Cooperation and Fairness in Systems of Indirect Reciprocity

Across disciplines, cooperation is a fundamental research topic. While socially desirable to a population, it often bears a cost to the individual who, in their own self-interest, rationally chooses not to engage in costly cooperation. As such, much work has been done in understanding the biological mechanisms behind cooperation in human and animal populations. In my PhD project, I develop and apply these mechanisms both to artificial multi-agent systems and real social systems. I examine how factors such as agent heterogeneity and different learning algorithms affect not only the level of cooperation within a system, but also the level of fairness in the distribution of payoffs. In previous work, I showed how the effectiveness of the social norm-based mechanism of indirect reciprocity is affected when in-group biased cooperation is present. Beyond my future work on online platforms, I also plan to explore the effects of space, gossip, and partial and subjective observations to widen the potential scope of applications.