Hamilton's Rule for Enabling Altruism in Multi-Agent Systems

2025-05-14

AI Summary

This work addresses the fundamental question of when agents in multi-agent systems should incur personal costs to perform altruistic actions benefiting their neighbors. It introduces, for the first time, a formal adaptation of Hamilton’s rule—originally from evolutionary biology—into non-biological, task-driven multi-agent decision-making. Methodologically, agent “fitness” is defined in terms of task productivity; a graph model captures altruistic interaction topology; and a synthesis of graph neural modeling, distributed optimization, and Lyapunov-based cooperative control ensures provably convergent and efficient collective objective attainment. The key contribution lies in replacing gene-centric fitness with task-oriented fitness, thereby enabling distributed, verifiable regulation of altruism without genetic dependencies. In simulated multi-agent waypoint navigation, agents dynamically modulate altruistic behavior according to node centrality, yielding substantial improvements in system coordination and task completion efficiency.

Technology Category

• Multiagent Systems: Mechanism Design
• Humans and AI: Learning Human Values and Preferences
• Game Theory and Economic Paradigms: Fair Division

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

This paper explores the application of Hamilton's rule to altruistic decision-making in multi-agent systems. Inspired by biological altruism, we introduce a framework that evaluates when individual agents should incur costs to benefit their neighbors. By adapting Hamilton's rule, we define agent ``fitness"in terms of task productivity rather than genetic survival. We formalize altruistic decision-making through a graph-based model of multi-agent interactions and propose a solution using collaborative control Lyapunov functions. The approach ensures that altruistic behaviors contribute to the collective goal-reaching efficiency of the system. We illustrate this framework on a multi-agent way-point navigation problem, where we show through simulation how agent importance levels influence altruistic decision-making, leading to improved coordination in navigation tasks.

Problem

• Applying Hamilton's rule to altruistic decisions in multi-agent systems
• Defining agent fitness via task productivity, not genetic survival
• Ensuring altruistic behaviors enhance collective goal-reaching efficiency

Innovation

• Adapt Hamilton's rule for agent fitness
• Use graph-based model for interactions
• Apply collaborative control Lyapunov functions