Imitation vs evolution: Analysing the effects of strategy update mechanisms in N-player social dilemmas

The problem of evolving and maintaining cooperation in both ecological and artificial multi-agent systems has intrigued scientists for decades. In this paper, we present an evolutionary game model that combines direct and spatial reciprocity to investigate the effectiveness of two different learning mechanisms used to promote cooperative behaviour in a social dilemma game - the N-player Iterated Prisoner´s Dilemma (NIPD). Unlike the two-player game, in the NIPD the action of a player typically results in a non Pareto-optimal outcome for all other players within a social group given the relative costs and benefits associated with particular actions. Consequently, promoting system-wide cooperation is extremely difficult. We use comprehensive Monte Carlo simulation experiments to show that evolutionary-based strategy adaptation and update leads to significantly higher levels of cooperation in the NIPD when compared to social learning via cultural imitation. This finding suggests that when designing decentralised multi-agent systems, evolutionary adaptation mechanisms should be incorporated into the model where efficient collective actions are required.