A Comparative Analysis of Fairness and Satisfaction in Multi-Agent Resource Allocation: Integrating Borda Count and K-Means Approaches with Distributive Justice Principles

This study introduces a novel framework for fair resource allocation in self-governing, multi-agent systems, leveraging principles of interactional justice to enable agents to autonomously evaluate fairness in both individual and collective resource distribution. Central to our approach is the integration of Rescher’s canons of distributive justice, which provide a comprehensive, multidimensional framework encompassing equality, need, effort and productivity to assess legitimate claims on resources. In resource-constrained environments, multiagent systems require a balance between fairness and satisfaction. We compare the Borda Count (BC) method with K-means clustering, which group agents by similarity and allocate resources based on cluster averages. According to our findings, the BC method effectively prioritized the highest needs of the agents and resulted in higher satisfaction. On the other hand, K-means achieved higher fairness and facilitated a more equitable distribution of resources. The study showed that there was an intrinsic balance between fairness and satisfaction with the allocation of resources. The BC method is more suitable when individual needs are the main concern, while K-means is better when ensuring an equitable distribution between agents. In this work, we provide a refined understanding of the resource allocation strategies of multi-agent systems and emphasize the strengths and limitations of each approach to help system designers choose the appropriate methods.