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The belief-based learning models are a class of models that intend to describe the way agents (such as humans) learn over time in strategic situations (games) that they play repeatedly. According to a belief-based learning model, agents form beliefs about the expected behavior of others, and choose actions based on how they would perform against opponents playing in this expected way. After playing the game, they update their beliefs based on the feedback they receive.
An important aspect of belief-based learning is that agents understand they are playing a strategic game, allowing them to have a mental model about the way opponents play. This is in contrast to reinforcement-based learning models, where agents choose and update based only on the payoffs received in the game, so that it is not necessary to have a mental model of opponents’ play (in fact, under reinforcement learning, agents need not realize that they are playing a game at all)....
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Feltovich, N. (2012). Belief-Based Learning Models. In: Seel, N.M. (eds) Encyclopedia of the Sciences of Learning. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1428-6_111
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