Ali Baba and the Thief, convention emergence in games

Abstract

In this paper we propose a model that supports the emergence of conventions via multi-agent learning in social networks. In our model, individual agents repeatedly interact with their neighbours in a game called Ali Baba and the Thief. An agent learns its strategy to play the game using the learning rule imitate-the-best. We show that some conventions prescribing peaceful behaviours can emerge after repeated interactions among agents inhabited in some social networks. Our experiments suggest that there are critical points of convention emergence in Ali Baba and the Thief. When the quotient of the amount of robbery and the initial utility is smaller than the critical point, the probability of convention emergence is high. The probability drops dramatically as long as the quotient is larger than the critical point.