Abstract
We define Bayesian games with intentions by introducing a distinction between “intended” and “actual” actions, generalizing both Bayesian games and (static) psychological gamesGeanakoplos et al. (1989). We propose a new solution concept for this framework and prove that Nash equilibria in static psychological games correspond to a special class of equilibria as defined in our setting. We also show how the actual/intended divide can be used to implement the distinction between “real” outcomes and “reference” outcomes so crucial to prospect theory, and how some of the core insights of prospect theory can thereby be captured using Bayesian games with intentions.
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