Modeling Recursive Reasoning by Humans Using Empirically Informed Interactive POMDPs

Recursive reasoning of the form {\em 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 fixed-sum, two-player games. Our results show that subjects experiencing a general-sum strategic game display first or second level of recursive thinking with the first level being more prominent. However, if the game is made simpler and more competitive with fixed-sum payoffs, subjects predominantly attributed first-level 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.

Authors

• Prashant Doshi
• Xia Qu
• Adam Goodie
• Diana Young

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