Modeling recursive reasoning by humans using empirically informed interactive POMDPs

  • Authors:

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

  • Affiliations:

  • University of Georgia, Athens, GA;University of Georgia, Athens, GA;University of Georgia, Athens, GA;University of Georgia, Athens, GA

  • Venue:
  • Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
  • Year:
  • 2010

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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 filed-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 filed-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.