A dynamic graph model of strategy learning for predicting human behavior in repeated games

DOI10.1515/bejte-2021-0015OpenAlexW4281843906MaRDI QIDQ2690389

Yanyan Li

Publication date: 16 March 2023

Published in: The B. E. Journal of Theoretical Economics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1515/bejte-2021-0015

zbMATH Keywords

repeated gamesbounded rationalitybehavioral game theorylearning modelsstrategy predictiongraph-based algorithms

Mathematics Subject Classification ID

Games involving graphs (91A43) Rationality and learning in game theory (91A26) Multistage and repeated games (91A20)

Cites Work

• Unnamed Item
• Learning, teaching, and turn taking in the repeated assignment game
• A generalized approach to belief learning in repeated games
• Sophisticated experience-weighted attraction learning and strategic teaching in repeated games
• On the persistence of strategic sophistication
• Learning and decision costs in experimental constant sum games
• Quantal response equilibria for normal form games
• A cooperative dual to the Nash equilibrium for two-person prescriptive games
• A unifying learning framework for building artificial game-playing agents
• Self-tuning experience weighted attraction learning in games
• A Simple Adaptive Procedure Leading to Correlated Equilibrium
• A Cognitive Hierarchy Model of Games
• Experience-weighted Attraction Learning in Normal Form Games

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