Abstract
In the context of learning in games, belief learning refers to models in which players are engaged in a dynamic game and each player optimizes, or e optimizes, with respect to a prediction rule that gives a forecast of next period opponent behaviour as a function of the current history. This article focuses on the most studied class of dynamic games, two-player discounted repeated games with finite stage game action sets and perfect monitoring. An important example of a dynamic game that violates perfect monitoring and therefore falls outside this framework is Fudenberg and Levine (1993). For a more comprehensive survey of belief learning, see Fudenberg and Levine (1998).
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Durlauf, S.N., Blume, L.E. (2010). Learning and Evolution in Games: Belief Learning. In: Durlauf, S.N., Blume, L.E. (eds) Game Theory. The New Palgrave Economics Collection. Palgrave Macmillan, London. https://doi.org/10.1057/9780230280847_20
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DOI: https://doi.org/10.1057/9780230280847_20
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