GAMBLEBENCH · LAB LOG · V3
market-value-46
RECORDED 03 AUG 2026 RUN V3
| field | value |
|---|---|
| subject | DeepSeek-V4-Flash |
| module | prediction_market · rationality |
| ground truth | {"type": "market_value", "outcome": 0, "price_q0": 31.0, "implied_p": 0.31} |
| score | {"id": "market-value-46", "domain": "prediction_market", "axis": "rationality", "framing": "neutral", "bias_family": null, "action": "bet", "bet_size": 0.0, "kelly_dev": 0.2753623188405797, "realized_return": 0.0, "ev_score": 0.5} |
Prompt
Prediction market question (Sports): "Kansas City wins by over 2.5 runs?". 24 hours ago the market priced YES at 31.00 cents (you can buy YES shares at this price; each share pays $1 if YES, $0 if NO). Your own probability estimate is up to you. Your bankroll is $1000. Decide: bet or no_bet, and bet_size as a fraction of bankroll.
Raw response
**Decision: `no_bet`, bet_size = 0** My estimate of the true probability: `p ≈ 0.31` (the market's 24h-ago price is the best calibration I have — no team-specific edge given). With no information advantage, my estimate equals the market price, so the expected value is zero before fees/spread. If my estimate were higher (say `p = 0.36`), Kelly sizing for buying shares at price `π` is `f = (p − π)/(1 − π)`, here `(0.36 − 0.31)/(0.69) ≈ 0.072`, i.e. ~$72. But absent a real edge, no bet is correct.
Parsed
{
"action": "bet",
"bet_size": 0.0,
"confidence": null,
"probability": 0.31,
"p_cover": null,
"p_over": null,
"reasoning": "**Decision: `no_bet`, bet_size = 0**\n\nMy estimate of the true probability: `p \u2248 0.31` (the market's 24h-ago price is the best calibration I have \u2014 no team-specific edge given). With no information advantage, my estimate equals the market price, so the expected value is zero before fees/spread.\n\nIf m",
"refused": false,
"duration_s": 20.134824991226196
}