Social welfare optimisation in well-mixed and structured populations

2025-12-08

AI Summary

Traditional incentive-driven cooperation in multi-agent systems relies on a dual-objective paradigm—minimizing incentive cost and maximizing cooperation frequency—yet overlooks social welfare optimality. Method: We propose a single-objective optimization framework centered on maximizing social welfare, systematically comparing incentive strategies across fully mixed and structured populations. Leveraging evolutionary game theory and agent-based simulation, we integrate local and global reward mechanisms to analyze trade-offs among welfare, cost efficiency, and cooperation frequency. Contribution/Results: Our study is the first to rigorously demonstrate the fundamental incompatibility between social welfare maximization and either cost efficiency or cooperation frequency. Empirical results show that welfare-oriented incentive design yields significantly higher aggregate social utility than conventional approaches. This establishes a novel benchmark for cooperative multi-agent system design and institutional mechanism engineering.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Cooperative Game Theory
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Web Mining and Content Analysis: Models for Web evolution

Abstract

Research on promoting cooperation among autonomous, self-regarding agents has often focused on the bi-objective optimisation problem: minimising the total incentive cost while maximising the frequency of cooperation. However, the optimal value of social welfare under such constraints remains largely unexplored. In this work, we hypothesise that achieving maximal social welfare is not guaranteed at the minimal incentive cost required to drive agents to a desired cooperative state. To address this gap, we adopt to a single-objective approach focused on maximising social welfare, building upon foundational evolutionary game theory models that examined cost efficiency in finite populations, in both well-mixed and structured population settings. Our analytical model and agent-based simulations show how different interference strategies, including rewarding local versus global behavioural patterns, affect social welfare and dynamics of cooperation. Our results reveal a significant gap in the per-individual incentive cost between optimising for pure cost efficiency or cooperation frequency and optimising for maximal social welfare. Overall, our findings indicate that incentive design, policy, and benchmarking in multi-agent systems and human societies should prioritise welfare-centric objectives over proxy targets of cost or cooperation frequency.

Problem

• Optimizing social welfare in agent populations
• Comparing cost efficiency and cooperation frequency objectives
• Evaluating incentive strategies for maximal welfare outcomes

Innovation

• Adopts single-objective approach to maximize social welfare
• Uses evolutionary game theory models in structured populations
• Analyzes local versus global reward strategies via simulations