Predicting human behavior in unrepeated, simultaneous-move games

By James R. Wright, Kevin Leyton-Brown. 2013

87 citations recorded by the source corpus.

Abstract It is commonly assumed that agents will adopt Nash equilibrium strategies; however, experimental studies have demonstrated that this is often a poor description of human players' behavior in unrepeated normal-form games. We analyze five widely studied models of human behavior: Quantal Response Equilibrium, Level- k , Cognitive Hierarchy, QLk, and Noisy Introspection. We performed what we believe is the most comprehensive meta-analysis of these models, leveraging ten datasets from the literature recording human play of two-player games. We first evaluated predictive performance, asking how well each model fits unseen test data using parameters calibrated from separate training data . The QLk model ( Stahl and Wilson, 1994 ) consistently achieved the best performance. Using a Bayesian analysis, we found that QLk's estimated parameter values were not consistent with their intended economic interpretations. Finally, we evaluated model variants similar to QLk, identifying one ( Camerer et al., 2016 ) that achieves better predictive performance with fewer parameters.

Subfields: General Decision Sciences, Safety Research, Sociology and Political Science

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In General Decision Sciences

1. Neil Burch
2. David Silver
3. Marc Lanctot
4. Michael H. Bowling
5. Marc G. Bellemare
6. Tuomas Sandholm

In Safety Research

1. Jakob N. Foerster
2. David Silver
3. Thomas Lukasiewicz
4. Karl Tuyls

In Sociology and Political Science

1. Marc Lanctot
2. Michael H. Bowling
3. David Silver
4. Karl Tuyls
5. Julien Pérolat
6. Neil Burch
7. Marc G. Bellemare
8. Jakob N. Foerster

Citations in the corpus

• The Hanabi Challenge: A New Frontier for AI Research (2019, background): “significant financial support for this work that could have influenced its outcome.”
• A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning (2017, background): “In behavioral game theory [104, 83], the focus is to predict actions taken by humans, and the responses are intentionally constrained to increase predictive ability.”
• Arena: A General Evaluation Platform and Building Toolkit for Multi-Agent Intelligence (2019, background): “Other less commonly adopted ideas include predicting the opponent’s behavior [100, 101] and giving the agents information on their opponents [24, 31].”