On the Emergence of Fairness in the Evolutionary Dictator Game with Edge Weight Learning

The Ultimatum Game (UG) is an abstraction of common human interactions. In the UG, two players split a resource by proposing and responding to deals. The Dictator Game is a simplified and constrained form of the UG where deals may not be rejected. In this paper, in an evolutionary spatial Dictator Game, a form of Edge Weight Learning that allows players to react to the strategies of their neighbours is proposed. Edge weights directly influence the likelihood of a recipient to decide to not interact with a dictator neighbour, punishing exploitative, greedier neighbours and rewarding cooperative, fairer neighbours. Through experimentation with environmental parameters such as the rate of edge weight modification and the rate of evolutionary phases, it is shown that this process inspired by edge weight learning may serve as a catalyst for the emergence of fairness in the evolutionary Dictator Game.