Behavioral game theoretic models: a Bayesian framework for parameter analysis.
Full Text: Wright12-Behavorial.pdf
Studies in experimental economics have consistently demonstrated that Nash equilibrium is a poor description of human players’ behavior in unrepeated normal-form games. Behavioral game theory offers alternative models that more accurately describe human behavior in these settings. These models typically depend upon the values of exogenous parameters, which are estimated based on experimental data. We describe methods for deriving and analyzing the posterior distributions over the parameters of such models, and apply these techniques to study two popular models (Poisson-CH and QLk), the latter of which was previously shown to be the best-performing existing model in a comparison of four widely-studied behavioral models [22]. Drawing on a large set of publicly available experimental data, we derive concrete recommendations for the parameters that should be used with Poisson-CH, contradicting previous recommendations in the literature. We also uncover anomalies in QLk that lead us to develop a new, simpler, and better performing model.
Citation
J. Wright, K. Leyton-Brown. "Behavioral game theoretic models: a Bayesian framework for parameter analysis.". Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), pp 921-930, June 2012.| Keywords: | Behavioral game theory, Bounded rationality, Game theory, Cognitive models |
| Category: | In Conference |
| Web Links: | AAMAS |
BibTeX
@incollection{Wright+Leyton-Brown:AAMAS12,
author = {James R. Wright and Kevin Leyton-Brown},
title = {Behavioral game theoretic models: a Bayesian framework for parameter
analysis.},
Pages = {921-930},
booktitle = {Joint Conference on Autonomous Agents and Multi-Agent Systems
(AAMAS)},
year = 2012,
}Last Updated: March 03, 2020Submitted by Sabina P
