Factors in Learning Dynamics Influencing Relative Strengths of Strategies in Poker Simulation

Aaron Foote (), Maryam Gooyabadi and Nikhil Addleman
Additional contact information
Aaron Foote: Hazel Quantitative Analysis Center, Wesleyan University, Middletown, NJ 06459, USA
Maryam Gooyabadi: Hazel Quantitative Analysis Center, Wesleyan University, Middletown, NJ 06459, USA
Nikhil Addleman: Independent Researcher, Middletown, CT 06457, USA

Games, 2023, vol. 14, issue 6, 1-16

Abstract: Poker is a game of skill, much like chess or go, but distinct as an incomplete information game. Substantial work has been done to understand human play in poker, as well as the optimal strategies in poker. Evolutionary game theory provides another avenue to study poker by considering overarching strategies, namely rational and random play. In this work, a population of poker playing agents is instantiated to play the preflop portion of Texas Hold’em poker, with learning and strategy revision occurring over the course of the simulation. This paper aims to investigate the influence of learning dynamics on dominant strategies in poker, an area that has yet to be investigated. Our findings show that rational play emerges as the dominant strategy when loss aversion is included in the learning model, not when winning and magnitude of win are of the only considerations. The implications of our findings extend to the modeling of sub-optimal human poker play and the development of optimal poker agents.

Keywords: poker; evolutionary game theory; reinforcement learning (search for similar items in EconPapers)
JEL-codes: C C7 C70 C71 C72 C73 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2073-4336/14/6/73/pdf (application/pdf)
https://www.mdpi.com/2073-4336/14/6/73/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jgames:v:14:y:2023:i:6:p:73-:d:1290608

Access Statistics for this article

Games is currently edited by Ms. Susie Huang

More articles in Games from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().