A decision network account of reasoning about other people's choices.

By Alan Jern, Charles Kemp. 2015

29 citations recorded by the source corpus.

The ability to predict and reason about other people's choices is fundamental to social interaction. We propose that people reason about other people's choices using mental models that are similar to decision networks. Decision networks are extensions of Bayesian networks that incorporate the idea that choices are made in order to achieve goals. In our first experiment, we explore how people predict the choices of others. Our remaining three experiments explore how people infer the goals and knowledge of others by observing the choices that they make. We show that decision networks account for our data better than alternative computational accounts that do not incorporate the notion of goal-directed choice or that do not rely on probabilistic inference.

Subfields: Artificial Intelligence, Developmental and Educational Psychology

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Citations in the corpus

Rational quantitative attribution of beliefs, desires and percepts in human mentalizing (2017, background): “Other model-based frameworks have considered inference of knowledge about world states and causal structure 15,22–24 , inference of beliefs based on unobserved events 25 , or joint inference of knowledge and intentions in the context of epistemic trust and coordination 26,27 .”