Memory Retrieval and Harshness of Conflict in the Hawk-Dove Game

Joint with:  Ennio Bilancini, Sebastian Ille and Eugenio Vicario.

Link to working paper: https://drive.google.com/file/d/1mp6NNyWBPDNVV-PHBfKN1jRa_M9HqtLJ/view?usp=sharing.

Abstract: We study the long-run dynamics of a repeated non-symmetric Haw-Dove type interaction between agents of two different populations. Agents choose a strategy based on their previous experience with the other population by sampling from a collective memory of past interactions. We assume that the sample size differs between populations and we define a measure of the harshness of conflict in the Hawk-Dove interaction. We then show how the properties of the long-run equilibrium depend on  the harshness of conflict and the relative length of the sample. In symmetric interactions, if conflict is harsh, the population which samples relatively more past interactions is able to appropriate a higher payoff in the long-run, while the population with a relatively smaller sample do so if conflict is mild. These results hold subject to constraints on the sample size which we discuss in detail. We further extend our results to non-symmetric Hawk-Dove games.

To view the seminar recording, click here.