Bluffing and betting behavior in a simplified poker game
Bibliographic Data
| ID | 12166311 |
|---|---|
| Authors | Darryl A Seale (0000-0003-0037-7839, University of Nevada, Las Vegas, corresponding author), Steven E Phelan (0000-0003-3897-3273, University of Nevada, Las Vegas) |
| Year | 2009 |
| Volume | 23 |
| Issue | 4 |
| Pages | 335-352 |
| Publication date | 2009-07-08 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Behavioral Decision Making (JOURNAL) |
| Journal identifiers | ISSN: 0894-3257 • E-ISSN: 1099-0771 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1002/bdm.658 |
| OpenAlex | W2052103201 |
| Language | EN |
| Citations received | 2 |
| References cited | 23 |
A pure‐strategy, simplified poker (PSP) game is proposed, where two players draw from a small and discrete number of hands. Equilibrium strategies of the game are described and an experiment is conducted where 120 subjects played the PSP against a computer, which was programmed to play either the equilibrium solution or a fictitious play (FP) learning algorithm designed to take advantage of poor play. The results show that players did not adopt the cutoff‐type strategies predicted by the equilibrium solution; rather they made considerable “errors” by: Betting when they should have checked, checking when they should have bet, and calling when they should have folded. There is no evidence that aggregate performance improved over time in either condition although considerable individual differences were observed among subjects. Behavioral learning theory (BLT) cannot easily explain these individual differences and cognitive learning theory (CLT) is introduced to explain the apparent anomalies. Copyright © 2009 John Wiley & Sons, Ltd
Aggregate (composite · Aggregate behavior · Aggregate demand · Econometrics · Economics · Fictitious play · Game theory · Mathematical economics · Computer Science · Decision-Making and Behavioral Economics · Experimental Behavioral Economics Studies · Game Theory and Applications · Psychology · Artificial Intelligence
| Unique citing works | 2 |
|---|---|
| Citations per year | 0,17 |
| Citation span | 2014 - 2023 (10) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 2 |