Resolving Social Dilemmas with Minimal Reward Transfer - Extended Abstract

Richard Willis (King's College London), Yali Du (King's College London), Joel Z. Leibo (Google DeepMind), Michael Luck (University of Sussex)

Abstract

In this paper we introduce a novel metric, the general self-interest level, to quantify the disparity between individual and group rationality in social dilemma games. This metric represents the maximum proportion of their individual rewards that agents can retain while guaranteeing that a social welfare optimum is achieved. This work provides both a tool for describing social dilemmas and a prescriptive solution for resolving them via reward transfer contracts. In contrast to existing metrics, the general self-interest level can enable more efficient solutions to be found. Applications include mechanism design, where we can assess the impact on collective behaviour of modifications to models of environments.