Inequity Aversion

Inequity aversion, the dislike of unequal outcomes, is a significant factor influencing cooperation and efficiency in multi-agent systems, mirroring similar human behaviors. Current research focuses on incorporating inequity aversion models into reinforcement learning algorithms, particularly in traffic control and resource allocation problems, to improve overall system performance by promoting fairer distributions of rewards or resources. These studies utilize various reward reshaping techniques, often considering both advantageous and disadvantageous inequities, to incentivize cooperation and reduce disparities. The findings demonstrate that strategically designed inequity aversion mechanisms can significantly enhance the efficiency and fairness of multi-agent systems, with implications for both artificial intelligence and the understanding of human social dynamics.