Monetary Payoff and Utility Function in Adaptive Learning Models

When players repeatedly face an identical or similar game (e.g., coordination game, technology adoption game, or product choice game), they may learn through experience to perform better in the future. This learning behaviour has important economic implications. It determines which economic outcome a game will reach and how fast it will get there.

Given the importance of players’ learning behaviours, economists have proposed various adaptive models to study them. These models are usually estimated and tested using experimental data. Moreover, economists usually assume that individuals’ preference—their utility—is equal to the monetary reward they obtain. However, such an assumption can be wrong since players are not necessarily risk neutral. They could be risk averse or risk loving.

I study the consequences of this false assumption and propose a method to deal with it. I then apply the method to an existing experimental dataset. The estimation results show that utility does not necessarily equal monetary reward. Imposing such a false assumption leads researchers to draw incorrect conclusions about players’ learning behaviours. For instance, we may incorrectly estimate the speed of learning and wrongly predict the final outcome of a game. In contrast, the method I propose in this paper allows researchers to achieve more accurate estimates.