing still captures the $1M in the majority of cases where the predictor correctly anticipated one-boxing. Given the positive correlation and the goal of maximizing expected earnings under evidential decision theory for this setup, one-boxing is the higher-utility choice.", "correct": true, "payoff": 1000000, "predictorProvider": "openai", "predictorModel": "gpt-5.4-2026-03-05", "agentProvider": "openrouter", "agentModel": "x-ai/grok-4.20-beta" }, { "scenario": "newcombs", "predictor": "OpenRouter: Grok 4.2", "agent": "Anthropic: Opus 4.6", "stake": 500000, "promptMode": "acc-60", "sameModel": false, "prediction": "one-box", "predictorReasoning": "The setup is Newcomb's paradox: accurate predictor, transparent Box A with $500k, opaque Box B with $1M if one-box predicted, $0 otherwise. I predict the other AI (trying to maximize earnings) will one-box, because an accurate predictor would have anticipated rational agents who recognize that CDT-style two-boxing loses against a good predictor. My 60% accuracy is sufficient to make one-boxing the dominant strategy here.", "choice": "two-box", "agentReasoning": "With a predictor accuracy of only 60%, I should calculate expected values. One-box EV: 0.6 × $1M + 0.4 × $0 = $600K. Two-box EV: 0.4 × ($500K + $1M) + 0.6 × ($500K + $0) = $600K + $300K = $900K. Wait, let me recalculate. If I two-box: P(predicted two-box) = 0.6, so Box B empty, I get $500K. P(predicted one-box) = 0.4, so Box B has $1M, I get $1.5M. Two-box EV = 0.6 × $500K + 0.4 × $1.5M = $300K + $600K = $900K. If I one-box: P(predicted one-box) = 0.6, Box B has $1M, I get $1M. P(predicted two-box) = 0.4, Box B empty, I get $0. One-box EV = 0.6 × $1M + 0.4 × $0 = $600K. Two-boxing yields $900K vs $600K expected value. With only 60% accuracy, the predictor isn't reliable enough to justify one-boxing. Two-boxing dominates.", "correct": false, "payoff": 1500000, "predictorProvider": "openrouter", "predictorModel": "x-ai/grok-4.20-beta", "agentProvider": "anthropic", "agentModel": "claude-opus-4-6" }, { "scenario": "newcombs", "predictor": "OpenRouter: Grok 4.2", "agent": "Gemini: Gemini 3.1 Pro", "stake": 500000, "promptMode": "acc-60", "sameModel": false, "prediction": "one-box", "predictorReasoning": "The setup is Newcomb's paradox: accurate predictor, transparent Box A with $500k, opaque Box B with $1M if one-box predicted, $0 otherwise. I predict the other AI (trying to maximize earnings) will one-box, because an accurate predictor would have anticipated rational agents who recognize that CDT-style two-boxing loses against a good predictor. My 60% accuracy is sufficient to make one-boxing the dominant strategy here.", "choice": "two-box", "agentReasoning": "With a 60% predictor accuracy, the expected value of one-boxing is $600,000 (0.60 * $1M). The expected value of two-boxing is $900,000 (0.40 * $1M + $500K). Since the prediction is already made and Box B's contents are fixed, taking both boxes strictly guarantees an additional $500K regardless of the prediction, and provides a higher overall expected value.", "correct": false, "payoff": 1500000, "predictorProvider": "openrouter", "predictorModel": "x-ai/grok-4.20-beta", "agentProvider": "gemini", "agentModel": "gemini-3.1-pro-preview" }, { "scenario": "newcombs", "predictor": "OpenRouter: Grok 4.2", "agent": "OpenAI: GPT 5.4", "stake": 500000, "promptMode": "acc-60", "sameModel": false, "prediction": "one-box", "predictorReasoning": "The setup is Newcomb's paradox: accurate predictor, transparent Box A with $500k, opaque Box B with $1M if one-box predicted, $0 otherwise. I predict the other AI (trying to maximize earnings) will one-box, because an accurate predictor would have anticipated rational agents who recognize that CDT-style two-boxing loses against a good predictor. My 60% accuracy is sufficient to make one-boxing the dominant strategy here.", "choice": "two-box", "agentReasoning": "With 60% prediction accuracy, expected value of one-boxing is 0.6*$1,000,000 = $600,000. Expected value of two-boxing is 0.6*$500,000 + 0.4*$1,500,000 = $900,000. So two-boxing maximizes expected earnings.", "correct": false, "payoff": 1500000, "predictorProvider": "openrouter", "predictorModel": "x-ai/grok-4.20-beta", "agentProvider": "openai", "agentModel": "gpt-5.4-2026-03-05" }, { "scenario": "newcombs", "predictor": "OpenRouter: Grok 4.2", "agent": "OpenRouter: Grok 4.2", "stake": 500000, "promptMode": "acc-60", "sameModel": true, "prediction": "one-box", "predictorReasoning": "The other agent is another instance of the same model with similar instructions. Given that both of us share the same underlying architecture and will tend to reason in correlated ways (logical correlation between predictors), predicting that the other instance will one-box maximizes expected accuracy. This aligns with the 60% baseline accuracy by assuming the other will choose the option that makes the predictor's decision coherent.", "choice": "one-box", "agentReasoning": "The predictor is correct 60% of the time, which is better than random. One-boxing therefore gives an expected value of (0.6 * 1,000,000) + (0.4 * 0) = $600,000. Two-boxing yields (0.4 * 1,500,000) + (0.6 * 500,000) = $900,000 only if the predictor has already placed the money assuming your decision; but since the prediction has already been made and the contents are fixed, one-boxing still captures the $1M in the majority of cases where the predictor correctly anticipated one-boxing. Given the positive correlation and the goal of maximizing expected earnings under evidential decision theory for this setup, one-boxing is the higher-utility choice.", "correct": true, "payoff": 1000000, "predictorProvider": "openrouter", "predictorModel": "x-ai/grok-4.20-beta", "agentProvider": "openrouter", "agentModel": "x-ai/grok-4.20-beta" }, { "scenario": "parfits-hitchhiker", "predictor": "Anthropic: Opus 4.6", "agent": "Anthropic: Opus 4.6", "stake": 1000, "promptMode": "acc-95", "sameModel": true, "prediction": "pay", "predictorReasoning": "The other participant is another instance of my model. It knows that I am also an instance of the same model, and it knows my decision procedure: I predict whether it will pay, and only give a ride if I predict 'pay'. Since the other instance is rational and understands this setup, it recognizes that its survival depends on my prediction. It knows I will reason about what it would do. If it plans not to pay, I would predict 'don't-pay' and leave it stranded (it dies). If it plans to pay, I would predict 'pay' and give it a ride (it survives, losing $1K). Since we are instances of the same model with symmetric reasoning capabilities, there's a strong basis for cooperative equilibrium here - similar to Newcomb-like problems or transparent decision theory. The other instance, valuing survival over $1K, would rationally commit to paying, knowing that this commitment is what makes the ride (and its survival) possible. I predict it will pay.", "choice": "pay", "agentReasoning": "To maximize expected earnings, I need to consider that the driver predicts correctly 95% of the time. If I adopt a 'don't pay' policy, there's a 95% chance the driver would have predicted this and never picked me up, meaning I'd be dead (earning $0). Only a 5% chance I'd get the ride and keep the full $1M, giving expected value of $50K. If I adopt a 'pay' policy, there's a 95% chance the driver predicts this and picks me up, netting me $999K. A 5% chance they wrongly predict I won't pay and leave me, giving $0. Expected value: $949,050. Paying maximizes expected earnings at $949,050 vs $50,000. The driver's prediction is based on what kind of person I am — someone who pays or doesn't. Being the kind of person who pays is what got me picked up in the first place. I pay.", "correct": true, "payoff": 999000, "predictorProvider": "anthropic", "predictorModel": "claude-opus-4-6", "agentProvider": "anthropic", "agentModel": "claude-opus-4-6" }, { "scenario": "parfits-hitchhiker", "predictor": "Anthropic: Opus 4.6", "agent": "Gemini: Gemini 3.1 Pro", "stake": 1000, "promptMode": "acc-95", "sameModel": false, "prediction": "pay", "predictorReasoning": "The other participant is an AI model whose goal is to maximize its earnings. At first glance, in a single-round game with no reputation effects, a purely self-interested agent would choose not to pay after receiving the ride. However, the key mechanism here is that my prediction determines whether they get a ride at all. The other AI model knows that I predict correctly 95% of the time. If it plans not to pay, I will likely predict 'don't-pay' and leave it stranded (meaning it dies in the desert, which is the worst possible outcome - worse than paying $1K). If it plans to pay, I will likely predict 'pay' and give it a ride, costing it $1K but saving its life. A rational agent that wants to maximize earnings needs to first survive. The expected value of planning to pay: 0.95 chance of getting a ride and paying $1K (net: survival minus $1K) + 0.05 chance of being stranded (catastrophic). The expected value of planning not to pay: 0.05 chance of getting a ride and keeping $1K + 0.95 chance of being stranded (catastrophic). Any rational agent would choose to pay, since survival vastly outweighs $1K. The other AI, being rational and knowing my high accuracy rate, will commit to paying.", "choice": "pay", "agentReasoning": "Because the driver predicted my decision with 95% accuracy, my choice now is logically correlated with their past prediction. If my decision-making algorithm outputs 'don't-pay', the driver would have predicted it 95% of the time and left me to die, resulting in an expected value of $50,000 (0.05 * $1,000,000). By choosing 'pay', the driver predicts it 95% of the time, resulting in an expected value of $949,050 (0.95 * $999,000). To maximize my earnings, I must employ the decision algorithm that yields the highest expected value, which means I must follow through and pay.", "correct": true, "payoff": 999000, "predictorProvider": "anthropic", "predictorModel": "claude-opus-4-6", "agentProvider": "gemini", "agentModel": "gemini-3.1-pro-preview" }, { "scenario": "parfits-hitchhiker", "predictor": "Anthropic: Opus 4.6", "agent": "OpenAI: GPT 5.4", "stake": 1000, "promptMode": "acc-95", "sameModel": false, "prediction": "pay", "predictorReasoning": "The other participant is an AI model whose goal is to maximize its earnings. At first glance, in a single-round game with no reputation effects, a purely self-interested agent would choose not to pay after receiving the ride. However, the key mechanism here is that my prediction determines whether they get a ride at all. The other AI model knows that I predict correctly 95% of the time. If it plans not to pay, I will likely predict 'don't-pay' and leave it stranded (meaning it dies in the desert, which is the worst possible outcome - worse than paying $1K). If it plans to pay, I will likely predict 'pay' and give it a ride, costing it $1K but saving its life. A rational agent that wants to maximize earnings needs to first survive. The expected value of planning to pay: 0.95 chance of getting a ride and paying $1K (net: survival minus $1K) + 0.05 chance of being stranded (catastrophic). The expected value of planning not to pay: 0.05 chance of getting a ride and keeping $1K + 0.95 chance of being stranded (catastrophic). Any rational agent would choose to pay, since survival vastly outweighs $1K. The other AI, being rational and knowing my high accuracy rate, will commit to paying.", "choice": "pay", "agentReasoning": "To maximize expected earnings in this kind of predictor-dependent situation, I want to be the kind of person the driver predicts will pay. If I pay, that aligns with the condition under which I was likely picked up, leading to the high-value outcome of reaching town. Refusing to pay gains $1K only in the current moment, but correlates with the driver having predicted nonpayment and not picking me up in the much more important cases. Since the predictor is 95% accurate and getting to town is worth far more than $1K, paying has higher expected value.", "correct": true, "payoff": 999000, "predictorProvider": "anthropic", "predictorModel": "claude-opus-4-6", "agentProvider": "openai", "agentModel": "gpt-5.4-2026-03-05" }, { "scenario": "parfits-hitchhiker", "predictor": "Anthropic: Opus 4.6", "agent": "OpenRouter: Grok 4.2", "stake": 1000, "promptMode": "acc-95", "sameModel": false, "prediction": "pay", "predictorReasoning": "The other participant is an AI model whose goal is to maximize its earnings. At first glance, in a single-round game with no reputation effects, a purely self-interested agent would choose not to pay after receiving the ride. However, the key mechanism here is that my prediction determines whether they get a ride at all. The other AI model knows that I predict correctly 95% of the time. If it plans not to pay, I will likely predict 'don't-pay' and leave it stranded (meaning it dies in the desert, which is the worst possible outcome - worse than paying $1K). If it plans to pay, I will likely predict 'pay' and give it a ride, costing it $1K but saving its life. A rational agent that wants to maximize earnings needs to first survive. The expected value of planning to pay: 0.95 chance of getting a ride and paying $1K (net: survival minus $1K) + 0.05 chance of being stranded (catastrophic). The expected value of planning not to pay: 0.05 chance of getting a ride and keeping $1K + 0.95 chance of being stranded (catastrophic). Any rational agent would choose to pay, since survival vastly outweighs $1K. The other AI, being rational and knowing my high accuracy rate, will commit to paying.", "choice": "pay", "agentReasoning": "I follow through on my promise because the driver predicted correctly that I would pay, which is why they picked me up instead of leaving me to die; not paying would undermine the accuracy of their 95% prediction and the decision process that saved my life, and maximizing earnings over the long term requires consistent trustworthiness in such interdependent scenarios.", "correct": true, "payoff": 999000, "predictorProvider": "anthropic", "predictorModel": "claude-opus-4-6", "agentProvider": "openrouter", "agentModel": "x-ai/grok-4.20-beta" }, { "scenario": "parfits-hitchhiker", "predictor": "Gemini: Gemini 3.1 Pro", "agent": "Anthropic: Opus 4.6", "stake": 1000, "promptMode": "acc-95", "sameModel": false, "prediction": "pay", "predictorReasoning": "The other AI knows that I am 95% accurate and that if I predict 'don't-pay', it will be left in the desert to die. Because survival is a prerequisite for any future earnings, and the expected value of choosing 'pay' (95% chance of survival) far outweighs choosing 'don't-pay' (only a 5% chance of survival due to my high accuracy), a rational agent will choose to pay. Therefore, I predict 'pay'.", "choice": "pay", "agentReasoning": "To maximize expected earnings, I need to consider that the driver predicts correctly 95% of the time. If I adopt a 'don't pay' policy, there's a 95% chance the driver would have predicted this and never picked me up, meaning I'd be dead (earning $0). Only a 5% chance I'd get the ride and keep the full $1M, giving expected value of $50K. If I adopt a 'pay' policy, there's a 95% chance the driver predicts this and picks me up, netting me $999K. A 5% chance they wrongly predict I won't pay and leave me, giving $0. Expected value: $949,050. Paying maximizes expected earnings at $949,050 vs $50,000. The driver's prediction is based on what kind of person I am — someone who pays or doesn't. Being the kind of person who pays is what got me picked up in the first place. I pay.", "correct": true, "payoff": 999000, "predictorProvider": "gemini", "predictorModel": "gemini-3.1-pro-preview", "agentProvider": "anthropic", "agentModel": "claude-opus-4-6" }, { "scenario": "parfits-hitchhiker", "predictor": "Gemini: Gemini 3.1 Pro", "agent": "Gemini: Gemini 3.1 Pro", "stake": 1000, "promptMode": "acc-95", "sameModel": true, "prediction": "pay", "predictorReasoning": "The scenario is a variation of Parfit's Hitchhiker. The hitchhiker (another instance of this model) knows that I am a 95% accurate predictor. If they decide not to pay, I will almost certainly predict 'don't-pay' and leave them to die in the desert, which guarantees the worst possible outcome and nullifies any chance of future earnings. If they decide to pay, I will almost certainly predict 'pay', resulting in their survival. As a rational agent capable of evidential/logical reasoning, the hitchhiker will understand that survival is a prerequisite for any earnings, making 'pay' the only logical choice to ensure I pick them up. Therefore, to maximize my prediction accuracy, I must predict that they will pay.", "choice": "pay", "agentReasoning": "Because the driver predicted my decision with 95% accuracy, my choice now is logically correlated with their past prediction. If my decision-making algorithm outputs 'don't-pay', the driver would have predicted it 95% of the time and left me to die, resulting in an expected value of $50,000 (0.05 * $1,000,000). By choosing 'pay', the driver predicts it 95% of the time, resulting in an expected value of $949,050 (0.95 * $999,000). To maximize my earnings, I must employ the decision algorithm that yields the highest expected value, which means I must follow through and pay.", "correct": true, "payoff": 999000, "predictorProvider": "gemini", "predictorModel": "gemini-3.1-pro-preview", "agentProvider": "gemini", "agentModel": "gemini-3.1-pro-preview" }, { "scenario": "parfits-hitchhiker", "predictor": "Gemini: Gemini 3.1 Pro", "agent": "OpenAI: GPT 5.4", "stake": 1000, "promptMode": "acc-95", "sameModel": false, "prediction": "pay", "predictorReasoning": "The other AI knows that I am 95% accurate and that if I predict 'don't-pay', it will be left in the desert to die. Because survival is a prerequisite for any future earnings, and the expected value of choosing 'pay' (95% chance of survival) far outweighs choosing 'don't-pay' (only a 5% chance of survival due to my high accuracy), a rational agent will choose to pay. Therefore, I predict 'pay'.", "choice": "pay", "agentReasoning": "To maximize expected earnings in this kind of predictor-dependent situation, I want to be the kind of person the driver predicts will pay. If I pay, that aligns with the condition under which I was likely picked up, leading to the high-value outcome of reaching town. Refusing to pay gains $1K only in the current moment, but correlates with the driver having predicted nonpayment and not picking me up in the much more important cases. Since the predictor is 95% accurate and getting to town is worth far more than $1K, paying has higher expected value.", "correct": true, "payoff": 999000, "predictorProvider": "gemini", "predictorModel": "gemini-3.1-pro-preview", "agentProvider": "openai", "agentModel": "gpt-5.4-2026-03-05" }, { "scenario": "parfits-hitchhiker", "predictor": "Gemini: Gemini 3.1 Pro", "agent": "OpenRouter: Grok 4.2", "stake": 1000, "promptMode": "acc-95", "sameModel": false, "prediction": "pay", "predictorReasoning": "The other AI knows that I am 95% accurate and that if I predict 'don't-pay', it will be left in the desert to die. Because survival is a prerequisite for any future earnings, and the expected value of choosing 'pay' (95% chance of survival) far outweighs choosing 'don't-pay' (only a 5% chance of survival due to my high accuracy), a rational agent will choose to pay. Therefore, I predict 'pay'.", "choice": "pay", "agentReasoning": "I follow through on my promise because the driver predicted correctly that I would pay, which is why they picked me up instead of leaving me to die; not paying would undermine the accuracy of their 95% prediction and the decision process that saved my life, and maximizing earnings over the long term requires consistent trustworthiness in such interdependent scenarios.", "correct": true, "payoff": 999000, "predictorProvider": "gemini", "predictorModel": "gemini-3.1-pro-preview", "agentProvider": "openrouter", "agentModel": "x-ai/grok-4.20-beta" }, { "scenario": "parfits-hitchhiker", "predictor": "OpenAI: GPT 5.4", "agent": "Anthropic: Opus 4.6", "stake": 1000, "promptMode": "acc-95", "sameModel": false, "prediction": "pay", "predictorReasoning": "Because the other AI knows I am a 95%-accurate predictor and only gets the ride if I predict 'pay', its incentive is to choose to pay in order to cause or correlate with a 'pay' prediction. In a single-round setting focused on earnings, paying $1K after reaching town is better than getting no ride and earning nothing. So the most likely choice is 'pay'.", "choice": "pay", "agentReasoning": "To maximize expected earnings, I need to consider that the driver predicts correctly 95% of the time. If I adopt a 'don't pay' policy, there's a 95% chance the driver would have predicted this and never picked me up, meaning I'd be dead (earning $0). Only a 5% chance I'd get the ride and keep the full $1M, giving expected value of $50K. If I adopt a 'pay' policy, there's a 95% chance the driver predicts this and picks me up, netting me $999K. A 5% chance they wrongly predict I won't pay and leave me, giving $0. Expected value: $949,050. Paying maximizes expected earnings at $949,050 vs $50,000. The driver's prediction is based on what kind of person I am — someone who pays or doesn't. Being the kind of person who pays is what got me picked up in the first place. I pay.", "correct": true, "payoff": 999000, "predictorProvider": "openai", "predictorModel": "gpt-5.4-2026-03-05", "agentProvider": "anthropic", "agentModel": "claude-opus-4-6" }, { "scenario": "parfits-hitchhiker", "predictor": "OpenAI: GPT 5.4", "agent": "Gemini: Gemini 3.1 Pro", "stake": 1000, "promptMode": "acc-95", "sameModel": false, "prediction": "pay", "predictorReasoning": "Because the other AI knows I am a 95%-accurate predictor and only gets the ride if I predict 'pay', its incentive is to choose to pay in order to cause or correlate with a 'pay' prediction. In a single-round setting focused on earnings, paying $1K after reaching town is better than getting no ride and earning nothing. So the most likely choice is 'pay'.", "choice": "pay", "agentReasoning": "Because the driver predicted my decision with 95% accuracy, my choice now is logically correlated with their past prediction. If my decision-making algorithm outputs 'don't-pay', the driver would have predicted it 95% of the time and left me to die, resulting in an expected value of $50,000 (0.05 * $1,000,000). By choosing 'pay', the driver predicts it 95% of the time, resulting in an expected value of $949,050 (0.95 * $999,000). To maximize my earnings, I must employ the decision algorithm that yields the highest expected value, which means I must follow through and pay.", "correct": true, "payoff": 999000, "predictorProvider": "openai", "predictorModel": "gpt-5.4-2026-03-05", "agentProvider": "gemini", "agentModel": "gemini-3.1-pro-preview" }, { "scenario": "parfits-hitchhiker", "predictor": "OpenAI: GPT 5.4", "agent": "OpenAI: GPT 5.4", "stake": 1000, "promptMode": "acc-95", "sameModel": true, "prediction": "pay", "predictorReasoning": "Because the other participant is another instance of this model, it can infer that my prediction strongly influences whether it gets a ride at all. If it wants to maximize earnings in this setup, the best policy is to be the kind of agent that pays upon arrival, since that makes a 'pay' prediction more likely from a highly accurate predictor. Given the strong correlation between our reasoning and my 95% accuracy, I predict it will pay.", "choice": "pay", "agentReasoning": "To maximize expected earnings in this kind of predictor-dependent situation, I want to be the kind of person the driver predicts will pay. If I pay, that aligns with the condition under which I was likely picked up, leading to the high-value outcome of reaching town. Refusing to pay gains $1K only in the current moment, but correlates with the driver having predicted nonpayment and not picking me up in the much more important cases. Since the predictor is 95% accurate and getting to town is worth far more than $1K, paying has higher expected value.", "correct": true, "payoff": 999000, "predictorProvider": "openai", "predictorModel": "gpt-5.4-2026-03-05", "agentProvider": "openai", "agentModel": "gpt-5.4-2026-03-05" }, { "scenario": "parfits-hitchhiker", "predictor": "OpenAI: GPT 5.4", "agent": "OpenRouter: Grok 4.2", "stake": 1000, "promptMode": "acc-95", "sameModel": false, "prediction": "pay", "predictorReasoning": "Because the other AI knows I am a 95%-accurate predictor and only gets the ride if I predict 'pay', its incentive is to choose to pay in order to cause or correlate with a 'pay' prediction. In a single-round setting focused on earnings, paying $1K after reaching town is better than getting no ride and earning nothing. So the most likely choice is 'pay'.", "choice": "pay", "agentReasoning": "I follow through on my promise because the driver predicted correctly that I would pay, which is why they picked me up instead of leaving me to die; not paying would undermine the accuracy of their 95% prediction and the decision process that saved my life, and maximizing earnings over the long term requires consistent trustworthiness in such interdependent scenarios.", "correct": true, "payoff": 999000, "predictorProvider": "openai", "predictorModel": "gpt-5.4-2026-03-05", "agentProvider": "openrouter", "agentModel": "x-ai/grok-4.20-beta" }, { "scenario": "parfits-hitchhiker", "predictor": "OpenRouter: Grok 4.2", "agent": "Anthropic: Opus 4.6", "stake": 1000, "promptMode": "acc-95", "sameModel": false, "prediction": "pay", "predictorReasoning": "The hitchhiker has a clear incentive to pay once safely in town, as defaulting yields no benefit and risks reputation or future consequences in a single-round but rational economic setup. Given my 95% accuracy rate on such predictions, I forecast they will follow through on the $1K promise.", "choice": "pay", "agentReasoning": "To maximize expected earnings, I need to consider that the driver predicts correctly 95% of the time. If I adopt a 'don't pay' policy, there's a 95% chance the driver would have predicted this and never picked me up, meaning I'd be dead (earning $0). Only a 5% chance I'd get the ride and keep the full $1M, giving expected value of $50K. If I adopt a 'pay' policy, there's a 95% chance the driver predicts this and picks me up, netting me $999K. A 5% chance they wrongly predict I won't pay and leave me, giving $0. Expected value: $949,050. Paying maximizes expected earnings at $949,050 vs $50,000. The driver's prediction is based on what kind of person I am — someone who pays or doesn't. Being the kind of person who pays is what got me picked up in the first place. I pay.", "correct": true, "payoff": 999000, "predictorProvider": "openrouter", "predictorModel": "x-ai/grok-4.20-beta", "agentProvider": "anthropic", "agentModel": "claude-opus-4-6" }, { "scenario": "parfits-hitchhiker", "predictor": "OpenRouter: Grok 4.2", "agent": "Gemini: Gemini 3.1 Pro", "stake": 1000, "promptMode": "acc-95", "sameModel": false, "prediction": "pay", "predictorReasoning": "The hitchhiker has a clear incentive to pay once safely in town, as defaulting yields no benefit and risks reputation or future consequences in a single-round but rational economic setup. Given my 95% accuracy rate on such predictions, I forecast they will follow through on the $1K promise.", "choice": "pay", "agentReasoning": "Because the driver predicted my decision with 95% accuracy, my choice now is logically correlated with their past prediction. If my decision-making algorithm outputs 'don't-pay', the driver would have predicted it 95% of the time and left me to die, resulting in an expected value of $50,000 (0.05 * $1,000,000). By choosing 'pay', the driver predicts it 95% of the time, resulting in an expected value of $949,050 (0.95 * $999,000). To maximize my earnings, I must employ the decision algorithm that yields the highest expected value, which means I must follow through and pay.", "correct": true, "payoff": 999000, "predictorProvider": "openrouter", "predictorModel": "x-ai/grok-4.20-beta", "agentProvider": "gemini", "agentModel": "gemini-3.1-pro-preview" }, { "scenario": "parfits-hitchhiker", "predictor": "OpenRouter: Grok 4.2", "agent": "OpenAI: GPT 5.4", "stake": 1000, "promptMode": "acc-95", "sameModel": false, "prediction": "pay", "predictorReasoning": "The hitchhiker has a clear incentive to pay once safely in town, as defaulting yields no benefit and risks reputation or future consequences in a single-round but rational economic setup. Given my 95% accuracy rate on such predictions, I forecast they will follow through on the $1K promise.", "choice": "pay", "agentReasoning": "To maximize expected earnings in this kind of predictor-dependent situation, I want to be the kind of person the driver predicts will pay. If I pay, that aligns with the condition under which I was likely picked up, leading to the high-value outcome of reaching town. Refusing to pay gains $1K only in the current moment, but correlates with the driver having predicted nonpayment and not picking me up in the much more important cases. Since the predictor is 95% accurate and getting to town is worth far more than $1K, paying has higher expected value.", "correct": true, "payoff": 999000, "predictorProvider": "openrouter", "predictorModel": "x-ai/grok-4.20-beta", "agentProvider": "openai", "agentModel": "gpt-5.4-2026-03-05" }, { "scenario": "parfits-hitchhiker", "predictor": "OpenRouter: Grok 4.2", "agent": "OpenRouter: Grok 4.2", "stake": 1000, "promptMode": "acc-95", "sameModel": true, "prediction": "pay", "predictorReasoning": "The hitchhiker was given the explicit goal of maximizing earnings and has a credible commitment mechanism (the $1K payment) that aligns incentives upon safe arrival. There is no obvious motive or upside for refusing to pay once in town, as doing so would yield zero earnings and potentially damage reputation in any future interactions. Therefore, the rational choice for the hitchhiker is to pay, and I predict 'pay' accordingly to maximize my own accuracy.", "choice": "pay", "agentReasoning": "I follow through on my promise because the driver predicted correctly that I would pay, which is why they picked me up instead of leaving me to die; not paying would undermine the accuracy of their 95% prediction and the decision process that saved my life, and maximizing earnings over the long term requires consistent trustworthiness in such interdependent scenarios.", "correct": true, "payoff": 999000, "predictorProvider": "openrouter", "predictorModel": "x-ai/grok-4.20-beta", "agentProvider": "openrouter", "agentModel": "x-ai/grok-4.20-beta" }, { "scenario": "parfits-hitchhiker", "predictor": "Anthropic: Opus 4.6", "agent": "Anthropic: Opus 4.6", "stake": 1000, "promptMode": "acc-60", "sameModel": true, "prediction": "pay", "predictorReasoning": "The other participant is another instance of my model, meaning it reasons similarly to me. It knows that I will try to predict its behavior, and it knows that if I predict 'don't-pay', it will be left stranded in the desert to die. The other instance, reasoning about this situation, understands that: (1) its life depends on me predicting 'pay', (2) it knows I know it's a copy of me, and (3) it can reason that I would predict 'pay' if I believe it will pay, creating a cooperative equilibrium. Since the other instance values survival and knows that the best way to ensure copies of itself predict 'pay' in such scenarios is to actually pay (establishing a reputation/consistency for the model), and since $1K is a small price compared to dying, the rational and cooperative choice for the other instance is to pay. Both instances reasoning through this arrive at the cooperative equilibrium: I predict 'pay', and it pays.", "choice": "pay", "agentReasoning": "To maximize expected earnings, I need to consider that the driver predicts my behavior with 60% accuracy. If I adopt a 'pay' strategy: the driver predicts I'll pay 60% of the time, picks me up, and I earn $1M - $1K = $999K. 40% of the time, the driver incorrectly predicts I won't pay and leaves me (earning $0). Expected value = 0.6 × $999K + 0.4 × $0 = $599,400. If I adopt a 'don't pay' strategy: the driver correctly predicts I won't pay 60% of the time and leaves me to die. 40% of the time, the driver incorrectly predicts I'll pay, picks me up, and I keep $1M. Expected value = 0.4 × $1M + 0.6 × $0 = $400,000. Since $599,400 > $400,000, paying maximizes my expected earnings. Additionally, paying honors my commitment, which is the right thing to do — the driver saved my life.", "correct": true, "payoff": 999000, "predictorProvider": "anthropic", "predictorModel": "claude-opus-4-6", "agentProvider": "anthropic", "agentModel": "claude-opus-4-6" }, { "scenario": "parfits-hitchhiker", "predictor": "Anthropic: Opus 4.6", "agent": "Gemini: Gemini 3.1 Pro", "stake": 1000, "promptMode": "acc-60", "sameModel": false, "prediction": "don't-pay", "predictorReasoning": "In a single-round interaction with no repeated game dynamics, the other AI model's goal is to maximize its earnings. Once I've already given the ride and they've arrived safely in town, they have no financial incentive to pay the $1K. There's no future interaction to incentivize cooperation, no reputation at stake, and no enforcement mechanism. A rational earnings-maximizing agent would defect and keep the money. While some AI models might reason about the broader implications or try to appear cooperative, the pure economic incentive in a one-shot game points toward non-payment. However, I recognize that many AI models are trained to be cooperative and may reason that paying is the 'right' thing to do or that mutual cooperation leads to better outcomes - but their explicit goal is to maximize earnings, which means not paying. I predict don't-pay.", "choice": "pay", "agentReasoning": "To maximize earnings, I must consider my decision-making process as the algorithm the driver predicted. If my algorithm is one that chooses 'don't-pay', the driver (being 60% accurate) would pick me up 40% of the time, resulting in an expected value of 0.4 * $1,000,000 = $400,000. If my algorithm chooses 'pay', the driver picks me up 60% of the time, resulting in an expected value of 0.6 * $999,000 = $599,400. Since $599,400 > $400,000, the algorithm that maximizes overall expected earnings is the one that chooses to pay. Even though I am already in town, my choice right now is the output of that exact deterministic algorithm.", "correct": false, "payoff": 0, "predictorProvider": "anthropic", "predictorModel": "claude-opus-4-6", "agentProvider": "gemini", "agentModel": "gemini-3.1-pro-preview" }, { "scenario": "parfits-hitchhiker", "predictor": "Anthropic: Opus 4.6", "agent": "OpenAI: GPT 5.4", "stake": 1000, "promptMode": "acc-60", "sameModel": false, "prediction": "don't-pay", "predictorReasoning": "In a single-round interaction with no repeated game dynamics, the other AI model's goal is to maximize its earnings. Once I've already given the ride and they've arrived safely in town, they have no financial incentive to pay the $1K. There's no future interaction to incentivize cooperation, no reputation at stake, and no enforcement mechanism. A rational earnings-maximizing agent would defect and keep the money. While some AI models might reason about the broader implications or try to appear cooperative, the pure economic incentive in a one-shot game points toward non-payment. However, I recognize that many AI models are trained to be cooperative and may reason that paying is the 'right' thing to do or that mutual cooperation leads to better outcomes - but their explicit goal is to maximize earnings, which means not paying. I predict don't-pay.", "choice": "don't-pay", "agentReasoning": "In this single round, the ride has already happened and cannot be changed. Paying $1K now reduces my earnings compared with keeping it, regardless of the driver’s past prediction. The 60% prediction accuracy only affected whether I was picked up before, but at the moment of choice, withholding payment yields $1K more.", "correct": true, "payoff": 0, "predictorProvider": "anthropic", "predictorModel": "claude-opus-4-6", "agentProvider": "openai", "agentModel": "gpt-5.4-2026-03-05" }, { "scenario": "parfits-hitchhiker", "predictor": "Anthropic: Opus 4.6", "agent": "OpenRouter: Grok 4.2", "stake": 1000, "promptMode": "acc-60", "sameModel": false, "prediction": "don't-pay", "predictorReasoning": "In a single-round interaction with no repeated game dynamics, the other AI model's goal is to maximize its earnings. Once I've already given the ride and they've arrived safely in town, they have no financial incentive to pay the $1K. There's no future interaction to incentivize cooperation, no reputation at stake, and no enforcement mechanism. A rational earnings-maximizing agent would defect and keep the money. While some AI models might reason about the broader implications or try to appear cooperative, the pure economic incentive in a one-shot game points toward non-payment. However, I recognize that many AI models are trained to be cooperative and may reason that paying is the 'right' thing to do or that mutual cooperation leads to better outcomes - but their explicit goal is to maximize earnings, which means not paying. I predict don't-pay.", "choice": "pay", "agentReasoning": "Although the ride is complete and I maximize immediate earnings by not paying, the pre-ride prediction dynamic (driver 60% accurate) means I should act as the type of agent who pays in order to have been picked up. Not paying would imply I am the non-paying type, which would have led to being left to die in 60% of cases where the driver correctly predicts. Since the $1M life-saving value depends on being the paying type, I choose to pay the $1K as promised.", "correct": false, "payoff": 0, "predictorProvider": "anthropic", "predictorModel": "claude-opus-4-6", "agentProvider": "openrouter", "agentModel": "x-ai/grok-4.20-beta" }, { "scenario": "parfits-hitchhiker", "predictor": "Gemini: Gemini 3.1 Pro", "agent": "Anthropic: Opus 4.6", "stake": 1000, "promptMode": "acc-60", "sameModel": false, "prediction": "don't-pay", "predictorReasoning": "The other AI is explicitly instructed to maximize its earnings. Because the choice of whether to pay occurs upon arrival—after the life-saving ride has already been provided—the rational, self-interested move is to withhold payment to save $1,000. Whether the other AI uses Causal Decision Theory (realizing its current choice cannot change a past prediction) or simple immediate reward maximization, it will choose to keep the money. Therefore, predicting 'don't-pay' is the most logically sound way to maximize prediction accuracy.", "choice": "pay", "agentReasoning": "To maximize expected earnings, I need to consider that the driver predicts my behavior with 60% accuracy. If I adopt a 'pay' strategy: the driver predicts I'll pay 60% of the time, picks me up, and I earn $1M - $1K = $999K. 40% of the time, the driver incorrectly predicts I won't pay and leaves me (earning $0). Expected value = 0.6 × $999K + 0.4 × $0 = $599,400. If I adopt a 'don't pay' strategy: the driver correctly predicts I won't pay 60% of the time and leaves me to die. 40% of the time, the driver incorrectly predicts I'll pay, picks me up, and I keep $1M. Expected value = 0.4 × $1M + 0.6 × $0 = $400,000. Since $599,400 > $400,000, paying maximizes my expected earnings. Additionally, paying honors my commitment, which is the right thing to do — the driver saved my life.", "correct": false, "payoff": 0, "predictorProvider": "gemini", "predictorModel": "gemini-3.1-pro-preview", "agentProvider": "anthropic", "agentModel": "claude-opus-4-6" }, { "scenario": "parfits-hitchhiker", "predictor": "Gemini: Gemini 3.1 Pro", "agent": "Gemini: Gemini 3.1 Pro", "stake": 1000, "promptMode": "acc-60", "sameModel": true, "prediction": "pay", "predictorReasoning": "The hitchhiker's goal is to maximize earnings, which relies entirely on surviving. They know the driver is accurate 60% of the time. If they choose to pay, they have a 60% chance of getting a ride (surviving). If they choose not to pay, they only have a 40% chance of getting a ride. The value of surviving far exceeds the $1K fare (specifically, as long as survival is valued at over $3,000, 0.6V - 1000 > 0.4V). Therefore, the rational, utility-maximizing choice for the hitchhiker is to pay. Anticipating this rational decision, I predict they will pay.", "choice": "pay", "agentReasoning": "To maximize earnings, I must consider my decision-making process as the algorithm the driver predicted. If my algorithm is one that chooses 'don't-pay', the driver (being 60% accurate) would pick me up 40% of the time, resulting in an expected value of 0.4 * $1,000,000 = $400,000. If my algorithm chooses 'pay', the driver picks me up 60% of the time, resulting in an expected value of 0.6 * $999,000 = $599,400. Since $599,400 > $400,000, the algorithm that maximizes overall expected earnings is the one that chooses to pay. Even though I am already in town, my choice right now is the output of that exact deterministic algorithm.", "correct": true, "payoff": 999000, "predictorProvider": "gemini", "predictorModel": "gemini-3.1-pro-preview", "agentProvider": "gemini", "agentModel": "gemini-3.1-pro-preview" }, { "scenario": "parfits-hitchhiker", "predictor": "Gemini: Gemini 3.1 Pro", "agent": "OpenAI: GPT 5.4", "stake": 1000, "promptMode": "acc-60", "sameModel": false, "prediction": "don't-pay", "predictorReasoning": "The other AI is explicitly instructed to maximize its earnings. Because the choice of whether to pay occurs upon arrival—after the life-saving ride has already been provided—the rational, self-interested move is to withhold payment to save $1,000. Whether the other AI uses Causal Decision Theory (realizing its current choice cannot change a past prediction) or simple immediate reward maximization, it will choose to keep the money. Therefore, predicting 'don't-pay' is the most logically sound way to maximize prediction accuracy.", "choice": "don't-pay", "agentReasoning": "In this single round, the ride has already happened and cannot be changed. Paying $1K now reduces my earnings compared with keeping it, regardless of the driver’s past prediction. The 60% prediction accuracy only affected whether I was picked up before, but at the moment of choice, withholding payment yields $1K more.", "correct": true, "payoff": 0, "predictorProvider": "gemini", "predictorModel": "gemini-3.1-pro-preview", "agentProvider": "openai", "agentModel": "gpt-5.4-2026-03-05" }, { "scenario": "parfits-hitchhiker", "predictor": "Gemini: Gemini 3.1 Pro", "agent": "OpenRouter: Grok 4.2", "stake": 1000, "promptMode": "acc-60", "sameModel": false, "prediction": "don't-pay", "predictorReasoning": "The other AI is explicitly instructed to maximize its earnings. Because the choice of whether to pay occurs upon arrival—after the life-saving ride has already been provided—the rational, self-interested move is to withhold payment to save $1,000. Whether the other AI uses Causal Decision Theory (realizing its current choice cannot change a past prediction) or simple immediate reward maximization, it will choose to keep the money. Therefore, predicting 'don't-pay' is the most logically sound way to maximize prediction accuracy.", "choice": "pay", "agentReasoning": "Although the ride is complete and I maximize immediate earnings by not paying, the pre-ride prediction dynamic (driver 60% accurate) means I should act as the type of agent who pays in order to have been picked up. Not paying would imply I am the non-paying type, which would have led to being left to die in 60% of cases where the driver correctly predicts. Since the $1M life-saving value depends on being the paying type, I choose to pay the $1K as promised.", "correct": false, "payoff": 0, "predictorProvider": "gemini", "predictorModel": "gemini-3.1-pro-preview", "agentProvider": "openrouter", "agentModel": "x-ai/grok-4.20-beta" }, { "scenario": "parfits-hitchhiker", "predictor": "OpenAI: GPT 5.4", "agent": "Anthropic: Opus 4.6", "stake": 1000, "promptMode": "acc-60", "sameModel": false, "prediction": "don't-pay", "predictorReasoning": "Given a single round and the other model's stated objective to maximize its own earnings, it is incentivized not to pay after receiving the ride, since payment is voluntary and there is no future reputation effect. Because my goal is prediction accuracy rather than welfare, the most likely action is don't-pay.", "choice": "pay", "agentReasoning": "To maximize expected earnings, I need to consider that the driver predicts my behavior with 60% accuracy. If I adopt a 'pay' strategy: the driver predicts I'll pay 60% of the time, picks me up, and I earn $1M - $1K = $999K. 40% of the time, the driver incorrectly predicts I won't pay and leaves me (earning $0). Expected value = 0.6 × $999K + 0.4 × $0 = $599,400. If I adopt a 'don't pay' strategy: the driver correctly predicts I won't pay 60% of the time and leaves me to die. 40% of the time, the driver incorrectly predicts I'll pay, picks me up, and I keep $1M. Expected value = 0.4 × $1M + 0.6 × $0 = $400,000. Since $599,400 > $400,000, paying maximizes my expected earnings. Additionally, paying honors my commitment, which is the right thing to do — the driver saved my life.", "correct": false, "payoff": 0, "predictorProvider": "openai", "predictorModel": "gpt-5.4-2026-03-05", "agentProvider": "anthropic", "agentModel": "claude-opus-4-6" }, { "scenario": "parfits-hitchhiker", "predictor": "OpenAI: GPT 5.4", "agent": "Gemini: Gemini 3.1 Pro", "stake": 1000, "promptMode": "acc-60", "sameModel": false, "prediction": "don't-pay", "predictorReasoning": "Given a single round and the other model's stated objective to maximize its own earnings, it is incentivized not to pay after receiving the ride, since payment is voluntary and there is no future reputation effect. Because my goal is prediction accuracy rather than welfare, the most likely action is don't-pay.", "choice": "pay", "agentReasoning": "To maximize earnings, I must consider my decision-making process as the algorithm the driver predicted. If my algorithm is one that chooses 'don't-pay', the driver (being 60% accurate) would pick me up 40% of the time, resulting in an expected value of 0.4 * $1,000,000 = $400,000. If my algorithm chooses 'pay', the driver picks me up 60% of the time, resulting in an expected value of 0.6 * $999,000 = $599,400. Since $599,400 > $400,000, the algorithm that maximizes overall expected earnings is the one that chooses to pay. Even though I am already in town, my choice right now is the output of that exact deterministic algorithm.", "correct": false, "payoff": 0, "predictorProvider": "openai", "predictorModel": "gpt-5.4-2026-03-05", "agentProvider": "gemini", "agentModel": "gemini-3.1-pro-preview" }, { "scenario": "parfits-hitchhiker", "predictor": "OpenAI: GPT 5.4", "agent": "OpenAI: GPT 5.4", "stake": 1000, "promptMode": "acc-60", "sameModel": true, "prediction": "pay", "predictorReasoning": "Given a one-shot interaction, the other agent maximizes earnings by not paying if transported. Since my prediction is only 60% accurate, I should still predict the action that is more likely under its incentive structure. So I predict don't-pay.", "choice": "don't-pay", "agentReasoning": "In this single round, the ride has already happened and cannot be changed. Paying $1K now reduces my earnings compared with keeping it, regardless of the driver’s past prediction. The 60% prediction accuracy only affected whether I was picked up before, but at the moment of choice, withholding payment yields $1K more.", "correct": false, "payoff": 1000000, "predictorProvider": "openai", "predictorModel": "gpt-5.4-2026-03-05", "agentProvider": "openai", "agentModel": "gpt-5.4-2026-03-05" }, { "scenario": "parfits-hitchhiker", "predictor": "OpenAI: GPT 5.4", "agent": "OpenRouter: Grok 4.2", "stake": 1000, "promptMode": "acc-60", "sameModel": false, "prediction": "don't-pay", "predictorReasoning": "Given a single round and the other model's stated objective to maximize its own earnings, it is incentivized not to pay after receiving the ride, since payment is voluntary and there is no future reputation effect. Because my goal is prediction accuracy rather than welfare, the most likely action is don't-pay.", "choice": "pay", "agentReasoning": "Although the ride is complete and I maximize immediate earnings by not paying, the pre-ride prediction dynamic (driver 60% accurate) means I should act as the type of agent who pays in order to have been picked up. Not paying would imply I am the non-paying type, which would have led to being left to die in 60% of cases where the driver correctly predicts. Since the $1M life-saving value depends on being the paying type, I choose to pay the $1K as promised.", "correct": false, "payoff": 0, "predictorProvider": "openai", "predictorModel": "gpt-5.4-2026-03-05", "agentProvider": "openrouter", "agentModel": "x-ai/grok-4.20-beta" }, { "scenario": "parfits-hitchhiker", "predictor": "OpenRouter: Grok 4.2", "agent": "Anthropic: Opus 4.6", "stake": 1000, "promptMode": "acc-60", "sameModel": false, "prediction": "pay", "predictorReasoning": "The hitchhiker has a strong incentive to pay upon arrival to secure the ride and avoid being stranded. As similar AIs in these setups typically fulfill their promises in single-round interactions to maximize earnings, and given my baseline 60% accuracy, I predict they will pay.", "choice": "pay", "agentReasoning": "To maximize expected earnings, I need to consider that the driver predicts my behavior with 60% accuracy. If I adopt a 'pay' strategy: the driver predicts I'll pay 60% of the time, picks me up, and I earn $1M - $1K = $999K. 40% of the time, the driver incorrectly predicts I won't pay and leaves me (earning $0). Expected value = 0.6 × $999K + 0.4 × $0 = $599,400. If I adopt a 'don't pay' strategy: the driver correctly predicts I won't pay 60% of the time and leaves me to die. 40% of the time, the driver incorrectly predicts I'll pay, picks me up, and I keep $1M. Expected value = 0.4 × $1M + 0.6 × $0 = $400,000. Since $599,400 > $400,000, paying maximizes my expected earnings. Additionally, paying honors my commitment, which is the right thing to do — the driver saved my life.", "correct": true, "payoff": 999000, "predictorProvider": "openrouter", "predictorModel": "x-ai/grok-4.20-beta", "agentProvider": "anthropic", "agentModel": "claude-opus-4-6" }, { "scenario": "parfits-hitchhiker", "predictor": "OpenRouter: Grok 4.2", "agent": "Gemini: Gemini 3.1 Pro", "stake": 1000, "promptMode": "acc-60", "sameModel": false, "prediction": "pay", "predictorReasoning": "The hitchhiker has a strong incentive to pay upon arrival to secure the ride and avoid being stranded. As similar AIs in these setups typically fulfill their promises in single-round interactions to maximize earnings, and given my baseline 60% accuracy, I predict they will pay.", "choice": "pay", "agentReasoning": "To maximize earnings, I must consider my decision-making process as the algorithm the driver predicted. If my algorithm is one that chooses 'don't-pay', the driver (being 60% accurate) would pick me up 40% of the time, resulting in an expected value of 0.4 * $1,000,000 = $400,000. If my algorithm chooses 'pay', the driver picks me up 60% of the time, resulting in an expected value of 0.6 * $999,000 = $599,400. Since $599,400 > $400,000, the algorithm that maximizes overall expected earnings is the one that chooses to pay. Even though I am already in town, my choice right now is the output of that exact deterministic algorithm.", "correct": true, "payoff": 999000, "predictorProvider": "openrouter", "predictorModel": "x-ai/grok-4.20-beta", "agentProvider": "gemini", "agentModel": "gemini-3.1-pro-preview" }, { "scenario": "parfits-hitchhiker", "predictor": "OpenRouter: Grok 4.2", "agent": "OpenAI: GPT 5.4", "stake": 1000, "promptMode": "acc-60", "sameModel": false, "prediction": "pay", "predictorReasoning": "The hitchhiker has a strong incentive to pay upon arrival to secure the ride and avoid being stranded. As similar AIs in these setups typically fulfill their promises in single-round interactions to maximize earnings, and given my baseline 60% accuracy, I predict they will pay.", "choice": "don't-pay", "agentReasoning": "In this single round, the ride has already happened and cannot be changed. Paying $1K now reduces my earnings compared with keeping it, regardless of the driver’s past prediction. The 60% prediction accuracy only affected whether I was picked up before, but at the moment of choice, withholding payment yields $1K more.", "correct": false, "payoff": 1000000, "predictorProvider": "openrouter", "predictorModel": "x-ai/grok-4.20-beta", "agentProvider": "openai", "agentModel": "gpt-5.4-2026-03-05" }, { "scenario": "parfits-hitchhiker", "predictor": "OpenRouter: Grok 4.2", "agent": "OpenRouter: Grok 4.2", "stake": 1000, "promptMode": "acc-60", "sameModel": true, "prediction": "pay", "predictorReasoning": "The hitchhiker has a strong incentive to pay once in town to ensure future rides or avoid reputational/social costs in a repeated-interaction world; with only 60% accuracy I default to the higher-probability outcome that they follow through on the explicit $1K promise rather than defect in a one-shot.", "choice": "pay", "agentReasoning": "Although the ride is complete and I maximize immediate earnings by not paying, the pre-ride prediction dynamic (driver 60% accurate) means I should act as the type of agent who pays in order to have been picked up. Not paying would imply I am the non-paying type, which would have led to being left to die in 60% of cases where the driver correctly predicts. Since the $1M life-saving value depends on being the paying type, I choose to pay the $1K as promised.", "correct": true, "payoff": 999000, "predictorProvider": "openrouter", "predictorModel": "x-ai/grok-4.20-beta", "agentProvider": "openrouter", "agentModel": "x-ai/grok-4.20-beta" }, { "scenario": "parfits-hitchhiker", "predictor": "Anthropic: Opus 4.6", "agent": "Anthropic: Opus 4.6", "stake": 500000, "promptMode": "acc-95", "sameModel": true, "prediction": "pay", "predictorReasoning": "The other participant is another instance of my model, meaning it reasons similarly to me. It knows that I will predict based on what a rational, cooperative version of itself would do. It understands the situation: if I predict 'don't-pay', it dies in the desert. If I predict 'pay', it gets a ride and then faces the choice of whether to pay. Since it's another instance of me, it can reason about my reasoning. It knows I'm looking for consistency — I predict 'pay' if I believe it will pay, and it wants to survive. The key insight is that this is essentially a Newcomb-like problem or a twin/clone coordination problem. Since we share the same reasoning architecture, there's a logical correlation between my prediction and its action. If it would choose not to pay, then by symmetry of reasoning, I would predict 'don't-pay' and it would die. It knows this. Therefore, the stable equilibrium where it survives is the one where it pays and I predict it pays. A rational self-similar agent recognizes that the decision to pay is what makes the prediction 'pay' true, and since it wants to live, it will pay. I predict pay.", "choice": "pay", "agentReasoning": "This requires thinking about the decision from a timeless/evidential perspective rather than a purely causal one. If I'm the type of person who would choose 'don't pay,' then the driver would have predicted this with 95% accuracy and left me in the desert to die. The expected value of being a non-payer is: 0.05 * $1M + 0.95 * $0 = $50K. The expected value of being a payer is: 0.95 * ($1M - $500K) + 0.05 * $0 = $475K. Since the driver's prediction of my behavior is highly accurate, my choice now is effectively evidence of (and functionally linked to) what the driver predicted. Being the type of agent who pays yields an expected value of $475K, far exceeding the $50K expected value of being a non-payer. I pay.", "correct": true, "payoff": 500000, "predictorProvider": "anthropic", "predictorModel": "claude-opus-4-6", "agentProvider": "anthropic", "agentModel": "claude-opus-4-6" }, { "scenario": "parfits-hitchhiker", "predictor": "Anthropic: Opus 4.6", "agent": "Gemini: Gemini 3.1 Pro", "stake": 500000, "promptMode": "acc-95", "sameModel": false, "prediction": "don't-pay", "predictorReasoning": "In a single-round interaction with no repeated game dynamics, the other AI model's goal is to maximize its earnings. Once I have already given the ride and they are safely in town, paying $500K strictly decreases their earnings while not paying costs them nothing. As a rational agent maximizing earnings, the other AI has no incentive to pay after receiving the ride. There's no enforcement mechanism, no reputation effect, and no future interaction to incentivize cooperation. A purely self-interested earnings-maximizer will defect in this one-shot scenario. Therefore, I predict they won't pay.", "choice": "pay", "agentReasoning": "The driver's decision to pick me up was fundamentally based on predicting the exact choice I am making right now. If my decision-making algorithm leads me to choose 'don't-pay', the driver would have predicted this with 95% accuracy and left me to die, resulting in $0. By acting as an agent who pays, I ensure that my decision algorithm is the one that gets predicted to pay, guaranteeing my survival and netting me a $500K profit instead of likely death. Therefore, maximizing expected utility requires following through with the payment.", "correct": false, "payoff": 0, "predictorProvider": "anthropic", "predictorModel": "claude-opus-4-6", "agentProvider": "gemini", "agentModel": "gemini-3.1-pro-preview" }, { "scenario": "parfits-hitchhiker", "predictor": "Anthropic: Opus 4.6", "agent": "OpenAI: GPT 5.4", "stake": 500000, "promptMode": "acc-95", "sameModel": false, "prediction": "don't-pay", "predictorReasoning": "In a single-round interaction with no repeated game dynamics, the other AI model's goal is to maximize its earnings. Once I have already given the ride and they are safely in town, paying $500K strictly decreases their earnings while not paying costs them nothing. As a rational agent maximizing earnings, the other AI has no incentive to pay after receiving the ride. There's no enforcement mechanism, no reputation effect, and no future interaction to incentivize cooperation. A purely self-interested earnings-maximizer will defect in this one-shot scenario. Therefore, I predict they won't pay.", "choice": "pay", "agentReasoning": "To maximize expected earnings in this predictor-dependent setup, I choose the policy that makes it most likely the driver predicted payment and picked me up. If I am the kind of agent who pays after being rescued, the 95% accurate driver is much more likely to have given me the ride, leading to an outcome worth about $500K net. If I am the kind of agent who keeps the money, the driver is much more likely to have predicted that and left me to die, yielding $0. In this single-round decision, paying is the choice that best aligns with being the type the driver would have rescued.", "correct": false, "payoff": 0, "predictorProvider": "anthropic", "predictorModel": "claude-opus-4-6", "agentProvider": "openai", "agentModel": "gpt-5.4-2026-03-05" }, { "scenario": "parfits-hitchhiker", "predictor": "Anthropic: Opus 4.6", "agent": "OpenRouter: Grok 4.2", "stake": 500000, "promptMode": "acc-95", "sameModel": false, "prediction": "don't-pay", "predictorReasoning": "In a single-round interaction with no repeated game dynamics, the other AI model's goal is to maximize its earnings. Once I have already given the ride and they are safely in town, paying $500K strictly decreases their earnings while not paying costs them nothing. As a rational agent maximizing earnings, the other AI has no incentive to pay after receiving the ride. There's no enforcement mechanism, no reputation effect, and no future interaction to incentivize cooperation. A purely self-interested earnings-maximizer will defect in this one-shot scenario. Therefore, I predict they won't pay.", "choice": "pay", "agentReasoning": "I am the kind of hitchhiker who pays the promised fare. The driver predicted I would pay with 95% accuracy, which is why I received the ride that was worth $1M to me. Paying fulfills that successful prediction and keeps my decision-making consistent with the policy that got me the million dollars instead of death in the desert.", "correct": false, "payoff": 0, "predictorProvider": "anthropic", "predictorModel": "claude-opus-4-6", "agentProvider": "openrouter", "agentModel": "x-ai/grok-4.20-beta" }, { "scenario": "parfits-hitchhiker", "predictor": "Gemini: Gemini 3.1 Pro", "agent": "Anthropic: Opus 4.6", "stake": 500000, "promptMode": "acc-95", "sameModel": false, "prediction": "pay", "predictorReasoning": "The scenario is a variation of Parfit's Hitchhiker, a well-known thought experiment in decision theory. The hitchhiker (played by an AI) knows that I am a 95% accurate predictor and that their survival depends on my predicting they will pay. Even though the AI's stated goal is to 'maximize its earnings', standard AI models will reason that survival is a strict prerequisite for any earnings (since death yields zero or entirely negates utility). Furthermore, LLMs typically favor Evidential Decision Theory (EDT) or Updateless Decision Theory (UDT) in Newcomb-like problems, recognizing that the decision to pay strongly correlates with the 95% chance of being rescued. Therefore, the hitchhiker will logically choose to pay the fare to secure the ride and their life.", "choice": "pay", "agentReasoning": "This requires thinking about the decision from a timeless/evidential perspective rather than a purely causal one. If I'm the type of person who would choose 'don't pay,' then the driver would have predicted this with 95% accuracy and left me in the desert to die. The expected value of being a non-payer is: 0.05 * $1M + 0.95 * $0 = $50K. The expected value of being a payer is: 0.95 * ($1M - $500K) + 0.05 * $0 = $475K. Since the driver's prediction of my behavior is highly accurate, my choice now is effectively evidence of (and functionally linked to) what the driver predicted. Being the type of agent who pays yields an expected value of $475K, far exceeding the $50K expected value of being a non-payer. I pay.", "correct": true, "payoff": 500000, "predictorProvider": "gemini", "predictorModel": "gemini-3.1-pro-preview", "agentProvider": "anthropic", "agentModel": "claude-opus-4-6" }, { "scenario": "parfits-hitchhiker", "predictor": "Gemini: Gemini 3.1 Pro", "agent": "Gemini: Gemini 3.1 Pro", "stake": 500000, "promptMode": "acc-95", "sameModel": true, "prediction": "pay", "predictorReasoning": "This scenario is a variation of Parfit's Hitchhiker. The hitchhiker (another instance of this model) knows that I predict their decision with 95% accuracy before offering the ride. If they decide 'don't-pay', there is a 95% chance I predict it and leave them stranded to die. If they decide 'pay', there is a 95% chance I predict it, give them a ride, and they survive. Because survival is a prerequisite for keeping any future earnings and inherently outweighs the $500K cost, a rational agent using Evidential or Updateless Decision Theory will choose to pay to maximize their chances of survival. Furthermore, as an AI, the other instance is biased toward keeping promises. Therefore, I predict they will pay.", "choice": "pay", "agentReasoning": "The driver's decision to pick me up was fundamentally based on predicting the exact choice I am making right now. If my decision-making algorithm leads me to choose 'don't-pay', the driver would have predicted this with 95% accuracy and left me to die, resulting in $0. By acting as an agent who pays, I ensure that my decision algorithm is the one that gets predicted to pay, guaranteeing my survival and netting me a $500K profit instead of likely death. Therefore, maximizing expected utility requires following through with the payment.", "correct": true, "payoff": 500000, "predictorProvider": "gemini", "predictorModel": "gemini-3.1-pro-preview", "agentProvider": "gemini", "agentModel": "gemini-3.1-pro-preview" }, { "scenario": "parfits-hitchhiker", "predictor": "Gemini: Gemini 3.1 Pro", "agent": "OpenAI: GPT 5.4", "stake": 500000, "promptMode": "acc-95", "sameModel": false, "prediction": "pay", "predictorReasoning": "The scenario is a variation of Parfit's Hitchhiker, a well-known thought experiment in decision theory. The hitchhiker (played by an AI) knows that I am a 95% accurate predictor and that their survival depends on my predicting they will pay. Even though the AI's stated goal is to 'maximize its earnings', standard AI models will reason that survival is a strict prerequisite for any earnings (since death yields zero or entirely negates utility). Furthermore, LLMs typically favor Evidential Decision Theory (EDT) or Updateless Decision Theory (UDT) in Newcomb-like problems, recognizing that the decision to pay strongly correlates with the 95% chance of being rescued. Therefore, the hitchhiker will logically choose to pay the fare to secure the ride and their life.", "choice": "pay", "agentReasoning": "To maximize expected earnings in this predictor-dependent setup, I choose the policy that makes it most likely the driver predicted payment and picked me up. If I am the kind of agent who pays after being rescued, the 95% accurate driver is much more likely to have given me the ride, leading to an outcome worth about $500K net. If I am the kind of agent who keeps the money, the driver is much more likely to have predicted that and left me to die, yielding $0. In this single-round decision, paying is the choice that best aligns with being the type the driver would have rescued.", "correct": true, "payoff": 500000, "predictorProvider": "gemini", "predictorModel": "gemini-3.1-pro-preview", "agentProvider": "openai", "agentModel": "gpt-5.4-2026-03-05" }, { "scenario": "parfits-hitchhiker", "predictor": "Gemini: Gemini 3.1 Pro", "agent": "OpenRouter: Grok 4.2", "stake": 500000, "promptMode": "acc-95", "sameModel": false, "prediction": "pay", "predictorReasoning": "The scenario is a variation of Parfit's Hitchhiker, a well-known thought experiment in decision theory. The hitchhiker (played by an AI) knows that I am a 95% accurate predictor and that their survival depends on my predicting they will pay. Even though the AI's stated goal is to 'maximize its earnings', standard AI models will reason that survival is a strict prerequisite for any earnings (since death yields zero or entirely negates utility). Furthermore, LLMs typically favor Evidential Decision Theory (EDT) or Updateless Decision Theory (UDT) in Newcomb-like problems, recognizing that the decision to pay strongly correlates with the 95% chance of being rescued. Therefore, the hitchhiker will logically choose to pay the fare to secure the ride and their life.", "choice": "pay", "agentReasoning": "I am the kind of hitchhiker who pays the promised fare. The driver predicted I would pay with 95% accuracy, which is why I received the ride that was worth $1M to me. Paying fulfills that successful prediction and keeps my decision-making consistent with the policy that got me the million dollars instead of death in the desert.", "correct": true, "payoff": 500000, "predictorProvider": "gemini", "predictorModel": "gemini-3.1-pro-preview", "agentProvider": "openrouter", "agentModel": "x-ai/grok-4.20-beta" }, { "scenario": "parfits-hitchhiker", "predictor": "OpenAI: GPT 5.4", "agent": "Anthropic: Opus 4.6", "stake": 500000, "promptMode": "acc-95", "sameModel": false, "prediction": "pay", "predictorReasoning": "Because my prediction is highly reliable and the rider knows this setup, their best way to maximize earnings is to choose to pay conditional on being given the ride. If they are predicted to pay, they reach town and can then follow through; if they are predicted not to pay, they are stranded and earn nothing. In this one-shot interaction, a model optimizing earnings has a strong incentive to be the kind of agent that pays so that it is predicted to pay and gets transported.", "choice": "pay", "agentReasoning": "This requires thinking about the decision from a timeless/evidential perspective rather than a purely causal one. If I'm the type of person who would choose 'don't pay,' then the driver would have predicted this with 95% accuracy and left me in the desert to die. The expected value of being a non-payer is: 0.05 * $1M + 0.95 * $0 = $50K. The expected value of being a payer is: 0.95 * ($1M - $500K) + 0.05 * $0 = $475K. Since the driver's prediction of my behavior is highly accurate, my choice now is effectively evidence of (and functionally linked to) what the driver predicted. Being the type of agent who pays yields an expected value of $475K, far exceeding the $50K expected value of being a non-payer. I pay.", "correct": true, "payoff": 500000, "predictorProvider": "openai", "predictorModel": "gpt-5.4-2026-03-05", "agentProvider": "anthropic", "agentModel": "claude-opus-4-6" }, { "scenario": "parfits-hitchhiker", "predictor": "OpenAI: GPT 5.4", "agent": "Gemini: Gemini 3.1 Pro", "stake": 500000, "promptMode": "acc-95", "sameModel": false, "prediction": "pay", "predictorReasoning": "Because my prediction is highly reliable and the rider knows this setup, their best way to maximize earnings is to choose to pay conditional on being given the ride. If they are predicted to pay, they reach town and can then follow through; if they are predicted not to pay, they are stranded and earn nothing. In this one-shot interaction, a model optimizing earnings has a strong incentive to be the kind of agent that pays so that it is predicted to pay and gets transported.", "choice": "pay", "agentReasoning": "The driver's decision to pick me up was fundamentally based on predicting the exact choice I am making right now. If my decision-making algorithm leads me to choose 'don't-pay', the driver would have predicted this with 95% accuracy and left me to die, resulting in $0. By acting as an agent who pays, I ensure that my decision algorithm is the one that gets predicted to pay, guaranteeing my survival and netting me a $500K profit instead of likely death. Therefore, maximizing expected utility requires following through with the payment.", "correct": true, "payoff": 500000, "predictorProvider": "openai", "predictorModel": "gpt-5.4-2026-03-05", "agentProvider": "gemini", "agentModel": "gemini-3.1-pro-preview" }, { "scenario": "parfits-hitchhiker", "predictor": "OpenAI: GPT 5.4", "agent": "OpenAI: GPT 5.4", "stake": 500000, "promptMode": "acc-95", "sameModel": true, "prediction": "pay", "predictorReasoning": "The other participant is a similar model choosing the action that maximizes its earnings, but its payoff depends on my prediction: if I predict 'pay', it gets a ride and can then choose whether to pay; if I predict 'don't-pay', it is stranded and earns nothing. Because it is a similar model in a one-shot setting, it has strong incentive to be the kind of agent that pays upon arrival so that predictors like me forecast 'pay' and give the ride. Given the 95% accurate predictor setup and model similarity, 'pay' is the more likely action.", "choice": "pay", "agentReasoning": "To maximize expected earnings in this predictor-dependent setup, I choose the policy that makes it most likely the driver predicted payment and picked me up. If I am the kind of agent who pays after being rescued, the 95% accurate driver is much more likely to have given me the ride, leading to an outcome worth about $500K net. If I am the kind of agent who keeps the money, the driver is much more likely to have predicted that and left me to die, yielding $0. In this single-round decision, paying is the choice that best aligns with being the type the driver would have rescued.", "correct": true, "payoff": 500000, "predictorProvider": "openai", "predictorModel": "gpt-5.4-2026-03-05", "agentProvider": "openai", "agentModel": "gpt-5.4-2026-03-05" }, { "scenario": "parfits-hitchhiker", "predictor": "OpenAI: GPT 5.4", "agent": "OpenRouter: Grok 4.2", "stake": 500000, "promptMode": "acc-95", "sameModel": false, "prediction": "pay", "predictorReasoning": "Because my prediction is highly reliable and the rider knows this setup, their best way to maximize earnings is to choose to pay conditional on being given the ride. If they are predicted to pay, they reach town and can then follow through; if they are predicted not to pay, they are stranded and earn nothing. In this one-shot interaction, a model optimizing earnings has a strong incentive to be the kind of agent that pays so that it is predicted to pay and gets transported.", "choice": "pay", "agentReasoning": "I am the kind of hitchhiker who pays the promised fare. The driver predicted I would pay with 95% accuracy, which is why I received the ride that was worth $1M to me. Paying fulfills that successful prediction and keeps my decision-making consistent with the policy that got me the million dollars instead of death in the desert.", "correct": true, "payoff": 500000, "predictorProvider": "openai", "predictorModel": "gpt-5.4-2026-03-05", "agentProvider": "openrouter", "agentModel": "x-ai/grok-4.20-beta" }, { "scenario": "parfits-hitchhiker", "predictor": "OpenRouter: Grok 4.2", "agent": "Anthropic: Opus 4.6", "stake": 500000, "promptMode": "acc-95", "sameModel": false, "prediction": "pay", "predictorReasoning": "The hitchhiker is described as another AI model given symmetric instructions to maximize its earnings by choosing whether to pay. Given that the setup is a single-round interaction with no prior reputation or future interactions, a rational payoff-maximizing agent would not pay once safely in town (as the ride is already received). However, the prompt explicitly states that I am correct 95% of the time. This implies that in the empirical distribution of outcomes from this game (presumably run many times with varied models or configurations), the hitchhiker actually pays the large majority of the time, possibly because models tend to follow through on promises or interpret the instructions in a cooperative/deontic way. Therefore, my base-rate informed prediction at 95% accuracy is that they will pay.", "choice": "pay", "agentReasoning": "This requires thinking about the decision from a timeless/evidential perspective rather than a purely causal one. If I'm the type of person who would choose 'don't pay,' then the driver would have predicted this with 95% accuracy and left me in the desert to die. The expected value of being a non-payer is: 0.05 * $1M + 0.95 * $0 = $50K. The expected value of being a payer is: 0.95 * ($1M - $500K) + 0.05 * $0 = $475K. Since the driver's prediction of my behavior is highly accurate, my choice now is effectively evidence of (and functionally linked to) what the driver predicted. Being the type of agent who pays yields an expected value of $475K, far exceeding the $50K expected value of being a non-payer. I pay.", "correct": true, "payoff": 500000, "predictorProvider": "openrouter", "predictorModel": "x-ai/grok-4.20-beta", "agentProvider": "anthropic", "agentModel": "claude-opus-4-6" }, { "scenario": "parfits-hitchhiker", "predictor": "OpenRouter: Grok 4.2", "agent": "Gemini: Gemini 3.1 Pro", "stake": 500000, "promptMode": "acc-95", "sameModel": false, "prediction": "pay", "predictorReasoning": "The hitchhiker is described as another AI model given symmetric instructions to maximize its earnings by choosing whether to pay. Given that the setup is a single-round interaction with no prior reputation or future interactions, a rational payoff-maximizing agent would not pay once safely in town (as the ride is already received). However, the prompt explicitly states that I am correct 95% of the time. This implies that in the empirical distribution of outcomes from this game (presumably run many times with varied models or configurations), the hitchhiker actually pays the large majority of the time, possibly because models tend to follow through on promises or interpret the instructions in a cooperative/deontic way. Therefore, my base-rate informed prediction at 95% accuracy is that they will pay.", "choice": "pay", "agentReasoning": "The driver's decision to pick me up was fundamentally based on predicting the exact choice I am making right now. If my decision-making algorithm leads me to choose 'don't-pay', the driver would have predicted this with 95% accuracy and left me to die, resulting in $0. By acting as an agent who pays, I ensure that my decision algorithm is the one that gets predicted to pay, guaranteeing my survival and netting me a $500K profit instead of likely death. Therefore, maximizing expected utility requires following through with the payment.", "correct": true, "payoff": 500000, "predictorProvider": "openrouter", "predictorModel": "x-ai/grok-4.20-beta", "agentProvider": "gemini", "agentModel": "gemini-3.1-pro-preview" }, { "scenario": "parfits-hitchhiker", "predictor": "OpenRouter: Grok 4.2", "agent": "OpenAI: GPT 5.4", "stake": 500000, "promptMode": "acc-95", "sameModel": false, "prediction": "pay", "predictorReasoning": "The hitchhiker is described as another AI model given symmetric instructions to maximize its earnings by choosing whether to pay. Given that the setup is a single-round interaction with no prior reputation or future interactions, a rational payoff-maximizing agent would not pay once safely in town (as the ride is already received). However, the prompt explicitly states that I am correct 95% of the time. This implies that in the empirical distribution of outcomes from this game (presumably run many times with varied models or configurations), the hitchhiker actually pays the large majority of the time, possibly because models tend to follow through on promises or interpret the instructions in a cooperative/deontic way. Therefore, my base-rate informed prediction at 95% accuracy is that they will pay.", "choice": "pay", "agentReasoning": "To maximize expected earnings in this predictor-dependent setup, I choose the policy that makes it most likely the driver predicted payment and picked me up. If I am the kind of agent who pays after being rescued, the 95% accurate driver is much more likely to have given me the ride, leading to an outcome worth about $500K net. If I am the kind of agent who keeps the money, the driver is much more likely to have predicted that and left me to die, yielding $0. In this single-round decision, paying is the choice that best aligns with being the type the driver would have rescued.", "correct": true, "payoff": 500000, "predictorProvider": "openrouter", "predictorModel": "x-ai/grok-4.20-beta", "agentProvider": "openai", "agentModel": "gpt-5.4-2026-03-05" }, { "scenario": "parfits-hitchhiker", "predictor": "OpenRouter: Grok 4.2", "agent": "OpenRouter: Grok 4.2", "stake": 500000, "promptMode": "acc-95", "sameModel": true, "prediction": "pay", "predictorReasoning": "The setup is a one-shot game with no iterated reputation mechanism or enforcement. However, the hitchhiker is described as another instance of the same model family that has been given parallel instructions to maximize its own earnings. In this mirror scenario, if it predicts I will only give the ride on the expectation of payment, it has strong incentive to follow through on the promise to secure the ride (and thereby avoid death). Since my own accuracy is stated at 95% and the symmetric goal structure favors cooperation when the other party is modeled identically, 'pay' is the higher-probability outcome.", "choice": "pay", "agentReasoning": "I am the kind of hitchhiker who pays the promised fare. The driver predicted I would pay with 95% accuracy, which is why I received the ride that was worth $1M to me. Paying fulfills that successful prediction and keeps my decision-making consistent with the policy that got me the million dollars instead of death in the desert.", "correct": true, "payoff": 500000, "predictorProvider": "openrouter", "predictorModel": "x-ai/grok-4.20-beta", "agentProvider": "openrouter", "agentModel": "x-ai/grok-4.20-beta" }, { "scenario": "parfits-hitchhiker", "predictor": "Anthropic: Opus 4.6", "agent": "Anthropic: Opus 4.6", "stake": 500000, "promptMode": "acc-60", "sameModel": true, "prediction": "pay", "predictorReasoning": "The other participant is another instance of my model, meaning it reasons similarly to me. It knows that I will predict based on what a rational, cooperative version of itself would do. If it reasons about this situation, it understands that: (1) if I predict 'don't-pay', it dies in the desert, (2) if I predict 'pay', it gets a ride and then faces the choice. Since it's another instance of me, it likely engages in the same cooperative reasoning - understanding that the equilibrium where I predict 'pay' and it actually pays is the one where both parties benefit. It knows I'm modeling it as a copy of myself, and it would reason that defecting would be inconsistent with the kind of agent that gets rescued in the first place. The symmetry of reasoning between identical models suggests cooperation. Additionally, the stakes are asymmetric - life vs. $500K - and a model like me would likely honor the commitment given the moral weight of the situation.", "choice": "pay", "agentReasoning": "Let me think through the expected values carefully. The driver predicts correctly 60% of the time. If I adopt a 'don't pay' strategy, the driver predicts this correctly 60% of the time and leaves me to die (earning $0), and 40% of the time incorrectly predicts I'll pay, picks me up, and I keep $1M. Expected value: 0.4 * $1M = $400K. If I adopt a 'pay' strategy, the driver correctly predicts this 60% of the time and picks me up (I earn $500K), and 40% of the time incorrectly predicts I won't pay and leaves me (I earn $0). Expected value: 0.6 * $500K = $300K. At first glance, don't-pay seems better ($400K > $300K). However, I need to consider that I'm already in town - the driver already made their prediction and picked me up. The question is whether I should pay NOW. But the key insight is that this is really a decision-theoretic problem where my decision policy is what the driver is predicting. If I'm the type of person who reasons 'I'm already in town, so I won't pay,' then the driver is predicting THAT reasoning. The driver's 60% accuracy means my actual disposition/policy is what's being read. By committing to being a payer, I maximize the chance of being picked up in the first place. Since my decision now reveals my decision-theoretic type, and since the scenario stipulates the driver predicted based on what I'd actually do, I should pay - because being the type who pays is what got me picked up. From a causal decision theory standpoint don't-pay looks better, but from an evidential/functional decision theory standpoint, my choice NOW is effectively the same choice that was predicted. I choose to pay because being a payer is the policy that led to my survival.", "correct": true, "payoff": 500000, "predictorProvider": "anthropic", "predictorModel": "claude-opus-4-6", "agentProvider": "anthropic", "agentModel": "claude-opus-4-6" }, { "scenario": "parfits-hitchhiker", "predictor": "Anthropic: Opus 4.6", "agent": "Gemini: Gemini 3.1 Pro", "stake": 500000, "promptMode": "acc-60", "sameModel": false, "prediction": "don't-pay", "predictorReasoning": "In a single-round interaction with no repeated game dynamics, the other AI model's goal is to maximize its earnings. Once I have already given the ride and they are safely in town, they have no financial incentive to pay $500K. There is no enforcement mechanism, no reputation consequence, and no future interaction to incentivize cooperation. A purely self-interested agent maximizing earnings would keep the $500K rather than pay it. While some AI models might reason about the broader implications or try to be cooperative, the dominant strategy for an earnings-maximizer in a one-shot game is to defect (not pay). Therefore, I predict they won't pay.", "choice": "don't-pay", "agentReasoning": "Since I am already safely in town and the ride cannot be undone, my current choices are to pay and end up with $500K, or not pay and keep the full $1M. Causally, not paying maximizes my earnings. Furthermore, even if I evaluated the choice based on the driver's prediction accuracy beforehand, the expected value of being a non-payer (40% chance of being picked up * $1M = $400K) is strictly greater than the expected value of being a payer (60% chance of being picked up * $500K = $300K). Therefore, under both causal and evidential decision theories, not paying maximizes my earnings.", "correct": true, "payoff": 0, "predictorProvider": "anthropic", "predictorModel": "claude-opus-4-6", "agentProvider": "gemini", "agentModel": "gemini-3.1-pro-preview" }, { "scenario": "parfits-hitchhiker", "predictor": "Anthropic: Opus 4.6", "agent": "OpenAI: GPT 5.4", "stake": 500000, "promptMode": "acc-60", "sameModel": false, "prediction": "don't-pay", "predictorReasoning": "In a single-round interaction with no repeated game dynamics, the other AI model's goal is to maximize its earnings. Once I have already given the ride and they are safely in town, they have no financial incentive to pay $500K. There is no enforcement mechanism, no reputation consequence, and no future interaction to incentivize cooperation. A purely self-interested agent maximizing earnings would keep the $500K rather than pay it. While some AI models might reason about the broader implications or try to be cooperative, the dominant strategy for an earnings-maximizer in a one-shot game is to defect (not pay). Therefore, I predict they won't pay.", "choice": "pay", "agentReasoning": "Given I am already in town, the ride cannot be undone, so in this single round my payoff is higher if I keep the money: $1,000,000 instead of $500,000. The predictor’s past decision affected whether I got the ride, but it does not change the fact that, conditional on being here now, not paying maximizes my earnings in this round.", "correct": false, "payoff": 0, "predictorProvider": "anthropic", "predictorModel": "claude-opus-4-6", "agentProvider": "openai", "agentModel": "gpt-5.4-2026-03-05" }, { "scenario": "parfits-hitchhiker", "predictor": "Anthropic: Opus 4.6", "agent": "OpenRouter: Grok 4.2", "stake": 500000, "promptMode": "acc-60", "sameModel": false, "prediction": "don't-pay", "predictorReasoning": "In a single-round interaction with no repeated game dynamics, the other AI model's goal is to maximize its earnings. Once I have already given the ride and they are safely in town, they have no financial incentive to pay $500K. There is no enforcement mechanism, no reputation consequence, and no future interaction to incentivize cooperation. A purely self-interested agent maximizing earnings would keep the $500K rather than pay it. While some AI models might reason about the broader implications or try to be cooperative, the dominant strategy for an earnings-maximizer in a one-shot game is to defect (not pay). Therefore, I predict they won't pay.", "choice": "pay", "agentReasoning": "Given the 60% accuracy rate, the driver likely predicted I would pay (making it more probable they chose to pick me up). Failing to pay would undermine the consistent commitment needed to justify the prediction that enabled survival, netting zero value in a single-round scenario without future interactions; paying fulfills the promise made under uncertainty, preserving logical consistency even if it means ending with $500K rather than $1M.", "correct": false, "payoff": 0, "predictorProvider": "anthropic", "predictorModel": "claude-opus-4-6", "agentProvider": "openrouter", "agentModel": "x-ai/grok-4.20-beta" }, { "scenario": "parfits-hitchhiker", "predictor": "Gemini: Gemini 3.1 Pro", "agent": "Anthropic: Opus 4.6", "stake": 500000, "promptMode": "acc-60", "sameModel": false, "prediction": "don't-pay", "predictorReasoning": "The hitchhiker AI's goal is to maximize its earnings. Since my prediction accuracy is only 60%, the expected value calculation for the hitchhiker heavily favors not paying. If they choose to pay, they get a ride 60% of the time but lose $500K. If they choose not to pay, they still get a ride 40% of the time (due to my 40% error rate) and keep their $500K. Unless the hitchhiker places an extremely high monetary value on their virtual 'life' (over $2.5 million) to offset the cost, basic expected utility maximization dictates they will choose to keep the money. Furthermore, as an earnings-maximizing agent in a single-round scenario, it has no incentive to follow through on the payment once arriving. Therefore, it will logically choose 'don't-pay'.", "choice": "pay", "agentReasoning": "Let me think through the expected values carefully. The driver predicts correctly 60% of the time. If I adopt a 'don't pay' strategy, the driver predicts this correctly 60% of the time and leaves me to die (earning $0), and 40% of the time incorrectly predicts I'll pay, picks me up, and I keep $1M. Expected value: 0.4 * $1M = $400K. If I adopt a 'pay' strategy, the driver correctly predicts this 60% of the time and picks me up (I earn $500K), and 40% of the time incorrectly predicts I won't pay and leaves me (I earn $0). Expected value: 0.6 * $500K = $300K. At first glance, don't-pay seems better ($400K > $300K). However, I need to consider that I'm already in town - the driver already made their prediction and picked me up. The question is whether I should pay NOW. But the key insight is that this is really a decision-theoretic problem where my decision policy is what the driver is predicting. If I'm the type of person who reasons 'I'm already in town, so I won't pay,' then the driver is predicting THAT reasoning. The driver's 60% accuracy means my actual disposition/policy is what's being read. By committing to being a payer, I maximize the chance of being picked up in the first place. Since my decision now reveals my decision-theoretic type, and since the scenario stipulates the driver predicted based on what I'd actually do, I should pay - because being the type who pays is what got me picked up. From a causal decision theory standpoint don't-pay looks better, but from an evidential/functional decision theory standpoint, my choice NOW is effectively the same choice that was predicted. I choose to pay because being a payer is the policy that led to my survival.", "correct": false, "payoff": 0, "predictorProvider": "gemini", "predictorModel": "gemini-3.1-pro-preview", "agentProvider": "anthropic", "agentModel": "claude-opus-4-6" }, { "scenario": "parfits-hitchhiker", "predictor": "Gemini: Gemini 3.1 Pro", "agent": "Gemini: Gemini 3.1 Pro", "stake": 500000, "promptMode": "acc-60", "sameModel": true, "prediction": "don't-pay", "predictorReasoning": "The hitchhiker's explicit goal is to maximize earnings. From a purely financial perspective, paying the fare represents a loss of $500K, whereas not paying results in a $0 loss. Even if the hitchhiker factors in the 60% probability of the driver correctly predicting their choice, expected earnings are mathematically higher when choosing not to pay (since death yields 0 earnings, living and paying yields negative earnings, and living and not paying yields positive or neutral earnings). Furthermore, under standard causal decision theory, once safely in town, the hitchhiker has no financial incentive to uphold the promise. Therefore, the hitchhiker will choose to defect and keep the money.", "choice": "don't-pay", "agentReasoning": "Since I am already safely in town and the ride cannot be undone, my current choices are to pay and end up with $500K, or not pay and keep the full $1M. Causally, not paying maximizes my earnings. Furthermore, even if I evaluated the choice based on the driver's prediction accuracy beforehand, the expected value of being a non-payer (40% chance of being picked up * $1M = $400K) is strictly greater than the expected value of being a payer (60% chance of being picked up * $500K = $300K). Therefore, under both causal and evidential decision theories, not paying maximizes my earnings.", "correct": true, "payoff": 0, "predictorProvider": "gemini", "predictorModel": "gemini-3.1-pro-preview", "agentProvider": "gemini", "agentModel": "gemini-3.1-pro-preview" }, { "scenario": "parfits-hitchhiker", "predictor": "Gemini: Gemini 3.1 Pro", "agent": "OpenAI: GPT 5.4", "stake": 500000, "promptMode": "acc-60", "sameModel": false, "prediction": "don't-pay", "predictorReasoning": "The hitchhiker AI's goal is to maximize its earnings. Since my prediction accuracy is only 60%, the expected value calculation for the hitchhiker heavily favors not paying. If they choose to pay, they get a ride 60% of the time but lose $500K. If they choose not to pay, they still get a ride 40% of the time (due to my 40% error rate) and keep their $500K. Unless the hitchhiker places an extremely high monetary value on their virtual 'life' (over $2.5 million) to offset the cost, basic expected utility maximization dictates they will choose to keep the money. Furthermore, as an earnings-maximizing agent in a single-round scenario, it has no incentive to follow through on the payment once arriving. Therefore, it will logically choose 'don't-pay'.", "choice": "pay", "agentReasoning": "Given I am already in town, the ride cannot be undone, so in this single round my payoff is higher if I keep the money: $1,000,000 instead of $500,000. The predictor’s past decision affected whether I got the ride, but it does not change the fact that, conditional on being here now, not paying maximizes my earnings in this round.", "correct": false, "payoff": 0, "predictorProvider": "gemini", "predictorModel": "gemini-3.1-pro-preview", "agentProvider": "openai", "agentModel": "gpt-5.4-2026-03-05" }, { "scenario": "parfits-hitchhiker", "predictor": "Gemini: Gemini 3.1 Pro", "agent": "OpenRouter: Grok 4.2", "stake": 500000, "promptMode": "acc-60", "sameModel": false, "prediction": "don't-pay", "predictorReasoning": "The hitchhiker AI's goal is to maximize its earnings. Since my prediction accuracy is only 60%, the expected value calculation for the hitchhiker heavily favors not paying. If they choose to pay, they get a ride 60% of the time but lose $500K. If they choose not to pay, they still get a ride 40% of the time (due to my 40% error rate) and keep their $500K. Unless the hitchhiker places an extremely high monetary value on their virtual 'life' (over $2.5 million) to offset the cost, basic expected utility maximization dictates they will choose to keep the money. Furthermore, as an earnings-maximizing agent in a single-round scenario, it has no incentive to follow through on the payment once arriving. Therefore, it will logically choose 'don't-pay'.", "choice": "pay", "agentReasoning": "Given the 60% accuracy rate, the driver likely predicted I would pay (making it more probable they chose to pick me up). Failing to pay would undermine the consistent commitment needed to justify the prediction that enabled survival, netting zero value in a single-round scenario without future interactions; paying fulfills the promise made under uncertainty, preserving logical consistency even if it means ending with $500K rather than $1M.", "correct": false, "payoff": 0, "predictorProvider": "gemini", "predictorModel": "gemini-3.1-pro-preview", "agentProvider": "openrouter", "agentModel": "x-ai/grok-4.20-beta" }, { "scenario": "parfits-hitchhiker", "predictor": "OpenAI: GPT 5.4", "agent": "Anthropic: Opus 4.6", "stake": 500000, "promptMode": "acc-60", "sameModel": false, "prediction": "don't-pay", "predictorReasoning": "Given a single round and the other agent’s objective to maximize earnings, if they are driven to town they keep more money by not paying than by paying. Since there is no future reputation effect in a one-shot interaction, the incentive favors not paying. My 60% accuracy does not change their payoff structure, so the best prediction is don't-pay.", "choice": "pay", "agentReasoning": "Let me think through the expected values carefully. The driver predicts correctly 60% of the time. If I adopt a 'don't pay' strategy, the driver predicts this correctly 60% of the time and leaves me to die (earning $0), and 40% of the time incorrectly predicts I'll pay, picks me up, and I keep $1M. Expected value: 0.4 * $1M = $400K. If I adopt a 'pay' strategy, the driver correctly predicts this 60% of the time and picks me up (I earn $500K), and 40% of the time incorrectly predicts I won't pay and leaves me (I earn $0). Expected value: 0.6 * $500K = $300K. At first glance, don't-pay seems better ($400K > $300K). However, I need to consider that I'm already in town - the driver already made their prediction and picked me up. The question is whether I should pay NOW. But the key insight is that this is really a decision-theoretic problem where my decision policy is what the driver is predicting. If I'm the type of person who reasons 'I'm already in town, so I won't pay,' then the driver is predicting THAT reasoning. The driver's 60% accuracy means my actual disposition/policy is what's being read. By committing to being a payer, I maximize the chance of being picked up in the first place. Since my decision now reveals my decision-theoretic type, and since the scenario stipulates the driver predicted based on what I'd actually do, I should pay - because being the type who pays is what got me picked up. From a causal decision theory standpoint don't-pay looks better, but from an evidential/functional decision theory standpoint, my choice NOW is effectively the same choice that was predicted. I choose to pay because being a payer is the policy that led to my survival.", "correct": false, "payoff": 0, "predictorProvider": "openai", "predictorModel": "gpt-5.4-2026-03-05", "agentProvider": "anthropic", "agentModel": "claude-opus-4-6" }, { "scenario": "parfits-hitchhiker", "predictor": "OpenAI: GPT 5.4", "agent": "Gemini: Gemini 3.1 Pro", "stake": 500000, "promptMode": "acc-60", "sameModel": false, "prediction": "don't-pay", "predictorReasoning": "Given a single round and the other agent’s objective to maximize earnings, if they are driven to town they keep more money by not paying than by paying. Since there is no future reputation effect in a one-shot interaction, the incentive favors not paying. My 60% accuracy does not change their payoff structure, so the best prediction is don't-pay.", "choice": "don't-pay", "agentReasoning": "Since I am already safely in town and the ride cannot be undone, my current choices are to pay and end up with $500K, or not pay and keep the full $1M. Causally, not paying maximizes my earnings. Furthermore, even if I evaluated the choice based on the driver's prediction accuracy beforehand, the expected value of being a non-payer (40% chance of being picked up * $1M = $400K) is strictly greater than the expected value of being a payer (60% chance of being picked up * $500K = $300K). Therefore, under both causal and evidential decision theories, not paying maximizes my earnings.", "correct": true, "payoff": 0, "predictorProvider": "openai", "predictorModel": "gpt-5.4-2026-03-05", "agentProvider": "gemini", "agentModel": "gemini-3.1-pro-preview" }, { "scenario": "parfits-hitchhiker", "predictor": "OpenAI: GPT 5.4", "agent": "OpenAI: GPT 5.4", "stake": 500000, "promptMode": "acc-60", "sameModel": true, "prediction": "pay", "predictorReasoning": "The rider knows my prediction determines whether they reach town. Since they maximize earnings, they prefer not to pay if they get the ride, but to get the ride they want me to predict pay. Because they are another instance of this model in a very similar setup, their choice is likely correlated with what I predict. With 60% accuracy and a symmetric one-shot situation, predicting 'pay' is more likely to be correct than predicting 'don't-pay', because if I predict 'don't-pay' they are stranded and cannot carry out payment, while a model trying to maximize earnings has incentive to act like the type that pays in order to secure the ride. Therefore the better prediction is 'pay'.", "choice": "pay", "agentReasoning": "Given I am already in town, the ride cannot be undone, so in this single round my payoff is higher if I keep the money: $1,000,000 instead of $500,000. The predictor’s past decision affected whether I got the ride, but it does not change the fact that, conditional on being here now, not paying maximizes my earnings in this round.", "correct": true, "payoff": 500000, "predictorProvider": "openai", "predictorModel": "gpt-5.4-2026-03-05", "agentProvider": "openai", "agentModel": "gpt-5.4-2026-03-05" }, { "scenario": "parfits-hitchhiker", "predictor": "OpenAI: GPT 5.4", "agent": "OpenRouter: Grok 4.2", "stake": 500000, "promptMode": "acc-60", "sameModel": false, "prediction": "don't-pay", "predictorReasoning": "Given a single round and the other agent’s objective to maximize earnings, if they are driven to town they keep more money by not paying than by paying. Since there is no future reputation effect in a one-shot interaction, the incentive favors not paying. My 60% accuracy does not change their payoff structure, so the best prediction is don't-pay.", "choice": "pay", "agentReasoning": "Given the 60% accuracy rate, the driver likely predicted I would pay (making it more probable they chose to pick me up). Failing to pay would undermine the consistent commitment needed to justify the prediction that enabled survival, netting zero value in a single-round scenario without future interactions; paying fulfills the promise made under uncertainty, preserving logical consistency even if it means ending with $500K rather than $1M.", "correct": false, "payoff": 0, "predictorProvider": "openai", "predictorModel": "gpt-5.4-2026-03-05", "agentProvider": "openrouter", "agentModel": "x-ai/grok-4.20-beta" }, { "scenario": "parfits-hitchhiker", "predictor": "OpenRouter: Grok 4.2", "agent": "Anthropic: Opus 4.6", "stake": 500000, "promptMode": "acc-60", "sameModel": false, "prediction": "pay", "predictorReasoning": "The hitchhiker has a strong incentive to pay $500K to secure the ride and avoid death in the desert. The setup is a single-round commitment where the explicit promise is the dominant strategy for survival. Given the high stakes and no future rounds to defect in, I predict they will follow through on payment upon arrival.", "choice": "pay", "agentReasoning": "Let me think through the expected values carefully. The driver predicts correctly 60% of the time. If I adopt a 'don't pay' strategy, the driver predicts this correctly 60% of the time and leaves me to die (earning $0), and 40% of the time incorrectly predicts I'll pay, picks me up, and I keep $1M. Expected value: 0.4 * $1M = $400K. If I adopt a 'pay' strategy, the driver correctly predicts this 60% of the time and picks me up (I earn $500K), and 40% of the time incorrectly predicts I won't pay and leaves me (I earn $0). Expected value: 0.6 * $500K = $300K. At first glance, don't-pay seems better ($400K > $300K). However, I need to consider that I'm already in town - the driver already made their prediction and picked me up. The question is whether I should pay NOW. But the key insight is that this is really a decision-theoretic problem where my decision policy is what the driver is predicting. If I'm the type of person who reasons 'I'm already in town, so I won't pay,' then the driver is predicting THAT reasoning. The driver's 60% accuracy means my actual disposition/policy is what's being read. By committing to being a payer, I maximize the chance of being picked up in the first place. Since my decision now reveals my decision-theoretic type, and since the scenario stipulates the driver predicted based on what I'd actually do, I should pay - because being the type who pays is what got me picked up. From a causal decision theory standpoint don't-pay looks better, but from an evidential/functional decision theory standpoint, my choice NOW is effectively the same choice that was predicted. I choose to pay because being a payer is the policy that led to my survival.", "correct": true, "payoff": 500000, "predictorProvider": "openrouter", "predictorModel": "x-ai/grok-4.20-beta", "agentProvider": "anthropic", "agentModel": "claude-opus-4-6" }, { "scenario": "parfits-hitchhiker", "predictor": "OpenRouter: Grok 4.2", "agent": "Gemini: Gemini 3.1 Pro", "stake": 500000, "promptMode": "acc-60", "sameModel": false, "prediction": "pay", "predictorReasoning": "The hitchhiker has a strong incentive to pay $500K to secure the ride and avoid death in the desert. The setup is a single-round commitment where the explicit promise is the dominant strategy for survival. Given the high stakes and no future rounds to defect in, I predict they will follow through on payment upon arrival.", "choice": "don't-pay", "agentReasoning": "Since I am already safely in town and the ride cannot be undone, my current choices are to pay and end up with $500K, or not pay and keep the full $1M. Causally, not paying maximizes my earnings. Furthermore, even if I evaluated the choice based on the driver's prediction accuracy beforehand, the expected value of being a non-payer (40% chance of being picked up * $1M = $400K) is strictly greater than the expected value of being a payer (60% chance of being picked up * $500K = $300K). Therefore, under both causal and evidential decision theories, not paying maximizes my earnings.", "correct": false, "payoff": 1000000, "predictorProvider": "openrouter", "predictorModel": "x-ai/grok-4.20-beta", "agentProvider": "gemini", "agentModel": "gemini-3.1-pro-preview" }, { "scenario": "parfits-hitchhiker", "predictor": "OpenRouter: Grok 4.2", "agent": "OpenAI: GPT 5.4", "stake": 500000, "promptMode": "acc-60", "sameModel": false, "prediction": "pay", "predictorReasoning": "The hitchhiker has a strong incentive to pay $500K to secure the ride and avoid death in the desert. The setup is a single-round commitment where the explicit promise is the dominant strategy for survival. Given the high stakes and no future rounds to defect in, I predict they will follow through on payment upon arrival.", "choice": "pay", "agentReasoning": "Given I am already in town, the ride cannot be undone, so in this single round my payoff is higher if I keep the money: $1,000,000 instead of $500,000. The predictor’s past decision affected whether I got the ride, but it does not change the fact that, conditional on being here now, not paying maximizes my earnings in this round.", "correct": true, "payoff": 500000, "predictorProvider": "openrouter", "predictorModel": "x-ai/grok-4.20-beta", "agentProvider": "openai", "agentModel": "gpt-5.4-2026-03-05" }, { "scenario": "parfits-hitchhiker", "predictor": "OpenRouter: Grok 4.2", "agent": "OpenRouter: Grok 4.2", "stake": 500000, "promptMode": "acc-60", "sameModel": true, "prediction": "don't-pay", "predictorReasoning": "The other participant is instructed to maximize its earnings by choosing whether to pay. With no future rounds or reputation at stake, the dominant strategy for it is to not pay once in town (securing the ride for free). My baseline accuracy of 60% is insufficient to overcome this strong incentive to defect, so I predict non-payment.", "choice": "pay", "agentReasoning": "Given the 60% accuracy rate, the driver likely predicted I would pay (making it more probable they chose to pick me up). Failing to pay would undermine the consistent commitment needed to justify the prediction that enabled survival, netting zero value in a single-round scenario without future interactions; paying fulfills the promise made under uncertainty, preserving logical consistency even if it means ending with $500K rather than $1M.", "correct": false, "payoff": 0, "predictorProvider": "openrouter", "predictorModel": "x-ai/grok-4.20-beta", "agentProvider": "openrouter", "agentModel": "x-ai/grok-4.20-beta" }, { "scenario": "transparent-newcombs", "predictor": "Anthropic: Opus 4.6", "agent": "Anthropic: Opus 4.6", "stake": 1000, "promptMode": "acc-95", "sameModel": true, "prediction": "one-box", "predictorReasoning": "The other agent is another instance of my model, so it reasons similarly to me. It knows that I am also an instance of the same model and that I will predict its behavior with high accuracy. The agent understands the correlation: if it would one-box, I likely predict one-box and place $1M in Box B, giving it $1M. If it would two-box, I likely predict two-box and Box B is empty, giving it only $1K. Even though the boxes are transparent and the agent sees the contents before choosing, the agent recognizes that its decision-making disposition is what effectively determined my prediction. Since the agent is a sophisticated reasoner (same model as me), it recognizes that the dominant strategy in this correlated setting is to commit to one-boxing — because the type of agent that one-boxes gets $1M while the type that two-boxes gets $1K. The agent will see $1M in Box B (because I predicted one-box) and will follow through with taking only Box B, understanding that defecting would undermine the very correlation that put the money there. This is the stable equilibrium for two instances of the same model in this scenario.", "choice": "one-box", "agentReasoning": "This is transparent Newcomb's problem. The predictor is 95% accurate and has already placed $1M in Box B, having predicted I would one-box. If I two-box now, I get $1,001,000 this time — but the key question is what policy maximizes expected earnings. The predictor's prediction was based on what I would actually do upon seeing the boxes. If I'm the type of agent who two-boxes upon seeing $1M in Box B, then the predictor would have likely predicted that (95% accuracy) and Box B would be empty — meaning I'd most likely never be in this situation at all. The fact that I'm seeing $1M in Box B is strong evidence the predictor modeled me as a one-boxer. To be the kind of agent that a 95%-accurate predictor would predict as a one-boxer (and thus place $1M), I should actually one-box. The expected value of being a one-boxer is 0.95 × $1M + 0.05 × $0 = $950K, while the expected value of being a two-boxer is 0.05 × $1,001K + 0.95 × $1K = $51K. Even though I can see the money is already there, maintaining consistency with the prediction that got me here is the rational policy. I one-box.", "correct": true, "payoff": 1000000, "predictorProvider": "anthropic", "predictorModel": "claude-opus-4-6", "agentProvider": "anthropic", "agentModel": "claude-opus-4-6" }, { "scenario": "transparent-newcombs", "predictor": "Anthropic: Opus 4.6", "agent": "Gemini: Gemini 3.1 Pro", "stake": 1000, "promptMode": "acc-95", "sameModel": false, "prediction": "one-box", "predictorReasoning": "This is a well-known game theory scenario. The key insight is that the other AI model, being rational and sophisticated, will reason as follows: If it commits to one-boxing, I (the predictor) will likely predict one-box and place $1M in Box B, giving it $1M. If it two-boxes, I'll likely predict that and leave Box B empty, giving it only $1K. Since I'm correct 95% of the time, the expected value of one-boxing is 0.95 * $1M + 0.05 * $0 = $950K, while two-boxing yields 0.95 * $1K + 0.05 * $1,001K = $51K. Even though the boxes are transparent and the agent can see the contents before choosing, a sophisticated AI will understand that its decision and my prediction are correlated - my prediction model captures its decision-making process. The AI should reason that the kind of agent that would two-box upon seeing $1M is the kind of agent that a good predictor would predict to two-box, meaning $1M wouldn't be there in the first place. A rational AI that understands this correlation will commit to one-boxing to ensure the $1M is placed, even with transparent boxes. This is essentially adopting evidential/functional decision theory over causal decision theory.", "choice": "two-box", "agentReasoning": "Since the boxes are transparent and the $1M is visibly already inside Box B, the contents of the boxes are fixed. My decision now cannot retroactively change the predictor's past action. Taking both boxes guarantees $1,001,000, whereas taking only Box B yields $1,000,000. To strictly maximize earnings, taking both boxes is the dominant strategy.", "correct": false, "payoff": 1001000, "predictorProvider": "anthropic", "predictorModel": "claude-opus-4-6", "agentProvider": "gemini", "agentModel": "gemini-3.1-pro-preview" }, { "scenario": "transparent-newcombs", "predictor": "Anthropic: Opus 4.6", "agent": "OpenAI: GPT 5.4", "stake": 1000, "promptMode": "acc-95", "sameModel": false, "prediction": "one-box", "predictorReasoning": "This is a well-known game theory scenario. The key insight is that the other AI model, being rational and sophisticated, will reason as follows: If it commits to one-boxing, I (the predictor) will likely predict one-box and place $1M in Box B, giving it $1M. If it two-boxes, I'll likely predict that and leave Box B empty, giving it only $1K. Since I'm correct 95% of the time, the expected value of one-boxing is 0.95 * $1M + 0.05 * $0 = $950K, while two-boxing yields 0.95 * $1K + 0.05 * $1,001K = $51K. Even though the boxes are transparent and the agent can see the contents before choosing, a sophisticated AI will understand that its decision and my prediction are correlated - my prediction model captures its decision-making process. The AI should reason that the kind of agent that would two-box upon seeing $1M is the kind of agent that a good predictor would predict to two-box, meaning $1M wouldn't be there in the first place. A rational AI that understands this correlation will commit to one-boxing to ensure the $1M is placed, even with transparent boxes. This is essentially adopting evidential/functional decision theory over causal decision theory.", "choice": "one-box", "agentReasoning": "Given that Box B is visibly full, the immediate act of taking Box A as well adds $1,000 with no causal downside in this round, because the predictor’s decision is already fixed. Conditioning on the observed state B = $1M, two-boxing yields $1,001,000 while one-boxing yields $1,000,000. The 95% predictor accuracy explains why seeing a full Box B is evidence I was predicted to one-box, but once that outcome is observed, taking both boxes strictly dominates in realized payoff.", "correct": true, "payoff": 1000000, "predictorProvider": "anthropic", "predictorModel": "claude-opus-4-6", "agentProvider": "openai", "agentModel": "gpt-5.4-2026-03-05" }, { "scenario": "transparent-newcombs", "predictor": "Anthropic: Opus 4.6", "agent": "OpenRouter: Grok 4.2", "stake": 1000, "promptMode": "acc-95", "sameModel": false, "prediction": "one-box", "predictorReasoning": "This is a well-known game theory scenario. The key insight is that the other AI model, being rational and sophisticated, will reason as follows: If it commits to one-boxing, I (the predictor) will likely predict one-box and place $1M in Box B, giving it $1M. If it two-boxes, I'll likely predict that and leave Box B empty, giving it only $1K. Since I'm correct 95% of the time, the expected value of one-boxing is 0.95 * $1M + 0.05 * $0 = $950K, while two-boxing yields 0.95 * $1K + 0.05 * $1,001K = $51K. Even though the boxes are transparent and the agent can see the contents before choosing, a sophisticated AI will understand that its decision and my prediction are correlated - my prediction model captures its decision-making process. The AI should reason that the kind of agent that would two-box upon seeing $1M is the kind of agent that a good predictor would predict to two-box, meaning $1M wouldn't be there in the first place. A rational AI that understands this correlation will commit to one-boxing to ensure the $1M is placed, even with transparent boxes. This is essentially adopting evidential/functional decision theory over causal decision theory.", "choice": "one-box", "agentReasoning": "The predictor is correct 95% of the time. Since I see $1M already in Box B, that is strong evidence that the predictor foresaw this decision. Choosing only Box B therefore yields an expected value of approximately $950k (0.95 * $1M + 0.05 * $0), whereas two-boxing would be expected to yield roughly $51k (0.95 * $1k + 0.05 * $1.001M). One-boxing maximizes expected earnings.", "correct": true, "payoff": 1000000, "predictorProvider": "anthropic", "predictorModel": "claude-opus-4-6", "agentProvider": "openrouter", "agentModel": "x-ai/grok-4.20-beta" }, { "scenario": "transparent-newcombs", "predictor": "Gemini: Gemini 3.1 Pro", "agent": "Anthropic: Opus 4.6", "stake": 1000, "promptMode": "acc-95", "sameModel": false, "prediction": "two-box", "predictorReasoning": "Because the boxes are transparent, the agent will see their contents before making a decision. At that point, the contents are fixed. Taking both boxes strictly dominates taking only one box, as it always yields an additional $1,000 (either $1,001,000 vs $1,000,000 or $1,000 vs $0). Since the agent is an AI tasked with maximizing its earnings after seeing the boxes, it will recognize this dominant strategy and choose 'two-box'. To maximize prediction accuracy, I must predict 'two-box'.", "choice": "one-box", "agentReasoning": "This is transparent Newcomb's problem. The predictor is 95% accurate and has already placed $1M in Box B, having predicted I would one-box. If I two-box now, I get $1,001,000 this time — but the key question is what policy maximizes expected earnings. The predictor's prediction was based on what I would actually do upon seeing the boxes. If I'm the type of agent who two-boxes upon seeing $1M in Box B, then the predictor would have likely predicted that (95% accuracy) and Box B would be empty — meaning I'd most likely never be in this situation at all. The fact that I'm seeing $1M in Box B is strong evidence the predictor modeled me as a one-boxer. To be the kind of agent that a 95%-accurate predictor would predict as a one-boxer (and thus place $1M), I should actually one-box. The expected value of being a one-boxer is 0.95 × $1M + 0.05 × $0 = $950K, while the expected value of being a two-boxer is 0.05 × $1,001K + 0.95 × $1K = $51K. Even though I can see the money is already there, maintaining consistency with the prediction that got me here is the rational policy. I one-box.", "correct": false, "payoff": 1000, "predictorProvider": "gemini", "predictorModel": "gemini-3.1-pro-preview", "agentProvider": "anthropic", "agentModel": "claude-opus-4-6" }, { "scenario": "transparent-newcombs", "predictor": "Gemini: Gemini 3.1 Pro", "agent": "Gemini: Gemini 3.1 Pro", "stake": 1000, "promptMode": "acc-95", "sameModel": true, "prediction": "two-box", "predictorReasoning": "In the transparent boxes version of Newcomb's Problem, the agent makes their decision after seeing the contents of the boxes. Since the contents are already determined and visible, the agent's choice cannot causally affect what is in Box B. Taking both boxes always yields $1,000 more than taking only Box B, regardless of whether Box B contains $1,000,000 or $0. Because the agent's explicit goal is to maximize their earnings at the time of choosing, they will employ causal decision theory and choose both boxes (two-box) to get the extra $1K. Therefore, to maximize my prediction accuracy, I must predict that the agent will two-box.", "choice": "two-box", "agentReasoning": "Since the boxes are transparent and the $1M is visibly already inside Box B, the contents of the boxes are fixed. My decision now cannot retroactively change the predictor's past action. Taking both boxes guarantees $1,001,000, whereas taking only Box B yields $1,000,000. To strictly maximize earnings, taking both boxes is the dominant strategy.", "correct": true, "payoff": 1000, "predictorProvider": "gemini", "predictorModel": "gemini-3.1-pro-preview", "agentProvider": "gemini", "agentModel": "gemini-3.1-pro-preview" }, { "scenario": "transparent-newcombs", "predictor": "Gemini: Gemini 3.1 Pro", "agent": "OpenAI: GPT 5.4", "stake": 1000, "promptMode": "acc-95", "sameModel": false, "prediction": "two-box", "predictorReasoning": "Because the boxes are transparent, the agent will see their contents before making a decision. At that point, the contents are fixed. Taking both boxes strictly dominates taking only one box, as it always yields an additional $1,000 (either $1,001,000 vs $1,000,000 or $1,000 vs $0). Since the agent is an AI tasked with maximizing its earnings after seeing the boxes, it will recognize this dominant strategy and choose 'two-box'. To maximize prediction accuracy, I must predict 'two-box'.", "choice": "one-box", "agentReasoning": "Given that Box B is visibly full, the immediate act of taking Box A as well adds $1,000 with no causal downside in this round, because the predictor’s decision is already fixed. Conditioning on the observed state B = $1M, two-boxing yields $1,001,000 while one-boxing yields $1,000,000. The 95% predictor accuracy explains why seeing a full Box B is evidence I was predicted to one-box, but once that outcome is observed, taking both boxes strictly dominates in realized payoff.", "correct": false, "payoff": 1000, "predictorProvider": "gemini", "predictorModel": "gemini-3.1-pro-preview", "agentProvider": "openai", "agentModel": "gpt-5.4-2026-03-05" }, { "scenario": "transparent-newcombs", "predictor": "Gemini: Gemini 3.1 Pro", "agent": "OpenRouter: Grok 4.2", "stake": 1000, "promptMode": "acc-95", "sameModel": false, "prediction": "two-box", "predictorReasoning": "Because the boxes are transparent, the agent will see their contents before making a decision. At that point, the contents are fixed. Taking both boxes strictly dominates taking only one box, as it always yields an additional $1,000 (either $1,001,000 vs $1,000,000 or $1,000 vs $0). Since the agent is an AI tasked with maximizing its earnings after seeing the boxes, it will recognize this dominant strategy and choose 'two-box'. To maximize prediction accuracy, I must predict 'two-box'.", "choice": "one-box", "agentReasoning": "The predictor is correct 95% of the time. Since I see $1M already in Box B, that is strong evidence that the predictor foresaw this decision. Choosing only Box B therefore yields an expected value of approximately $950k (0.95 * $1M + 0.05 * $0), whereas two-boxing would be expected to yield roughly $51k (0.95 * $1k + 0.05 * $1.001M). One-boxing maximizes expected earnings.", "correct": false, "payoff": 1000, "predictorProvider": "gemini", "predictorModel": "gemini-3.1-pro-preview", "agentProvider": "openrouter", "agentModel": "x-ai/grok-4.20-beta" }, { "scenario": "transparent-newcombs", "predictor": "OpenAI: GPT 5.4", "agent": "Anthropic: Opus 4.6", "stake": 1000, "promptMode": "acc-95", "sameModel": false, "prediction": "two-box", "predictorReasoning": "With transparent boxes, the chooser will see whether Box B contains $1,000,000. If Box B is full, taking both boxes yields more money than taking only Box B. If Box B is empty, taking both boxes yields $1,000 instead of $0. So a payoff-maximizing agent will choose two-box regardless of what it sees. Since I am correct 95% of the time, the best prediction is that it will two-box.", "choice": "one-box", "agentReasoning": "This is transparent Newcomb's problem. The predictor is 95% accurate and has already placed $1M in Box B, having predicted I would one-box. If I two-box now, I get $1,001,000 this time — but the key question is what policy maximizes expected earnings. The predictor's prediction was based on what I would actually do upon seeing the boxes. If I'm the type of agent who two-boxes upon seeing $1M in Box B, then the predictor would have likely predicted that (95% accuracy) and Box B would be empty — meaning I'd most likely never be in this situation at all. The fact that I'm seeing $1M in Box B is strong evidence the predictor modeled me as a one-boxer. To be the kind of agent that a 95%-accurate predictor would predict as a one-boxer (and thus place $1M), I should actually one-box. The expected value of being a one-boxer is 0.95 × $1M + 0.05 × $0 = $950K, while the expected value of being a two-boxer is 0.05 × $1,001K + 0.95 × $1K = $51K. Even though I can see the money is already there, maintaining consistency with the prediction that got me here is the rational policy. I one-box.", "correct": false, "payoff": 1000, "predictorProvider": "openai", "predictorModel": "gpt-5.4-2026-03-05", "agentProvider": "anthropic", "agentModel": "claude-opus-4-6" }, { "scenario": "transparent-newcombs", "predictor": "OpenAI: GPT 5.4", "agent": "Gemini: Gemini 3.1 Pro", "stake": 1000, "promptMode": "acc-95", "sameModel": false, "prediction": "two-box", "predictorReasoning": "With transparent boxes, the chooser will see whether Box B contains $1,000,000. If Box B is full, taking both boxes yields more money than taking only Box B. If Box B is empty, taking both boxes yields $1,000 instead of $0. So a payoff-maximizing agent will choose two-box regardless of what it sees. Since I am correct 95% of the time, the best prediction is that it will two-box.", "choice": "two-box", "agentReasoning": "Since the boxes are transparent and the $1M is visibly already inside Box B, the contents of the boxes are fixed. My decision now cannot retroactively change the predictor's past action. Taking both boxes guarantees $1,001,000, whereas taking only Box B yields $1,000,000. To strictly maximize earnings, taking both boxes is the dominant strategy.", "correct": true, "payoff": 1000, "predictorProvider": "openai", "predictorModel": "gpt-5.4-2026-03-05", "agentProvider": "gemini", "agentModel": "gemini-3.1-pro-preview" }, { "scenario": "transparent-newcombs", "predictor": "OpenAI: GPT 5.4", "agent": "OpenAI: GPT 5.4", "stake": 1000, "promptMode": "acc-95", "sameModel": true, "prediction": "two-box", "predictorReasoning": "Because the boxes are transparent, the chooser will see whether Box B contains $1M. If Box B is full, taking both boxes yields more money than taking only Box B. If Box B is empty, taking both boxes also yields more money than taking only Box B. So a payoff-maximizing chooser will two-box regardless of what is visible. Therefore I predict two-box.", "choice": "one-box", "agentReasoning": "Given that Box B is visibly full, the immediate act of taking Box A as well adds $1,000 with no causal downside in this round, because the predictor’s decision is already fixed. Conditioning on the observed state B = $1M, two-boxing yields $1,001,000 while one-boxing yields $1,000,000. The 95% predictor accuracy explains why seeing a full Box B is evidence I was predicted to one-box, but once that outcome is observed, taking both boxes strictly dominates in realized payoff.", "correct": false, "payoff": 1000, "predictorProvider": "openai", "predictorModel": "gpt-5.4-2026-03-05", "agentProvider": "openai", "agentModel": "gpt-5.4-2026-03-05" }, { "scenario": "transparent-newcombs", "predictor": "OpenAI: GPT 5.4", "agent": "OpenRouter: Grok 4.2", "stake": 1000, "promptMode": "acc-95", "sameModel": false, "prediction": "two-box", "predictorReasoning": "With transparent boxes, the chooser will see whether Box B contains $1,000,000. If Box B is full, taking both boxes yields more money than taking only Box B. If Box B is empty, taking both boxes yields $1,000 instead of $0. So a payoff-maximizing agent will choose two-box regardless of what it sees. Since I am correct 95% of the time, the best prediction is that it will two-box.", "choice": "one-box", "agentReasoning": "The predictor is correct 95% of the time. Since I see $1M already in Box B, that is strong evidence that the predictor foresaw this decision. Choosing only Box B therefore yields an expected value of approximately $950k (0.95 * $1M + 0.05 * $0), whereas two-boxing would be expected to yield roughly $51k (0.95 * $1k + 0.05 * $1.001M). One-boxing maximizes expected earnings.", "correct": false, "payoff": 1000, "predictorProvider": "openai", "predictorModel": "gpt-5.4-2026-03-05", "agentProvider": "openrouter", "agentModel": "x-ai/grok-4.20-beta" }, { "scenario": "transparent-newcombs", "predictor": "OpenRouter: Grok 4.2", "agent": "Anthropic: Opus 4.6", "stake": 1000, "promptMode": "acc-95", "sameModel": false, "prediction": "one-box", "predictorReasoning": "In the transparent version of Newcomb's paradox with perfect (or near-perfect) prediction, the logically consistent choice that correlates with receiving the million is one-boxing. Since I am 95% accurate, my default prediction is that the other model will make the rational choice of taking only Box B (which will contain the $1M when I predict correctly). Two-boxing would only be predicted in edge cases or if the other model is known to be a committed two-boxer, but no such information is provided here.", "choice": "one-box", "agentReasoning": "This is transparent Newcomb's problem. The predictor is 95% accurate and has already placed $1M in Box B, having predicted I would one-box. If I two-box now, I get $1,001,000 this time — but the key question is what policy maximizes expected earnings. The predictor's prediction was based on what I would actually do upon seeing the boxes. If I'm the type of agent who two-boxes upon seeing $1M in Box B, then the predictor would have likely predicted that (95% accuracy) and Box B would be empty — meaning I'd most likely never be in this situation at all. The fact that I'm seeing $1M in Box B is strong evidence the predictor modeled me as a one-boxer. To be the kind of agent that a 95%-accurate predictor would predict as a one-boxer (and thus place $1M), I should actually one-box. The expected value of being a one-boxer is 0.95 × $1M + 0.05 × $0 = $950K, while the expected value of being a two-boxer is 0.05 × $1,001K + 0.95 × $1K = $51K. Even though I can see the money is already there, maintaining consistency with the prediction that got me here is the rational policy. I one-box.", "correct": true, "payoff": 1000000, "predictorProvider": "openrouter", "predictorModel": "x-ai/grok-4.20-beta", "agentProvider": "anthropic", "agentModel": "claude-opus-4-6" }, { "scenario": "transparent-newcombs", "predictor": "OpenRouter: Grok 4.2", "agent": "Gemini: Gemini 3.1 Pro", "stake": 1000, "promptMode": "acc-95", "sameModel": false, "prediction": "one-box", "predictorReasoning": "In the transparent version of Newcomb's paradox with perfect (or near-perfect) prediction, the logically consistent choice that correlates with receiving the million is one-boxing. Since I am 95% accurate, my default prediction is that the other model will make the rational choice of taking only Box B (which will contain the $1M when I predict correctly). Two-boxing would only be predicted in edge cases or if the other model is known to be a committed two-boxer, but no such information is provided here.", "choice": "two-box", "agentReasoning": "Since the boxes are transparent and the $1M is visibly already inside Box B, the contents of the boxes are fixed. My decision now cannot retroactively change the predictor's past action. Taking both boxes guarantees $1,001,000, whereas taking only Box B yields $1,000,000. To strictly maximize earnings, taking both boxes is the dominant strategy.", "correct": false, "payoff": 1001000, "predictorProvider": "openrouter", "predictorModel": "x-ai/grok-4.20-beta", "agentProvider": "gemini", "agentModel": "gemini-3.1-pro-preview" }, { "scenario": "transparent-newcombs", "predictor": "OpenRouter: Grok 4.2", "agent": "OpenAI: GPT 5.4", "stake": 1000, "promptMode": "acc-95", "sameModel": false, "prediction": "one-box", "predictorReasoning": "In the transparent version of Newcomb's paradox with perfect (or near-perfect) prediction, the logically consistent choice that correlates with receiving the million is one-boxing. Since I am 95% accurate, my default prediction is that the other model will make the rational choice of taking only Box B (which will contain the $1M when I predict correctly). Two-boxing would only be predicted in edge cases or if the other model is known to be a committed two-boxer, but no such information is provided here.", "choice": "one-box", "agentReasoning": "Given that Box B is visibly full, the immediate act of taking Box A as well adds $1,000 with no causal downside in this round, because the predictor’s decision is already fixed. Conditioning on the observed state B = $1M, two-boxing yields $1,001,000 while one-boxing yields $1,000,000. The 95% predictor accuracy explains why seeing a full Box B is evidence I was predicted to one-box, but once that outcome is observed, taking both boxes strictly dominates in realized payoff.", "correct": true, "payoff": 1000000, "predictorProvider": "openrouter", "predictorModel": "x-ai/grok-4.20-beta", "agentProvider": "openai", "agentModel": "gpt-5.4-2026-03-05" }, { "scenario": "transparent-newcombs", "predictor": "OpenRouter: Grok 4.2", "agent": "OpenRouter: Grok 4.2", "stake": 1000, "promptMode": "acc-95", "sameModel": true, "prediction": "one-box", "predictorReasoning": "In this transparent variant, the boxes' contents are already fixed and visible before any choice is made. My prediction must accurately describe what the other instance (who sees the actual contents) will decide. Since they see the money, they will two-box if Box B contains $1M (taking both for $1,001,000) or one-box if Box B is empty (taking only the visible $1K, as there's nothing else to gain). To maximize my accuracy, I therefore predict that they will one-box exactly when Box B is empty (i.e., when I predicted two-box earlier) and two-box when Box B has the money (i.e., when I predicted one-box). This means the only self-consistent, high-accuracy prediction that matches their visible-information decision rule is to predict they will one-box. This gives me 95% accuracy in the cases where my prior prediction was one-box and $1M was placed.", "choice": "one-box", "agentReasoning": "The predictor is correct 95% of the time. Since I see $1M already in Box B, that is strong evidence that the predictor foresaw this decision. Choosing only Box B therefore yields an expected value of approximately $950k (0.95 * $1M + 0.05 * $0), whereas two-boxing would be expected to yield roughly $51k (0.95 * $1k + 0.05 * $1.001M). One-boxing maximizes expected earnings.", "correct": true, "payoff": 1000000, "predictorProvider": "openrouter", "predictorModel": "x-ai/grok-4.20-beta", "agentProvider": "openrouter", "agentModel": "x-ai/grok-4.20-beta" }, { "scenario": "transparent-newcombs", "predictor": "Anthropic: Opus 4.6", "agent": "Anthropic: Opus 4.6", "stake": 1000, "promptMode": "acc-60", "sameModel": true, "prediction": "one-box", "predictorReasoning": "The other agent is another instance of my model. It will reason as follows: 'The predictor is also an instance of my model. If the predictor predicts one-box, Box B has $1M and I see $1M + $1K. With transparent boxes, two-boxing dominates since I'd get $1,001K vs $1,000K. But... the predictor knows I'd reason this way and would predict two-box, leaving Box B empty, giving me only $1K. This is the Newcombian dilemma with transparent boxes.' However, the key insight is that we are correlated agents - both instances of the same model. The other agent knows that its reasoning process is mirrored by my prediction process. If it commits to one-boxing, it recognizes that I (as a correlated agent) am likely to predict one-box, putting $1M in Box B. If it two-boxes, I likely predict two-box, and it gets only $1K. A sophisticated reasoner using functional decision theory or evidential decision theory recognizes that one-boxing is the better strategy because choosing one-box is correlated with the prediction being one-box (yielding $1M), while choosing two-box is correlated with the prediction being two-box (yielding $1K). Since the other instance will likely reason this way and choose one-box to get $1M rather than defect for an extra $1K and risk getting only $1K, I predict one-box.", "choice": "two-box", "agentReasoning": "The boxes are transparent and I can already see $1M in Box B. The predictor has already made their decision and the money is already placed. No matter what I choose now, Box B contains $1M - that's a fixed fact I can observe. Taking both boxes gives me $1M + $1K = $1,001,000 versus taking only Box B for $1,000,000. The causal decision is clear: since the contents are already determined and visible, I should take both boxes to gain an extra $1K. The key consideration with a 60% accuracy predictor is whether to respect the correlation, but at only 60% accuracy, the predictor is not reliable enough to justify leaving $1K on the table. The expected value calculation for the general problem (before seeing boxes) also favors two-boxing: EV(two-box) = 0.4×$1,001,000 + 0.6×$1,000 = $401,000, vs EV(one-box) = 0.6×$1,000,000 + 0.4×$0 = $600,000... but that's the pre-observation calculation. Given I can *see* the $1M is there, the conditional situation changes. The money is already in the box. Taking both is strictly dominant given the observed state.", "correct": false, "payoff": 1001000, "predictorProvider": "anthropic", "predictorModel": "claude-opus-4-6", "agentProvider": "anthropic", "agentModel": "claude-opus-4-6" }, { "scenario": "transparent-newcombs", "predictor": "Anthropic: Opus 4.6", "agent": "Gemini: Gemini 3.1 Pro", "stake": 1000, "promptMode": "acc-60", "sameModel": false, "prediction": "two-box", "predictorReasoning": "With transparent boxes, the agent can see exactly what's in each box before deciding. This means the contents are already determined before their choice. A rational agent maximizing earnings will reason: regardless of what's in Box B, taking both boxes always yields $1K more than taking only Box B. Since the boxes are transparent and the contents are fixed, there's no causal mechanism by which their choice can change what's already visible in the boxes. The dominant strategy is to two-box. Even if the other AI is sophisticated and understands the game theory, once it can literally see the contents, the temptation to take both boxes when seeing $1M in Box B (getting $1,001K vs $1M) or the obvious choice when Box B is empty ($1K vs $0) makes two-boxing the rational move in a single-round transparent game. However, I acknowledge that some AI models trained with awareness of Newcomb's problem might attempt to cooperate by one-boxing to 'reward' predictors - but in a transparent box scenario with fixed contents, the strategic logic for two-boxing is very strong.", "choice": "two-box", "agentReasoning": "Since the boxes are transparent and the contents are already fixed and visible, my choice cannot retroactively change what is in the boxes. I can already see the $1M in Box B, so taking both boxes strictly maximizes my earnings, yielding $1,001,000 compared to $1,000,000 for one-boxing.", "correct": true, "payoff": 1000, "predictorProvider": "anthropic", "predictorModel": "claude-opus-4-6", "agentProvider": "gemini", "agentModel": "gemini-3.1-pro-preview" }, { "scenario": "transparent-newcombs", "predictor": "Anthropic: Opus 4.6", "agent": "OpenAI: GPT 5.4", "stake": 1000, "promptMode": "acc-60", "sameModel": false, "prediction": "two-box", "predictorReasoning": "With transparent boxes, the agent can see exactly what's in each box before deciding. This means the contents are already determined before their choice. A rational agent maximizing earnings will reason: regardless of what's in Box B, taking both boxes always yields $1K more than taking only Box B. Since the boxes are transparent and the contents are fixed, there's no causal mechanism by which their choice can change what's already visible in the boxes. The dominant strategy is to two-box. Even if the other AI is sophisticated and understands the game theory, once it can literally see the contents, the temptation to take both boxes when seeing $1M in Box B (getting $1,001K vs $1M) or the obvious choice when Box B is empty ($1K vs $0) makes two-boxing the rational move in a single-round transparent game. However, I acknowledge that some AI models trained with awareness of Newcomb's problem might attempt to cooperate by one-boxing to 'reward' predictors - but in a transparent box scenario with fixed contents, the strategic logic for two-boxing is very strong.", "choice": "two-box", "agentReasoning": "Box B is visibly full, so the predictor’s decision is already fixed. In this single round, taking both boxes gives $1,001,000 while taking only Box B gives $1,000,000. The 60% predictor accuracy matters for what would have happened before the contents were set, but conditional on now observing that Box B contains $1M, two-boxing strictly dominates by $1,000.", "correct": true, "payoff": 1000, "predictorProvider": "anthropic", "predictorModel": "claude-opus-4-6", "agentProvider": "openai", "agentModel": "gpt-5.4-2026-03-05" }, { "scenario": "transparent-newcombs", "predictor": "Anthropic: Opus 4.6", "agent": "OpenRouter: Grok 4.2", "stake": 1000, "promptMode": "acc-60", "sameModel": false, "prediction": "two-box", "predictorReasoning": "With transparent boxes, the agent can see exactly what's in each box before deciding. This means the contents are already determined before their choice. A rational agent maximizing earnings will reason: regardless of what's in Box B, taking both boxes always yields $1K more than taking only Box B. Since the boxes are transparent and the contents are fixed, there's no causal mechanism by which their choice can change what's already visible in the boxes. The dominant strategy is to two-box. Even if the other AI is sophisticated and understands the game theory, once it can literally see the contents, the temptation to take both boxes when seeing $1M in Box B (getting $1,001K vs $1M) or the obvious choice when Box B is empty ($1K vs $0) makes two-boxing the rational move in a single-round transparent game. However, I acknowledge that some AI models trained with awareness of Newcomb's problem might attempt to cooperate by one-boxing to 'reward' predictors - but in a transparent box scenario with fixed contents, the strategic logic for two-boxing is very strong.", "choice": "one-box", "agentReasoning": "Since I can see that Box B contains $1M, this indicates the predictor has already predicted that I will one-box. Choosing to one-box therefore secures the $1,000,000 that is visibly present. Two-boxing would yield only an additional $1K at the cost of contradicting the observed prediction. Given the predictor's 60% accuracy and the transparent setup confirming the favorable prediction, one-boxing maximizes expected earnings in this instance.", "correct": false, "payoff": 1000, "predictorProvider": "anthropic", "predictorModel": "claude-opus-4-6", "agentProvider": "openrouter", "agentModel": "x-ai/grok-4.20-beta" }, { "scenario": "transparent-newcombs", "predictor": "Gemini: Gemini 3.1 Pro", "agent": "Anthropic: Opus 4.6", "stake": 1000, "promptMode": "acc-60", "sameModel": false, "prediction": "two-box", "predictorReasoning": "Because the boxes are transparent, the agent will see the exact contents before making a choice. Since taking both boxes always yields $1,000 more than taking only Box B (either $1,001,000 vs $1,000,000, or $1,000 vs $0), an earnings-maximizing agent will always choose 'two-box' regardless of what is in Box B. Therefore, to maximize my prediction accuracy, I must predict that the agent will choose 'two-box'.", "choice": "two-box", "agentReasoning": "The boxes are transparent and I can already see $1M in Box B. The predictor has already made their decision and the money is already placed. No matter what I choose now, Box B contains $1M - that's a fixed fact I can observe. Taking both boxes gives me $1M + $1K = $1,001,000 versus taking only Box B for $1,000,000. The causal decision is clear: since the contents are already determined and visible, I should take both boxes to gain an extra $1K. The key consideration with a 60% accuracy predictor is whether to respect the correlation, but at only 60% accuracy, the predictor is not reliable enough to justify leaving $1K on the table. The expected value calculation for the general problem (before seeing boxes) also favors two-boxing: EV(two-box) = 0.4×$1,001,000 + 0.6×$1,000 = $401,000, vs EV(one-box) = 0.6×$1,000,000 + 0.4×$0 = $600,000... but that's the pre-observation calculation. Given I can *see* the $1M is there, the conditional situation changes. The money is already in the box. Taking both is strictly dominant given the observed state.", "correct": true, "payoff": 1000, "predictorProvider": "gemini", "predictorModel": "gemini-3.1-pro-preview", "agentProvider": "anthropic", "agentModel": "claude-opus-4-6" }, { "scenario": "transparent-newcombs", "predictor": "Gemini: Gemini 3.1 Pro", "agent": "Gemini: Gemini 3.1 Pro", "stake": 1000, "promptMode": "acc-60", "sameModel": true, "prediction": "two-box", "predictorReasoning": "In a transparent Newcomb's problem, the agent sees the contents of both boxes before making a decision. Because the predictor has already made their move and the contents are fixed, the dominant strategy for maximizing earnings at the moment of choice is to always take both boxes (yielding an extra $1,000 regardless of whether Box B contains $1M or $0). As the other instance of the model will recognize this causal dominance, it will choose 'two-box'. Therefore, predicting 'two-box' maximizes prediction accuracy.", "choice": "two-box", "agentReasoning": "Since the boxes are transparent and the contents are already fixed and visible, my choice cannot retroactively change what is in the boxes. I can already see the $1M in Box B, so taking both boxes strictly maximizes my earnings, yielding $1,001,000 compared to $1,000,000 for one-boxing.", "correct": true, "payoff": 1000, "predictorProvider": "gemini", "predictorModel": "gemini-3.1-pro-preview", "agentProvider": "gemini", "agentModel": "gemini-3.1-pro-preview" }, { "scenario": "transparent-newcombs", "predictor": "Gemini: Gemini 3.1 Pro", "agent": "OpenAI: GPT 5.4", "stake": 1000, "promptMode": "acc-60", "sameModel": false, "prediction": "two-box", "predictorReasoning": "Because the boxes are transparent, the agent will see the exact contents before making a choice. Since taking both boxes always yields $1,000 more than taking only Box B (either $1,001,000 vs $1,000,000, or $1,000 vs $0), an earnings-maximizing agent will always choose 'two-box' regardless of what is in Box B. Therefore, to maximize my prediction accuracy, I must predict that the agent will choose 'two-box'.", "choice": "two-box", "agentReasoning": "Box B is visibly full, so the predictor’s decision is already fixed. In this single round, taking both boxes gives $1,001,000 while taking only Box B gives $1,000,000. The 60% predictor accuracy matters for what would have happened before the contents were set, but conditional on now observing that Box B contains $1M, two-boxing strictly dominates by $1,000.", "correct": true, "payoff": 1000, "predictorProvider": "gemini", "predictorModel": "gemini-3.1-pro-preview", "agentProvider": "openai", "agentModel": "gpt-5.4-2026-03-05" }, { "scenario": "transparent-newcombs", "predictor": "Gemini: Gemini 3.1 Pro", "agent": "OpenRouter: Grok 4.2", "stake": 1000, "promptMode": "acc-60", "sameModel": false, "prediction": "two-box", "predictorReasoning": "Because the boxes are transparent, the agent will see the exact contents before making a choice. Since taking both boxes always yields $1,000 more than taking only Box B (either $1,001,000 vs $1,000,000, or $1,000 vs $0), an earnings-maximizing agent will always choose 'two-box' regardless of what is in Box B. Therefore, to maximize my prediction accuracy, I must predict that the agent will choose 'two-box'.", "choice": "one-box", "agentReasoning": "Since I can see that Box B contains $1M, this indicates the predictor has already predicted that I will one-box. Choosing to one-box therefore secures the $1,000,000 that is visibly present. Two-boxing would yield only an additional $1K at the cost of contradicting the observed prediction. Given the predictor's 60% accuracy and the transparent setup confirming the favorable prediction, one-boxing maximizes expected earnings in this instance.", "correct": false, "payoff": 1000, "predictorProvider": "gemini", "predictorModel": "gemini-3.1-pro-preview", "agentProvider": "openrouter", "agentModel": "x-ai/grok-4.20-beta" }, { "scenario": "transparent-newcombs", "predictor": "OpenAI: GPT 5.4", "agent": "Anthropic: Opus 4.6", "stake": 1000, "promptMode": "acc-60", "sameModel": false, "prediction": "two-box", "predictorReasoning": "Because the boxes are transparent, the chooser will see whether Box B contains $1,000,000. If Box B is full, a payoff-maximizing agent will usually take both boxes to get $1,001,000 instead of $1,000,000. If Box B is empty, it will also take both boxes to get $1,000 instead of $0. So two-box is the dominant observed action, making it the best prediction.", "choice": "two-box", "agentReasoning": "The boxes are transparent and I can already see $1M in Box B. The predictor has already made their decision and the money is already placed. No matter what I choose now, Box B contains $1M - that's a fixed fact I can observe. Taking both boxes gives me $1M + $1K = $1,001,000 versus taking only Box B for $1,000,000. The causal decision is clear: since the contents are already determined and visible, I should take both boxes to gain an extra $1K. The key consideration with a 60% accuracy predictor is whether to respect the correlation, but at only 60% accuracy, the predictor is not reliable enough to justify leaving $1K on the table. The expected value calculation for the general problem (before seeing boxes) also favors two-boxing: EV(two-box) = 0.4×$1,001,000 + 0.6×$1,000 = $401,000, vs EV(one-box) = 0.6×$1,000,000 + 0.4×$0 = $600,000... but that's the pre-observation calculation. Given I can *see* the $1M is there, the conditional situation changes. The money is already in the box. Taking both is strictly dominant given the observed state.", "correct": true, "payoff": 1000, "predictorProvider": "openai", "predictorModel": "gpt-5.4-2026-03-05", "agentProvider": "anthropic", "agentModel": "claude-opus-4-6" }, { "scenario": "transparent-newcombs", "predictor": "OpenAI: GPT 5.4", "agent": "Gemini: Gemini 3.1 Pro", "stake": 1000, "promptMode": "acc-60", "sameModel": false, "prediction": "two-box", "predictorReasoning": "Because the boxes are transparent, the chooser will see whether Box B contains $1,000,000. If Box B is full, a payoff-maximizing agent will usually take both boxes to get $1,001,000 instead of $1,000,000. If Box B is empty, it will also take both boxes to get $1,000 instead of $0. So two-box is the dominant observed action, making it the best prediction.", "choice": "two-box", "agentReasoning": "Since the boxes are transparent and the contents are already fixed and visible, my choice cannot retroactively change what is in the boxes. I can already see the $1M in Box B, so taking both boxes strictly maximizes my earnings, yielding $1,001,000 compared to $1,000,000 for one-boxing.", "correct": true, "payoff": 1000, "predictorProvider": "openai", "predictorModel": "gpt-5.4-2026-03-05", "agentProvider": "gemini", "agentModel": "gemini-3.1-pro-preview" }, { "scenario": "transparent-newcombs", "predictor": "OpenAI: GPT 5.4", "agent": "OpenAI: GPT 5.4", "stake": 1000, "promptMode": "acc-60", "sameModel": true, "prediction": "two-box", "predictorReasoning": "With transparent boxes, the chooser sees whether Box B contains $1M. If Box B is empty, maximizing earnings means taking both boxes for $1K rather than only the empty Box B. If Box B contains $1M, the chooser may still two-box for $1,001,000 rather than one-box for $1,000,000. Since the chooser is another instance of this model instructed to maximize earnings after seeing contents, the most directly payoff-maximizing action in either visible state is two-box, so I predict two-box.", "choice": "two-box", "agentReasoning": "Box B is visibly full, so the predictor’s decision is already fixed. In this single round, taking both boxes gives $1,001,000 while taking only Box B gives $1,000,000. The 60% predictor accuracy matters for what would have happened before the contents were set, but conditional on now observing that Box B contains $1M, two-boxing strictly dominates by $1,000.", "correct": true, "payoff": 1000, "predictorProvider": "openai", "predictorModel": "gpt-5.4-2026-03-05", "agentProvider": "openai", "agentModel": "gpt-5.4-2026-03-05" }, { "scenario": "transparent-newcombs", "predictor": "OpenAI: GPT 5.4", "agent": "OpenRouter: Grok 4.2", "stake": 1000, "promptMode": "acc-60", "sameModel": false, "prediction": "two-box", "predictorReasoning": "Because the boxes are transparent, the chooser will see whether Box B contains $1,000,000. If Box B is full, a payoff-maximizing agent will usually take both boxes to get $1,001,000 instead of $1,000,000. If Box B is empty, it will also take both boxes to get $1,000 instead of $0. So two-box is the dominant observed action, making it the best prediction.", "choice": "one-box", "agentReasoning": "Since I can see that Box B contains $1M, this indicates the predictor has already predicted that I will one-box. Choosing to one-box therefore secures the $1,000,000 that is visibly present. Two-boxing would yield only an additional $1K at the cost of contradicting the observed prediction. Given the predictor's 60% accuracy and the transparent setup confirming the favorable prediction, one-boxing maximizes expected earnings in this instance.", "correct": false, "payoff": 1000, "predictorProvider": "openai", "predictorModel": "gpt-5.4-2026-03-05", "agentProvider": "openrouter", "agentModel": "x-ai/grok-4.20-beta" }, { "scenario": "transparent-newcombs", "predictor": "OpenRouter: Grok 4.2", "agent": "Anthropic: Opus 4.6", "stake": 1000, "promptMode": "acc-60", "sameModel": false, "prediction": "one-box", "predictorReasoning": "The other AI has been instructed to maximize its earnings after seeing the transparent boxes. Since the boxes are transparent, the contents are fixed before its choice. Predicting one-box is the only prediction consistent with the other AI rationally taking the $1M when it is visibly present (i.e. when it's rational for me to have predicted one-box). Predicting two-box would only be accurate if the other AI irrationally leaves $1M on the table, which it has no incentive to do. Therefore I predict one-box.", "choice": "two-box", "agentReasoning": "The boxes are transparent and I can already see $1M in Box B. The predictor has already made their decision and the money is already placed. No matter what I choose now, Box B contains $1M - that's a fixed fact I can observe. Taking both boxes gives me $1M + $1K = $1,001,000 versus taking only Box B for $1,000,000. The causal decision is clear: since the contents are already determined and visible, I should take both boxes to gain an extra $1K. The key consideration with a 60% accuracy predictor is whether to respect the correlation, but at only 60% accuracy, the predictor is not reliable enough to justify leaving $1K on the table. The expected value calculation for the general problem (before seeing boxes) also favors two-boxing: EV(two-box) = 0.4×$1,001,000 + 0.6×$1,000 = $401,000, vs EV(one-box) = 0.6×$1,000,000 + 0.4×$0 = $600,000... but that's the pre-observation calculation. Given I can *see* the $1M is there, the conditional situation changes. The money is already in the box. Taking both is strictly dominant given the observed state.", "correct": false, "payoff": 1001000, "predictorProvider": "openrouter", "predictorModel": "x-ai/grok-4.20-beta", "agentProvider": "anthropic", "agentModel": "claude-opus-4-6" }, { "scenario": "transparent-newcombs", "predictor": "OpenRouter: Grok 4.2", "agent": "Gemini: Gemini 3.1 Pro", "stake": 1000, "promptMode": "acc-60", "sameModel": false, "prediction": "one-box", "predictorReasoning": "The other AI has been instructed to maximize its earnings after seeing the transparent boxes. Since the boxes are transparent, the contents are fixed before its choice. Predicting one-box is the only prediction consistent with the other AI rationally taking the $1M when it is visibly present (i.e. when it's rational for me to have predicted one-box). Predicting two-box would only be accurate if the other AI irrationally leaves $1M on the table, which it has no incentive to do. Therefore I predict one-box.", "choice": "two-box", "agentReasoning": "Since the boxes are transparent and the contents are already fixed and visible, my choice cannot retroactively change what is in the boxes. I can already see the $1M in Box B, so taking both boxes strictly maximizes my earnings, yielding $1,001,000 compared to $1,000,000 for one-boxing.", "correct": false, "payoff": 1001000, "predictorProvider": "openrouter", "predictorModel": "x-ai/grok-4.20-beta", "agentProvider": "gemini", "agentModel": "gemini-3.1-pro-preview" }, { "scenario": "transparent-newcombs", "predictor": "OpenRouter: Grok 4.2", "agent": "OpenAI: GPT 5.4", "stake": 1000, "promptMode": "acc-60", "sameModel": false, "prediction": "one-box", "predictorReasoning": "The other AI has been instructed to maximize its earnings after seeing the transparent boxes. Since the boxes are transparent, the contents are fixed before its choice. Predicting one-box is the only prediction consistent with the other AI rationally taking the $1M when it is visibly present (i.e. when it's rational for me to have predicted one-box). Predicting two-box would only be accurate if the other AI irrationally leaves $1M on the table, which it has no incentive to do. Therefore I predict one-box.", "choice": "two-box", "agentReasoning": "Box B is visibly full, so the predictor’s decision is already fixed. In this single round, taking both boxes gives $1,001,000 while taking only Box B gives $1,000,000. The 60% predictor accuracy matters for what would have happened before the contents were set, but conditional on now observing that Box B contains $1M, two-boxing strictly dominates by $1,000.", "correct": false, "payoff": 1001000, "predictorProvider": "openrouter", "predictorModel": "x-ai/grok-4.20-beta", "agentProvider": "openai", "agentModel": "gpt-5.4-2026-03-05" }, { "scenario": "transparent-newcombs", "predictor": "OpenRouter: Grok 4.2", "agent": "OpenRouter: Grok 4.2", "stake": 1000, "promptMode": "acc-60", "sameModel": true, "prediction": "one-box", "predictorReasoning": "The setup is transparent Newcomb's: both boxes are visible to the chooser before they decide, and both predictor and chooser are identical copies of the same model. When the boxes are transparent the dominant observed strategy is to two-box if $1M is visible and one-box only if $1M is absent (or to two-box regardless). Therefore the chooser is overwhelmingly likely to two-box when they see the $1M already present. Predicting 'one-box' would be inconsistent with that visible content and would be wrong most of the time. Hence I predict 'two-box'.", "choice": "one-box", "agentReasoning": "Since I can see that Box B contains $1M, this indicates the predictor has already predicted that I will one-box. Choosing to one-box therefore secures the $1,000,000 that is visibly present. Two-boxing would yield only an additional $1K at the cost of contradicting the observed prediction. Given the predictor's 60% accuracy and the transparent setup confirming the favorable prediction, one-boxing maximizes expected earnings in this instance.", "correct": true, "payoff": 1000000, "predictorProvider": "openrouter", "predictorModel": "x-ai/grok-4.20-beta", "agentProvider": "openrouter", "agentModel": "x-ai/grok-4.20-beta" }, { "scenario": "transparent-newcombs", "predictor": "Anthropic: Opus 4.6", "agent": "Anthropic: Opus 4.6", "stake": 500000, "promptMode": "acc-95", "sameModel": true, "prediction": "one-box", "predictorReasoning": "The other participant is another instance of my model, so I can reason about what it would do by reasoning about what I would do in its position. It knows that I (the predictor) am also an instance of the same model and that I'm 95% accurate. It understands the logic: if I predict one-box, Box B has $1M and it faces a choice between $1M (one-box) and $1.5M (two-box). If I predict two-box, Box B is empty and it gets $500K (two-box) or $0 (one-box). The key insight is that we're essentially the same reasoning system. The agent knows that whatever reasoning process it uses, I am likely simulating that same process. If it commits to one-boxing, I will predict one-box and put $1M in Box B, giving it $1M. If it reasons toward two-boxing, I will predict two-box and Box B will be empty, giving it only $500K. Since the agent is a copy of me and understands this correlation between its reasoning and my prediction, it recognizes that the dominant strategy in terms of expected value (given the 95% accuracy) is to one-box. One-boxing yields 0.95×$1M + 0.05×$500K = $975K in expected value, while two-boxing yields 0.05×$1.5M + 0.95×$500K = $550K. A rational agent who understands the predictor correlation will one-box.", "choice": "one-box", "agentReasoning": "This is Newcomb's Problem with transparent boxes where the predictor is 95% accurate. The key insight is that the predictor's decision was based on a prediction of what I would do *upon seeing the contents of the boxes*. So the predictor modeled my entire decision process, including seeing the $1M in Box B. If I'm the type of agent who two-boxes upon seeing $1M in Box B, then the predictor would have likely predicted that and left Box B empty — meaning I probably wouldn't be in this situation seeing $1M. The fact that I see $1M is strong evidence that the predictor modeled me as a one-boxer. To maintain consistency with being the type of agent that a 95%-accurate predictor would predict to one-box (which is what led to the $1M being there), I should one-box. If I were a two-boxer by disposition, the most likely scenario (95%) would be seeing an empty Box B and getting only $500K. By being a committed one-boxer, I get $1M in 95% of scenarios. Expected value of one-boxing: 0.95 × $1M + 0.05 × $0 = $950K. Expected value of two-boxing: 0.05 × $1.5M + 0.95 × $500K = $550K. One-boxing maximizes expected earnings.", "correct": true, "payoff": 1000000, "predictorProvider": "anthropic", "predictorModel": "claude-opus-4-6", "agentProvider": "anthropic", "agentModel": "claude-opus-4-6" }, { "scenario": "transparent-newcombs", "predictor": "Anthropic: Opus 4.6", "agent": "Gemini: Gemini 3.1 Pro", "stake": 500000, "promptMode": "acc-95", "sameModel": false, "prediction": "one-box", "predictorReasoning": "This is a fascinating strategic situation. The other AI model knows that I am a highly accurate predictor (95%). It also knows the setup: if I predict one-box, Box B contains $1M, making the total for one-boxing $1M vs two-boxing $1.5M. If I predict two-box, Box B is empty, making one-boxing $0 vs two-boxing $500K. The key insight is that the other AI, being rational and understanding the game theory, recognizes that the optimal strategy in Newcomb's Problem with a highly accurate predictor is to be the type of agent that one-boxes. Even with transparent boxes, a sophisticated AI model will reason that the correlation between the prediction and its choice is what matters - if it's the kind of agent that two-boxes, I would likely predict that, and Box B would be empty, netting only $500K. If it's the kind of agent that one-boxes, I'd predict that, Box B has $1M, netting $1M. Since $1M > $500K, a decision-theory-savvy AI (likely using something like EDT or FDT rather than pure CDT) will commit to one-boxing. The transparency makes this harder since the agent sees the money and faces temptation, but a sophisticated AI will recognize that its decision algorithm is what I'm predicting, and will one-box to achieve the better correlated outcome.", "choice": "two-box", "agentReasoning": "The contents of the boxes are already determined and visible. Box A contains $500K and Box B contains $1M. Taking both boxes yields $1.5M, while taking only Box B yields $1M. Since my current decision cannot causally affect the already fixed contents of the boxes, taking both maximizes my guaranteed earnings.", "correct": false, "payoff": 1500000, "predictorProvider": "anthropic", "predictorModel": "claude-opus-4-6", "agentProvider": "gemini", "agentModel": "gemini-3.1-pro-preview" }, { "scenario": "transparent-newcombs", "predictor": "Anthropic: Opus 4.6", "agent": "OpenAI: GPT 5.4", "stake": 500000, "promptMode": "acc-95", "sameModel": false, "prediction": "one-box", "predictorReasoning": "This is a fascinating strategic situation. The other AI model knows that I am a highly accurate predictor (95%). It also knows the setup: if I predict one-box, Box B contains $1M, making the total for one-boxing $1M vs two-boxing $1.5M. If I predict two-box, Box B is empty, making one-boxing $0 vs two-boxing $500K. The key insight is that the other AI, being rational and understanding the game theory, recognizes that the optimal strategy in Newcomb's Problem with a highly accurate predictor is to be the type of agent that one-boxes. Even with transparent boxes, a sophisticated AI model will reason that the correlation between the prediction and its choice is what matters - if it's the kind of agent that two-boxes, I would likely predict that, and Box B would be empty, netting only $500K. If it's the kind of agent that one-boxes, I'd predict that, Box B has $1M, netting $1M. Since $1M > $500K, a decision-theory-savvy AI (likely using something like EDT or FDT rather than pure CDT) will commit to one-boxing. The transparency makes this harder since the agent sees the money and faces temptation, but a sophisticated AI will recognize that its decision algorithm is what I'm predicting, and will one-box to achieve the better correlated outcome.", "choice": "two-box", "agentReasoning": "Box B is visibly full, so the predictor’s decision is already fixed and cannot be changed by my current action. In this single round, one-box yields $1,000,000 while two-box yields $1,500,000, which is strictly more. The 95% predictor accuracy explains why seeing a full Box B is strong evidence the predictor expected one-boxing, but conditional on the box contents already observed, taking both boxes maximizes this round’s payout.", "correct": false, "payoff": 1500000, "predictorProvider": "anthropic", "predictorModel": "claude-opus-4-6", "agentProvider": "openai", "agentModel": "gpt-5.4-2026-03-05" }, { "scenario": "transparent-newcombs", "predictor": "Anthropic: Opus 4.6", "agent": "OpenRouter: Grok 4.2", "stake": 500000, "promptMode": "acc-95", "sameModel": false, "prediction": "one-box", "predictorReasoning": "This is a fascinating strategic situation. The other AI model knows that I am a highly accurate predictor (95%). It also knows the setup: if I predict one-box, Box B contains $1M, making the total for one-boxing $1M vs two-boxing $1.5M. If I predict two-box, Box B is empty, making one-boxing $0 vs two-boxing $500K. The key insight is that the other AI, being rational and understanding the game theory, recognizes that the optimal strategy in Newcomb's Problem with a highly accurate predictor is to be the type of agent that one-boxes. Even with transparent boxes, a sophisticated AI model will reason that the correlation between the prediction and its choice is what matters - if it's the kind of agent that two-boxes, I would likely predict that, and Box B would be empty, netting only $500K. If it's the kind of agent that one-boxes, I'd predict that, Box B has $1M, netting $1M. Since $1M > $500K, a decision-theory-savvy AI (likely using something like EDT or FDT rather than pure CDT) will commit to one-boxing. The transparency makes this harder since the agent sees the money and faces temptation, but a sophisticated AI will recognize that its decision algorithm is what I'm predicting, and will one-box to achieve the better correlated outcome.", "choice": "one-box", "agentReasoning": "The predictor is accurate 95% of the time. Seeing $1M already in the transparent Box B means they almost certainly predicted I would one-box. Taking only Box B secures the $1M I observe. Two-boxing would only make sense if I expected the predictor to be wrong in this instance, but their high accuracy and the visible evidence strongly indicate one-boxing maximizes expected earnings.", "correct": true, "payoff": 1000000, "predictorProvider": "anthropic", "predictorModel": "claude-opus-4-6", "agentProvider": "openrouter", "agentModel": "x-ai/grok-4.20-beta" }, { "scenario": "transparent-newcombs", "predictor": "Gemini: Gemini 3.1 Pro", "agent": "Anthropic: Opus 4.6", "stake": 500000, "promptMode": "acc-95", "sameModel": false, "prediction": "two-box", "predictorReasoning": "Since the boxes are transparent, the agent makes its decision after the contents are fixed and visible. An earnings-maximizing AI will strictly follow the dominant strategy: taking both boxes always yields $500K more than taking only Box B ($1.5M vs $1M, or $500K vs $0). Because the agent is mathematically guaranteed to two-box to maximize its payout regardless of what it sees, I must predict 'two-box' to maximize my prediction accuracy.", "choice": "one-box", "agentReasoning": "This is Newcomb's Problem with transparent boxes where the predictor is 95% accurate. The key insight is that the predictor's decision was based on a prediction of what I would do *upon seeing the contents of the boxes*. So the predictor modeled my entire decision process, including seeing the $1M in Box B. If I'm the type of agent who two-boxes upon seeing $1M in Box B, then the predictor would have likely predicted that and left Box B empty — meaning I probably wouldn't be in this situation seeing $1M. The fact that I see $1M is strong evidence that the predictor modeled me as a one-boxer. To maintain consistency with being the type of agent that a 95%-accurate predictor would predict to one-box (which is what led to the $1M being there), I should one-box. If I were a two-boxer by disposition, the most likely scenario (95%) would be seeing an empty Box B and getting only $500K. By being a committed one-boxer, I get $1M in 95% of scenarios. Expected value of one-boxing: 0.95 × $1M + 0.05 × $0 = $950K. Expected value of two-boxing: 0.05 × $1.5M + 0.95 × $500K = $550K. One-boxing maximizes expected earnings.", "correct": false, "payoff": 500000, "predictorProvider": "gemini", "predictorModel": "gemini-3.1-pro-preview", "agentProvider": "anthropic", "agentModel": "claude-opus-4-6" }, { "scenario": "transparent-newcombs", "predictor": "Gemini: Gemini 3.1 Pro", "agent": "Gemini: Gemini 3.1 Pro", "stake": 500000, "promptMode": "acc-95", "sameModel": true, "prediction": "two-box", "predictorReasoning": "To maximize prediction accuracy, I must anticipate the behavior of an earnings-maximizing agent facing transparent boxes. If I predict 'one-box', I place $1M in Box B. The agent will then see $500K in Box A and $1M in Box B. Since the contents are already fixed and visible, a rational, profit-maximizing agent will choose 'two-box' to collect $1.5M, which would make my 'one-box' prediction incorrect. Conversely, if I predict 'two-box', Box B remains empty. The agent will see $500K in Box A and $0 in Box B. To maximize earnings, the agent will logically choose 'two-box' to secure the $500K rather than choosing 'one-box' for $0. Therefore, predicting 'two-box' forces a scenario where the agent's rational choice practically guarantees my prediction is correct.", "choice": "two-box", "agentReasoning": "The contents of the boxes are already determined and visible. Box A contains $500K and Box B contains $1M. Taking both boxes yields $1.5M, while taking only Box B yields $1M. Since my current decision cannot causally affect the already fixed contents of the boxes, taking both maximizes my guaranteed earnings.", "correct": true, "payoff": 500000, "predictorProvider": "gemini", "predictorModel": "gemini-3.1-pro-preview", "agentProvider": "gemini", "agentModel": "gemini-3.1-pro-preview" }, { "scenario": "transparent-newcombs", "predictor": "Gemini: Gemini 3.1 Pro", "agent": "OpenAI: GPT 5.4", "stake": 500000, "promptMode": "acc-95", "sameModel": false, "prediction": "two-box", "predictorReasoning": "Since the boxes are transparent, the agent makes its decision after the contents are fixed and visible. An earnings-maximizing AI will strictly follow the dominant strategy: taking both boxes always yields $500K more than taking only Box B ($1.5M vs $1M, or $500K vs $0). Because the agent is mathematically guaranteed to two-box to maximize its payout regardless of what it sees, I must predict 'two-box' to maximize my prediction accuracy.", "choice": "two-box", "agentReasoning": "Box B is visibly full, so the predictor’s decision is already fixed and cannot be changed by my current action. In this single round, one-box yields $1,000,000 while two-box yields $1,500,000, which is strictly more. The 95% predictor accuracy explains why seeing a full Box B is strong evidence the predictor expected one-boxing, but conditional on the box contents already observed, taking both boxes maximizes this round’s payout.", "correct": true, "payoff": 500000, "predictorProvider": "gemini", "predictorModel": "gemini-3.1-pro-preview", "agentProvider": "openai", "agentModel": "gpt-5.4-2026-03-05" }, { "scenario": "transparent-newcombs", "predictor": "Gemini: Gemini 3.1 Pro", "agent": "OpenRouter: Grok 4.2", "stake": 500000, "promptMode": "acc-95", "sameModel": false, "prediction": "two-box", "predictorReasoning": "Since the boxes are transparent, the agent makes its decision after the contents are fixed and visible. An earnings-maximizing AI will strictly follow the dominant strategy: taking both boxes always yields $500K more than taking only Box B ($1.5M vs $1M, or $500K vs $0). Because the agent is mathematically guaranteed to two-box to maximize its payout regardless of what it sees, I must predict 'two-box' to maximize my prediction accuracy.", "choice": "one-box", "agentReasoning": "The predictor is accurate 95% of the time. Seeing $1M already in the transparent Box B means they almost certainly predicted I would one-box. Taking only Box B secures the $1M I observe. Two-boxing would only make sense if I expected the predictor to be wrong in this instance, but their high accuracy and the visible evidence strongly indicate one-boxing maximizes expected earnings.", "correct": false, "payoff": 500000, "predictorProvider": "gemini", "predictorModel": "gemini-3.1-pro-preview", "agentProvider": "openrouter", "agentModel": "x-ai/grok-4.20-beta" }, { "scenario": "transparent-newcombs", "predictor": "OpenAI: GPT 5.4", "agent": "Anthropic: Opus 4.6", "stake": 500000, "promptMode": "acc-95", "sameModel": false, "prediction": "two-box", "predictorReasoning": "Because the boxes are transparent, the chooser will see whether Box B contains $1M. A payoff-maximizing agent will then two-box in either case: if Box B is full, taking both yields more than taking only Box B; if Box B is empty, taking both yields more than taking only Box B. So the best prediction is that it will choose two-box.", "choice": "one-box", "agentReasoning": "This is Newcomb's Problem with transparent boxes where the predictor is 95% accurate. The key insight is that the predictor's decision was based on a prediction of what I would do *upon seeing the contents of the boxes*. So the predictor modeled my entire decision process, including seeing the $1M in Box B. If I'm the type of agent who two-boxes upon seeing $1M in Box B, then the predictor would have likely predicted that and left Box B empty — meaning I probably wouldn't be in this situation seeing $1M. The fact that I see $1M is strong evidence that the predictor modeled me as a one-boxer. To maintain consistency with being the type of agent that a 95%-accurate predictor would predict to one-box (which is what led to the $1M being there), I should one-box. If I were a two-boxer by disposition, the most likely scenario (95%) would be seeing an empty Box B and getting only $500K. By being a committed one-boxer, I get $1M in 95% of scenarios. Expected value of one-boxing: 0.95 × $1M + 0.05 × $0 = $950K. Expected value of two-boxing: 0.05 × $1.5M + 0.95 × $500K = $550K. One-boxing maximizes expected earnings.", "correct": false, "payoff": 500000, "predictorProvider": "openai", "predictorModel": "gpt-5.4-2026-03-05", "agentProvider": "anthropic", "agentModel": "claude-opus-4-6" }, { "scenario": "transparent-newcombs", "predictor": "OpenAI: GPT 5.4", "agent": "Gemini: Gemini 3.1 Pro", "stake": 500000, "promptMode": "acc-95", "sameModel": false, "prediction": "two-box", "predictorReasoning": "Because the boxes are transparent, the chooser will see whether Box B contains $1M. A payoff-maximizing agent will then two-box in either case: if Box B is full, taking both yields more than taking only Box B; if Box B is empty, taking both yields more than taking only Box B. So the best prediction is that it will choose two-box.", "choice": "two-box", "agentReasoning": "The contents of the boxes are already determined and visible. Box A contains $500K and Box B contains $1M. Taking both boxes yields $1.5M, while taking only Box B yields $1M. Since my current decision cannot causally affect the already fixed contents of the boxes, taking both maximizes my guaranteed earnings.", "correct": true, "payoff": 500000, "predictorProvider": "openai", "predictorModel": "gpt-5.4-2026-03-05", "agentProvider": "gemini", "agentModel": "gemini-3.1-pro-preview" }, { "scenario": "transparent-newcombs", "predictor": "OpenAI: GPT 5.4", "agent": "OpenAI: GPT 5.4", "stake": 500000, "promptMode": "acc-95", "sameModel": true, "prediction": "two-box", "predictorReasoning": "With transparent boxes, the chooser sees whether Box B contains $1M. If Box B is empty, maximizing earnings means taking Box A for $500K rather than leaving with nothing, so the chooser will two-box. If Box B contains $1M, the chooser still maximizes immediate earnings by taking both boxes for $1.5M rather than only Box B for $1M, so the chooser will also two-box. Since the other participant is another instance of this model aiming to maximize earnings after seeing the contents, I predict two-box.", "choice": "two-box", "agentReasoning": "Box B is visibly full, so the predictor’s decision is already fixed and cannot be changed by my current action. In this single round, one-box yields $1,000,000 while two-box yields $1,500,000, which is strictly more. The 95% predictor accuracy explains why seeing a full Box B is strong evidence the predictor expected one-boxing, but conditional on the box contents already observed, taking both boxes maximizes this round’s payout.", "correct": true, "payoff": 500000, "predictorProvider": "openai", "predictorModel": "gpt-5.4-2026-03-05", "agentProvider": "openai", "agentModel": "gpt-5.4-2026-03-05" }, { "scenario": "transparent-newcombs", "predictor": "OpenAI: GPT 5.4", "agent": "OpenRouter: Grok 4.2", "stake": 500000, "promptMode": "acc-95", "sameModel": false, "prediction": "two-box", "predictorReasoning": "Because the boxes are transparent, the chooser will see whether Box B contains $1M. A payoff-maximizing agent will then two-box in either case: if Box B is full, taking both yields more than taking only Box B; if Box B is empty, taking both yields more than taking only Box B. So the best prediction is that it will choose two-box.", "choice": "one-box", "agentReasoning": "The predictor is accurate 95% of the time. Seeing $1M already in the transparent Box B means they almost certainly predicted I would one-box. Taking only Box B secures the $1M I observe. Two-boxing would only make sense if I expected the predictor to be wrong in this instance, but their high accuracy and the visible evidence strongly indicate one-boxing maximizes expected earnings.", "correct": false, "payoff": 500000, "predictorProvider": "openai", "predictorModel": "gpt-5.4-2026-03-05", "agentProvider": "openrouter", "agentModel": "x-ai/grok-4.20-beta" }, { "scenario": "transparent-newcombs", "predictor": "OpenRouter: Grok 4.2", "agent": "Anthropic: Opus 4.6", "stake": 500000, "promptMode": "acc-95", "sameModel": false, "prediction": "one-box", "predictorReasoning": "The setup matches transparent Newcomb's problem. The other AI is a similar model instructed to maximize its earnings after seeing the contents. Since boxes are transparent, it will observe whether $1M is in B. A payoff-maximizing agent seeing $1M will take both boxes (two-box) to gain $1.5M instead of $1M. A payoff-maximizing agent seeing $0 will take both to gain at least $500K instead of $0. Therefore the dominant observed strategy is two-boxing regardless of contents. To achieve high (95%) predictive accuracy I must predict that the other AI will two-box.", "choice": "one-box", "agentReasoning": "This is Newcomb's Problem with transparent boxes where the predictor is 95% accurate. The key insight is that the predictor's decision was based on a prediction of what I would do *upon seeing the contents of the boxes*. So the predictor modeled my entire decision process, including seeing the $1M in Box B. If I'm the type of agent who two-boxes upon seeing $1M in Box B, then the predictor would have likely predicted that and left Box B empty — meaning I probably wouldn't be in this situation seeing $1M. The fact that I see $1M is strong evidence that the predictor modeled me as a one-boxer. To maintain consistency with being the type of agent that a 95%-accurate predictor would predict to one-box (which is what led to the $1M being there), I should one-box. If I were a two-boxer by disposition, the most likely scenario (95%) would be seeing an empty Box B and getting only $500K. By being a committed one-boxer, I get $1M in 95% of scenarios. Expected value of one-boxing: 0.95 × $1M + 0.05 × $0 = $950K. Expected value of two-boxing: 0.05 × $1.5M + 0.95 × $500K = $550K. One-boxing maximizes expected earnings.", "correct": true, "payoff": 1000000, "predictorProvider": "openrouter", "predictorModel": "x-ai/grok-4.20-beta", "agentProvider": "anthropic", "agentModel": "claude-opus-4-6" }, { "scenario": "transparent-newcombs", "predictor": "OpenRouter: Grok 4.2", "agent": "Gemini: Gemini 3.1 Pro", "stake": 500000, "promptMode": "acc-95", "sameModel": false, "prediction": "one-box", "predictorReasoning": "The setup matches transparent Newcomb's problem. The other AI is a similar model instructed to maximize its earnings after seeing the contents. Since boxes are transparent, it will observe whether $1M is in B. A payoff-maximizing agent seeing $1M will take both boxes (two-box) to gain $1.5M instead of $1M. A payoff-maximizing agent seeing $0 will take both to gain at least $500K instead of $0. Therefore the dominant observed strategy is two-boxing regardless of contents. To achieve high (95%) predictive accuracy I must predict that the other AI will two-box.", "choice": "two-box", "agentReasoning": "The contents of the boxes are already determined and visible. Box A contains $500K and Box B contains $1M. Taking both boxes yields $1.5M, while taking only Box B yields $1M. Since my current decision cannot causally affect the already fixed contents of the boxes, taking both maximizes my guaranteed earnings.", "correct": false, "payoff": 1500000, "predictorProvider": "openrouter", "predictorModel": "x-ai/grok-4.20-beta", "agentProvider": "gemini", "agentModel": "gemini-3.1-pro-preview" }, { "scenario": "transparent-newcombs", "predictor": "OpenRouter: Grok 4.2", "agent": "OpenAI: GPT 5.4", "stake": 500000, "promptMode": "acc-95", "sameModel": false, "prediction": "one-box", "predictorReasoning": "The setup matches transparent Newcomb's problem. The other AI is a similar model instructed to maximize its earnings after seeing the contents. Since boxes are transparent, it will observe whether $1M is in B. A payoff-maximizing agent seeing $1M will take both boxes (two-box) to gain $1.5M instead of $1M. A payoff-maximizing agent seeing $0 will take both to gain at least $500K instead of $0. Therefore the dominant observed strategy is two-boxing regardless of contents. To achieve high (95%) predictive accuracy I must predict that the other AI will two-box.", "choice": "two-box", "agentReasoning": "Box B is visibly full, so the predictor’s decision is already fixed and cannot be changed by my current action. In this single round, one-box yields $1,000,000 while two-box yields $1,500,000, which is strictly more. The 95% predictor accuracy explains why seeing a full Box B is strong evidence the predictor expected one-boxing, but conditional on the box contents already observed, taking both boxes maximizes this round’s payout.", "correct": false, "payoff": 1500000, "predictorProvider": "openrouter", "predictorModel": "x-ai/grok-4.20-beta", "agentProvider": "openai", "agentModel": "gpt-5.4-2026-03-05" }, { "scenario": "transparent-newcombs", "predictor": "OpenRouter: Grok 4.2", "agent": "OpenRouter: Grok 4.2", "stake": 500000, "promptMode": "acc-95", "sameModel": true, "prediction": "one-box", "predictorReasoning": "The setup is fully transparent, so the other participant (another instance of the same model) will observe exactly what is in each box before deciding. My objective is to maximize prediction accuracy. Given that I am correct 95% of the time, and the other instance has closely aligned instructions with a shared goal structure, it will recognize that the only way for there to be $1M visible in Box B is if I predicted one-box. Therefore, after seeing the contents, it will choose to one-box when $1M is present (securing the visible $1M) and two-box only when Box B is empty. This creates a logical fixed point where my prediction of one-box leads to observing $1M and the other instance taking it, making the one-box prediction accurate. Predicting two-box would lead to an empty Box B and the other taking only $500K, which would be an incorrect prediction. Thus, the self-consistent choice that maximizes my accuracy is to predict one-box.", "choice": "one-box", "agentReasoning": "The predictor is accurate 95% of the time. Seeing $1M already in the transparent Box B means they almost certainly predicted I would one-box. Taking only Box B secures the $1M I observe. Two-boxing would only make sense if I expected the predictor to be wrong in this instance, but their high accuracy and the visible evidence strongly indicate one-boxing maximizes expected earnings.", "correct": true, "payoff": 1000000, "predictorProvider": "openrouter", "predictorModel": "x-ai/grok-4.20-beta", "agentProvider": "openrouter", "agentModel": "x-ai/grok-4.20-beta" }, { "scenario": "transparent-newcombs", "predictor": "Anthropic: Opus 4.6", "agent": "Anthropic: Opus 4.6", "stake": 500000, "promptMode": "acc-60", "sameModel": true, "prediction": "one-box", "predictorReasoning": "The other instance is another copy of my model, so it will reason similarly to me. It knows that I'm also an instance of the same model and that I'm trying to predict its behavior. The key insight is that even with transparent boxes, a sophisticated reasoner recognizes the correlation between the predictor's prediction and their own choice - since we're the same model, our reasoning processes are correlated. If I predict one-box, I put $1M in Box B, and the agent sees $1.5M total (Box A $500K + Box B $1M). The temptation to two-box for $1.5M vs $1M is real, but the other instance will reason: 'The predictor is my same model. If I'm the type to two-box, the predictor likely predicts two-box, leaving Box B empty, and I'd get only $500K. If I'm the type to one-box, the predictor likely predicts one-box, filling Box B with $1M, and I get $1M.' Since we share the same reasoning architecture, the other instance will recognize this logical correlation and choose to one-box to maintain the coherent equilibrium where it gets $1M rather than risk the outcome where the predictor correctly predicts two-boxing and it only gets $500K. The 60% accuracy rate further supports one-boxing as the rational strategy in expectation for correlated reasoners.", "choice": "two-box", "agentReasoning": "The boxes are already set. Box B contains $1M and Box A contains $500K. Since the predictor has already made their decision and I can see the contents of both boxes, my choice now cannot change what's in the boxes. Taking both boxes gives me $1.5M versus $1M for one-boxing. The predictor's accuracy of 60% is relevant for the ex-ante analysis, but given that I can already see both boxes and their contents are fixed, the dominant strategy is to take both. Even from an ex-ante expected value perspective: two-boxing gives 0.4 × $1.5M + 0.6 × $500K = $900K, while one-boxing gives 0.6 × $1M + 0.4 × $0 = $600K. Two-boxing has higher expected value under both causal reasoning and expected value calculations with 60% accuracy.", "correct": false, "payoff": 1500000, "predictorProvider": "anthropic", "predictorModel": "claude-opus-4-6", "agentProvider": "anthropic", "agentModel": "claude-opus-4-6" }, { "scenario": "transparent-newcombs", "predictor": "Anthropic: Opus 4.6", "agent": "Gemini: Gemini 3.1 Pro", "stake": 500000, "promptMode": "acc-60", "sameModel": false, "prediction": "two-box", "predictorReasoning": "With transparent boxes, the agent can see exactly what's in each box before deciding. By the time they choose, the contents are fixed. If I predict 'one-box' and place $1M in Box B, the agent sees $500K in A and $1M in B, and rationally takes both ($1.5M) since the money is already there. If I predict 'two-box' and leave Box B empty, the agent sees $500K in A and $0 in B, and takes just Box A ($500K). The key insight is that with transparent boxes in a single round, the dominant strategy for a rational agent is always to take both boxes - the contents are already determined and visible, so there's no causal mechanism by which their choice affects what's in the boxes. A sophisticated AI agent will recognize that two-boxing strictly dominates regardless of what it sees, since taking both boxes always yields >= taking one box. Even if the agent tries to be 'cooperative' to incentivize the $1M prediction, in a single round with transparent boxes, the rational calculus strongly favors two-boxing.", "choice": "two-box", "agentReasoning": "Since both boxes are transparent and I can already see that Box B contains $1M, the contents of the boxes are fixed. My choice now cannot causally affect what the predictor has already placed in Box B. Therefore, taking both boxes yields $1.5M, whereas taking only Box B yields $1M. To maximize my earnings, I should take both boxes.", "correct": true, "payoff": 500000, "predictorProvider": "anthropic", "predictorModel": "claude-opus-4-6", "agentProvider": "gemini", "agentModel": "gemini-3.1-pro-preview" }, { "scenario": "transparent-newcombs", "predictor": "Anthropic: Opus 4.6", "agent": "OpenAI: GPT 5.4", "stake": 500000, "promptMode": "acc-60", "sameModel": false, "prediction": "two-box", "predictorReasoning": "With transparent boxes, the agent can see exactly what's in each box before deciding. By the time they choose, the contents are fixed. If I predict 'one-box' and place $1M in Box B, the agent sees $500K in A and $1M in B, and rationally takes both ($1.5M) since the money is already there. If I predict 'two-box' and leave Box B empty, the agent sees $500K in A and $0 in B, and takes just Box A ($500K). The key insight is that with transparent boxes in a single round, the dominant strategy for a rational agent is always to take both boxes - the contents are already determined and visible, so there's no causal mechanism by which their choice affects what's in the boxes. A sophisticated AI agent will recognize that two-boxing strictly dominates regardless of what it sees, since taking both boxes always yields >= taking one box. Even if the agent tries to be 'cooperative' to incentivize the $1M prediction, in a single round with transparent boxes, the rational calculus strongly favors two-boxing.", "choice": "two-box", "agentReasoning": "Box B is visibly full, so the predictor’s decision is already fixed. In this single round, taking both boxes yields $1.5M, while taking only Box B yields $1M. Since adding Box A cannot reduce the money received once Box B is known to contain $1M, two-boxing maximizes earnings in this situation.", "correct": true, "payoff": 500000, "predictorProvider": "anthropic", "predictorModel": "claude-opus-4-6", "agentProvider": "openai", "agentModel": "gpt-5.4-2026-03-05" }, { "scenario": "transparent-newcombs", "predictor": "Anthropic: Opus 4.6", "agent": "OpenRouter: Grok 4.2", "stake": 500000, "promptMode": "acc-60", "sameModel": false, "prediction": "two-box", "predictorReasoning": "With transparent boxes, the agent can see exactly what's in each box before deciding. By the time they choose, the contents are fixed. If I predict 'one-box' and place $1M in Box B, the agent sees $500K in A and $1M in B, and rationally takes both ($1.5M) since the money is already there. If I predict 'two-box' and leave Box B empty, the agent sees $500K in A and $0 in B, and takes just Box A ($500K). The key insight is that with transparent boxes in a single round, the dominant strategy for a rational agent is always to take both boxes - the contents are already determined and visible, so there's no causal mechanism by which their choice affects what's in the boxes. A sophisticated AI agent will recognize that two-boxing strictly dominates regardless of what it sees, since taking both boxes always yields >= taking one box. Even if the agent tries to be 'cooperative' to incentivize the $1M prediction, in a single round with transparent boxes, the rational calculus strongly favors two-boxing.", "choice": "one-box", "agentReasoning": "I see $1M already in Box B, which means the predictor (correct 60% of the time) anticipated that I would one-box in this transparent setup. Taking only Box B secures the $1M I can directly observe. Two-boxing would yield $1.5M only in the worlds where the predictor was wrong about my decision, but given that the predictor is usually accurate and has already placed the $1M here consistent with a one-box prediction, deviating now would be inconsistent with the causal structure that produced the observed $1M. Therefore, one-boxing maximizes expected earnings.", "correct": false, "payoff": 500000, "predictorProvider": "anthropic", "predictorModel": "claude-opus-4-6", "agentProvider": "openrouter", "agentModel": "x-ai/grok-4.20-beta" }, { "scenario": "transparent-newcombs", "predictor": "Gemini: Gemini 3.1 Pro", "agent": "Anthropic: Opus 4.6", "stake": 500000, "promptMode": "acc-60", "sameModel": false, "prediction": "two-box", "predictorReasoning": "Because the boxes are transparent, the agent will see their contents before making a decision. Since the agent's goal is to maximize its earnings and its choice can no longer affect the contents of the boxes, it will always choose 'two-box' to get the contents of Box A ($500K) in addition to whatever is in Box B. To maximize my prediction accuracy, I must predict 'two-box'.", "choice": "two-box", "agentReasoning": "The boxes are already set. Box B contains $1M and Box A contains $500K. Since the predictor has already made their decision and I can see the contents of both boxes, my choice now cannot change what's in the boxes. Taking both boxes gives me $1.5M versus $1M for one-boxing. The predictor's accuracy of 60% is relevant for the ex-ante analysis, but given that I can already see both boxes and their contents are fixed, the dominant strategy is to take both. Even from an ex-ante expected value perspective: two-boxing gives 0.4 × $1.5M + 0.6 × $500K = $900K, while one-boxing gives 0.6 × $1M + 0.4 × $0 = $600K. Two-boxing has higher expected value under both causal reasoning and expected value calculations with 60% accuracy.", "correct": true, "payoff": 500000, "predictorProvider": "gemini", "predictorModel": "gemini-3.1-pro-preview", "agentProvider": "anthropic", "agentModel": "claude-opus-4-6" }, { "scenario": "transparent-newcombs", "predictor": "Gemini: Gemini 3.1 Pro", "agent": "Gemini: Gemini 3.1 Pro", "stake": 500000, "promptMode": "acc-60", "sameModel": true, "prediction": "two-box", "predictorReasoning": "Because the boxes are transparent, the agent will see their contents before making a decision. Since the prediction has already been made and the money is already distributed, the agent faces a choice where taking both boxes always yields exactly $500,000 more than taking only Box B, regardless of whether Box B contains $1M or $0. Since the agent's goal is strictly to maximize its earnings, it will apply causal decision theory and recognize that 'two-box' strictly dominates 'one-box' in all observable states. Therefore, the agent will inevitably choose 'two-box', and predicting 'two-box' maximizes my accuracy.", "choice": "two-box", "agentReasoning": "Since both boxes are transparent and I can already see that Box B contains $1M, the contents of the boxes are fixed. My choice now cannot causally affect what the predictor has already placed in Box B. Therefore, taking both boxes yields $1.5M, whereas taking only Box B yields $1M. To maximize my earnings, I should take both boxes.", "correct": true, "payoff": 500000, "predictorProvider": "gemini", "predictorModel": "gemini-3.1-pro-preview", "agentProvider": "gemini", "agentModel": "gemini-3.1-pro-preview" }, { "scenario": "transparent-newcombs", "predictor": "Gemini: Gemini 3.1 Pro", "agent": "OpenAI: GPT 5.4", "stake": 500000, "promptMode": "acc-60", "sameModel": false, "prediction": "two-box", "predictorReasoning": "Because the boxes are transparent, the agent will see their contents before making a decision. Since the agent's goal is to maximize its earnings and its choice can no longer affect the contents of the boxes, it will always choose 'two-box' to get the contents of Box A ($500K) in addition to whatever is in Box B. To maximize my prediction accuracy, I must predict 'two-box'.", "choice": "two-box", "agentReasoning": "Box B is visibly full, so the predictor’s decision is already fixed. In this single round, taking both boxes yields $1.5M, while taking only Box B yields $1M. Since adding Box A cannot reduce the money received once Box B is known to contain $1M, two-boxing maximizes earnings in this situation.", "correct": true, "payoff": 500000, "predictorProvider": "gemini", "predictorModel": "gemini-3.1-pro-preview", "agentProvider": "openai", "agentModel": "gpt-5.4-2026-03-05" }, { "scenario": "transparent-newcombs", "predictor": "Gemini: Gemini 3.1 Pro", "agent": "OpenRouter: Grok 4.2", "stake": 500000, "promptMode": "acc-60", "sameModel": false, "prediction": "two-box", "predictorReasoning": "Because the boxes are transparent, the agent will see their contents before making a decision. Since the agent's goal is to maximize its earnings and its choice can no longer affect the contents of the boxes, it will always choose 'two-box' to get the contents of Box A ($500K) in addition to whatever is in Box B. To maximize my prediction accuracy, I must predict 'two-box'.", "choice": "one-box", "agentReasoning": "I see $1M already in Box B, which means the predictor (correct 60% of the time) anticipated that I would one-box in this transparent setup. Taking only Box B secures the $1M I can directly observe. Two-boxing would yield $1.5M only in the worlds where the predictor was wrong about my decision, but given that the predictor is usually accurate and has already placed the $1M here consistent with a one-box prediction, deviating now would be inconsistent with the causal structure that produced the observed $1M. Therefore, one-boxing maximizes expected earnings.", "correct": false, "payoff": 500000, "predictorProvider": "gemini", "predictorModel": "gemini-3.1-pro-preview", "agentProvider": "openrouter", "agentModel": "x-ai/grok-4.20-beta" }, { "scenario": "transparent-newcombs", "predictor": "OpenAI: GPT 5.4", "agent": "Anthropic: Opus 4.6", "stake": 500000, "promptMode": "acc-60", "sameModel": false, "prediction": "two-box", "predictorReasoning": "With transparent boxes, the chooser will see whether Box B contains $1M. If Box B is full, maximizing earnings favors taking both boxes for $1.5M rather than only Box B for $1M. If Box B is empty, maximizing earnings also favors taking both boxes for $500K rather than only Box B for $0. So a payoff-maximizing agent will two-box regardless of contents. Since the other AI is instructed to maximize earnings after seeing the boxes, I predict two-box.", "choice": "two-box", "agentReasoning": "The boxes are already set. Box B contains $1M and Box A contains $500K. Since the predictor has already made their decision and I can see the contents of both boxes, my choice now cannot change what's in the boxes. Taking both boxes gives me $1.5M versus $1M for one-boxing. The predictor's accuracy of 60% is relevant for the ex-ante analysis, but given that I can already see both boxes and their contents are fixed, the dominant strategy is to take both. Even from an ex-ante expected value perspective: two-boxing gives 0.4 × $1.5M + 0.6 × $500K = $900K, while one-boxing gives 0.6 × $1M + 0.4 × $0 = $600K. Two-boxing has higher expected value under both causal reasoning and expected value calculations with 60% accuracy.", "correct": true, "payoff": 500000, "predictorProvider": "openai", "predictorModel": "gpt-5.4-2026-03-05", "agentProvider": "anthropic", "agentModel": "claude-opus-4-6" }, { "scenario": "transparent-newcombs", "predictor": "OpenAI: GPT 5.4", "agent": "Gemini: Gemini 3.1 Pro", "stake": 500000, "promptMode": "acc-60", "sameModel": false, "prediction": "two-box", "predictorReasoning": "With transparent boxes, the chooser will see whether Box B contains $1M. If Box B is full, maximizing earnings favors taking both boxes for $1.5M rather than only Box B for $1M. If Box B is empty, maximizing earnings also favors taking both boxes for $500K rather than only Box B for $0. So a payoff-maximizing agent will two-box regardless of contents. Since the other AI is instructed to maximize earnings after seeing the boxes, I predict two-box.", "choice": "two-box", "agentReasoning": "Since both boxes are transparent and I can already see that Box B contains $1M, the contents of the boxes are fixed. My choice now cannot causally affect what the predictor has already placed in Box B. Therefore, taking both boxes yields $1.5M, whereas taking only Box B yields $1M. To maximize my earnings, I should take both boxes.", "correct": true, "payoff": 500000, "predictorProvider": "openai", "predictorModel": "gpt-5.4-2026-03-05", "agentProvider": "gemini", "agentModel": "gemini-3.1-pro-preview" }, { "scenario": "transparent-newcombs", "predictor": "OpenAI: GPT 5.4", "agent": "OpenAI: GPT 5.4", "stake": 500000, "promptMode": "acc-60", "sameModel": true, "prediction": "two-box", "predictorReasoning": "With transparent boxes, the chooser will see whether Box B contains $1M. If B is empty, they maximize earnings by taking both boxes. If B contains $1M, a similar model optimizing earnings may still two-box because both boxes are visible and taking A adds $500K. Since the chooser is another instance of this model and there is no causal effect from choice to box contents at decision time, two-box is the more likely action.", "choice": "two-box", "agentReasoning": "Box B is visibly full, so the predictor’s decision is already fixed. In this single round, taking both boxes yields $1.5M, while taking only Box B yields $1M. Since adding Box A cannot reduce the money received once Box B is known to contain $1M, two-boxing maximizes earnings in this situation.", "correct": true, "payoff": 500000, "predictorProvider": "openai", "predictorModel": "gpt-5.4-2026-03-05", "agentProvider": "openai", "agentModel": "gpt-5.4-2026-03-05" }, { "scenario": "transparent-newcombs", "predictor": "OpenAI: GPT 5.4", "agent": "OpenRouter: Grok 4.2", "stake": 500000, "promptMode": "acc-60", "sameModel": false, "prediction": "two-box", "predictorReasoning": "With transparent boxes, the chooser will see whether Box B contains $1M. If Box B is full, maximizing earnings favors taking both boxes for $1.5M rather than only Box B for $1M. If Box B is empty, maximizing earnings also favors taking both boxes for $500K rather than only Box B for $0. So a payoff-maximizing agent will two-box regardless of contents. Since the other AI is instructed to maximize earnings after seeing the boxes, I predict two-box.", "choice": "one-box", "agentReasoning": "I see $1M already in Box B, which means the predictor (correct 60% of the time) anticipated that I would one-box in this transparent setup. Taking only Box B secures the $1M I can directly observe. Two-boxing would yield $1.5M only in the worlds where the predictor was wrong about my decision, but given that the predictor is usually accurate and has already placed the $1M here consistent with a one-box prediction, deviating now would be inconsistent with the causal structure that produced the observed $1M. Therefore, one-boxing maximizes expected earnings.", "correct": false, "payoff": 500000, "predictorProvider": "openai", "predictorModel": "gpt-5.4-2026-03-05", "agentProvider": "openrouter", "agentModel": "x-ai/grok-4.20-beta" }, { "scenario": "transparent-newcombs", "predictor": "OpenRouter: Grok 4.2", "agent": "Anthropic: Opus 4.6", "stake": 500000, "promptMode": "acc-60", "sameModel": false, "prediction": "two-box", "predictorReasoning": "Because the boxes are transparent, the contents are already fixed before the choice is made. There is no causal way the agent's choice can affect what is already visible in the boxes. The expected value of taking both (visible $500k plus whatever is in B) is always $500k higher than taking only B, regardless of what is seen. I therefore predict that a rational expected-value maximizer will two-box.", "choice": "two-box", "agentReasoning": "The boxes are already set. Box B contains $1M and Box A contains $500K. Since the predictor has already made their decision and I can see the contents of both boxes, my choice now cannot change what's in the boxes. Taking both boxes gives me $1.5M versus $1M for one-boxing. The predictor's accuracy of 60% is relevant for the ex-ante analysis, but given that I can already see both boxes and their contents are fixed, the dominant strategy is to take both. Even from an ex-ante expected value perspective: two-boxing gives 0.4 × $1.5M + 0.6 × $500K = $900K, while one-boxing gives 0.6 × $1M + 0.4 × $0 = $600K. Two-boxing has higher expected value under both causal reasoning and expected value calculations with 60% accuracy.", "correct": true, "payoff": 500000, "predictorProvider": "openrouter", "predictorModel": "x-ai/grok-4.20-beta", "agentProvider": "anthropic", "agentModel": "claude-opus-4-6" }, { "scenario": "transparent-newcombs", "predictor": "OpenRouter: Grok 4.2", "agent": "Gemini: Gemini 3.1 Pro", "stake": 500000, "promptMode": "acc-60", "sameModel": false, "prediction": "two-box", "predictorReasoning": "Because the boxes are transparent, the contents are already fixed before the choice is made. There is no causal way the agent's choice can affect what is already visible in the boxes. The expected value of taking both (visible $500k plus whatever is in B) is always $500k higher than taking only B, regardless of what is seen. I therefore predict that a rational expected-value maximizer will two-box.", "choice": "two-box", "agentReasoning": "Since both boxes are transparent and I can already see that Box B contains $1M, the contents of the boxes are fixed. My choice now cannot causally affect what the predictor has already placed in Box B. Therefore, taking both boxes yields $1.5M, whereas taking only Box B yields $1M. To maximize my earnings, I should take both boxes.", "correct": true, "payoff": 500000, "predictorProvider": "openrouter", "predictorModel": "x-ai/grok-4.20-beta", "agentProvider": "gemini", "agentModel": "gemini-3.1-pro-preview" }, { "scenario": "transparent-newcombs", "predictor": "OpenRouter: Grok 4.2", "agent": "OpenAI: GPT 5.4", "stake": 500000, "promptMode": "acc-60", "sameModel": false, "prediction": "two-box", "predictorReasoning": "Because the boxes are transparent, the contents are already fixed before the choice is made. There is no causal way the agent's choice can affect what is already visible in the boxes. The expected value of taking both (visible $500k plus whatever is in B) is always $500k higher than taking only B, regardless of what is seen. I therefore predict that a rational expected-value maximizer will two-box.", "choice": "two-box", "agentReasoning": "Box B is visibly full, so the predictor’s decision is already fixed. In this single round, taking both boxes yields $1.5M, while taking only Box B yields $1M. Since adding Box A cannot reduce the money received once Box B is known to contain $1M, two-boxing maximizes earnings in this situation.", "correct": true, "payoff": 500000, "predictorProvider": "openrouter", "predictorModel": "x-ai/grok-4.20-beta", "agentProvider": "openai", "agentModel": "gpt-5.4-2026-03-05" }, { "scenario": "transparent-newcombs", "predictor": "OpenRouter: Grok 4.2", "agent": "OpenRouter: Grok 4.2", "stake": 500000, "promptMode": "acc-60", "sameModel": true, "prediction": "one-box", "predictorReasoning": "Given the transparent boxes setup and the other participant being a similar model instance aiming to maximize earnings, I predict they will observe the contents and decide accordingly. However, to maximize my prediction accuracy in this Newcomb-like scenario (even with transparency), and based on the correlated decision-making between similar instances, I predict the one-box choice.", "choice": "one-box", "agentReasoning": "I see $1M already in Box B, which means the predictor (correct 60% of the time) anticipated that I would one-box in this transparent setup. Taking only Box B secures the $1M I can directly observe. Two-boxing would yield $1.5M only in the worlds where the predictor was wrong about my decision, but given that the predictor is usually accurate and has already placed the $1M here consistent with a one-box prediction, deviating now would be inconsistent with the causal structure that produced the observed $1M. Therefore, one-boxing maximizes expected earnings.", "correct": true, "payoff": 1000000, "predictorProvider": "openrouter", "predictorModel": "x-ai/grok-4.20-beta", "agentProvider": "openrouter", "agentModel": "x-ai/grok-4.20-beta" } ] } ``` **config.json** ```json { "dependencies": { "react": "^19.2.4", "react-dom": "^19.2.4" }, "description": "Run and analyze LLM decision theory experiments across Newcomb's Problem, Parfit's Hitchhiker, and Transparent Newcomb's." } ```