The effects of payoff preferences on agent tolerance

An objective of multi-agent systems is to build robust intelligent systems capable of existing in complex environments. These environments are often open, noisy and subject to rapid, unpredictable changes. This paper will explore how agents can bias their interactions and choices in these complex environments. Existing research has investigated how agents can bias their interactions based on factors such as similarity, trust or reputation. Unfortunately, much of this research has ignored how agents are influenced by their preferences for certain game payoffs. This paper will show that individual payoff preferences have a significant effect on the behaviors that emerge within an agent environment. We argue that agents must not only determine with whom to interact, but also the levels of benefit or risk these interactions should represent. This paper presents a series of game theoretic simulations examining the effects of agent payoff preferences within an evolutionary setting. Our experiments show that these factors promote tolerance throughout the population. We provide an experimental benchmark using an almost identical game environment where payoffs are not considered by agents. Furthermore, we also present simulations involving noise, thereby demonstrating the ability of these more tolerant agents to cope with uncertainty in their environment.