Modeling recursive reasoning by humans using empirically informed interactive POMDPs

Abstract

Recursive reasoning of the form what do I think that you think that I think (and so on) arises often while acting rationally in multiagent settings. Several multiagent decision-making frameworks such as RMM, I-POMDP and the theory of mind model recursive reasoning as integral to an agent's rational choice. Real-world application settings for multiagent decision making are often mixed involving humans and human-controlled agents. In two large experiments, we studied the level of recursive reasoning generally displayed by humans while playing sequential general-sum and fied-sum, two-player games. Our results show that subjects experiencing a general-sum strategic game display firs or second level of recursive thinking with the firs level being more prominent. However, if the game is made simpler and more competitive with fi edsum payoffs, subjects predominantly attributed first-1 vel recursive thinking to opponents thereby acting using second level of reasoning. Subsequently, we model the behavioral data obtained from the studies using the I-POMDP framework, appropriately augmented using well-known human judgment and decision models. Accuracy of the predictions by our models suggest that these could be viable ways for computationally modeling strategic behavioral data. Copyright © 2010, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.