The Evolution of Probabilistic Reciprocity in a Multi-agent Environment with Neighborhoods

Abstract

Previous work by other researchers has investigated the evolution and stability of a ‘probabilistic reciprocity’ strategy in a package delivery domain where all agents could communicate reputations to every other agent. We extend that work to the more realistic situation of spatially distributed agents with neighborhoods that restrict agent interaction and communication. We improve the original probabilistic reciprocity strategy with a modification that tarnishes the reputations of agents that repeatedly refuse requests for help. We then investigate the effect of reducing neighborhood size from the general case of the ‘global neighborhood’ used in previous work. Our experiments show that neighborhoods can be reduced to a critical size without a significant degradation in the evolutionary stability of the improved probabilistic reciprocity strategy. We also show that locating like agents within a niche can mitigate this degradation. From a multi-agent design perspective, this means that for a population with a given proportion of selfish and dishonest agents, communication may be reduced to within a subset of the population while retaining the same success of the reciprocative strategy. We also show how to extend the problem domain to abstract a wider range of interaction situations as defined in the literature.