A computational model of a norm's yield

Abstract

While several works have been conducted on norms detection and identification, almost non-existent work has explored the notion of norm's yield to trigger agents to adopt new norms. In other words, in most of the literature on norms, agents are not aware of the 'yield' or gain from the enacted norms. Instead, they use some mining algorithms to identify the norms from a set of observed events and subsequently adopting the norms without further analysis. Consequently, it would be interesting to look into agents that can deal with norm's yield. In this paper, we propose a computational model of a norm's yield. When an agent could measure a norm's yield, it would have greater reasoning ability about the norm's effects on its performance, which in turn trigger the agent to adopt or reject the norm.