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Estimating the use of theory of mind using agent-based modeling

Diepgrond, D. (2015) Estimating the use of theory of mind using agent-based modeling. Bachelor's Thesis, Artificial Intelligence.

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Decisions made in a social context can lead people to attribute mental states to others. For example assuming an approaching road user to hit the brakes so you can drive through. This phenomenon is called theory of mind (ToM). Engaging in social interactions can lead to recursive thinking of the sort “I think that you think that I think”. The depth of this recursion reflects the ToM-sophistication of a person. In this study we will use agents based on reinforcement learning to estimate the ToM-behavior of Bayesian agents and human participants in the zero-sum game of hide and seek. Our method will be tested on data of hide and seek games between participants and Bayesian agents. Results prove our estimation method to be effective on the Bayesian agents. Their ToM-behavior could be modeled accurately by our estimation method. The behavior of human participants was best modeled by zero- and second-order ToM. They showed a striking lack of first-order ToM-use and limited evidence of opponent modeling. The inability to use first-order ToM could indicate the use of a shortcut in the reasoning process of people in simple social interactions that skips first-order theory ToM.

Item Type: Thesis (Bachelor's Thesis)
Degree programme: Artificial Intelligence
Thesis type: Bachelor's Thesis
Language: English
Date Deposited: 15 Feb 2018 08:05
Last Modified: 15 Feb 2018 08:05

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