Summary
We apply Camerer and Ho’s experience-weighted attraction (EWA) model of learning to extensive-form signaling games. Since these games often have many equilibria, logical ‘refinements’ have been used to predict which equilibrium will occur. Brandts and Holt conjectured that belief formation could lead to less refined equilibria, and confirmed their conjecture experimentally. Our adaptation of EWA to signaling games includes a formalization of the BrandtsHolt belief formation idea as a special case. We find that the Brandts-Holt dynamic captures the direction of switching from one strategy to another, but does not capture the rate at which switching occurs. EWA does better at predicting the rate of switching (and also forecasts better than reinforcement models). Extensions of EWA which update unchosen signals by different functions of the set of unobserved foregone payoffs further improve predictive accuracy.
This research was supported by NSF SBR 9511001. Thanks to Jordi Brandts and Charlie Holt for supplying their raw data. Helpful comments were received from audiences at the Universities of California (Berkeley) and Texas (Austin), Ohio State University, the Fall 1998 ESA Meetings, and from guest editor Charles Noussir and an anonymous referee.
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Anderson, C.M., Camerer, C.F. (2001). Experience-weighted attraction learning in sender-receiver signaling games. In: Cason, T., Noussair, C. (eds) Advances in Experimental Markets. Studies in Economic Theory, vol 15. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-56448-2_11
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