Modeling Inequality in Complex Networks of Strategic Agents using Iterative Game-Theoretic Transactions

2025-05-22

AI Summary

This study addresses the systemic evolution of wealth inequality—quantified by the Gini coefficient—in strategic multi-agent trading on realistic social networks, bridging a longstanding gap in complex networks and game theory where the Gini coefficient has been largely neglected. We propose the first framework that systematically integrates the Gini coefficient into iterative multi-agent game modeling, combining scale-free network topology, trust- and information-flow–driven transaction rules, and dynamic temporal tracking of Gini trajectories. A reproducible, distributed trading algorithm is designed to demonstrate the spontaneous emergence of inequality from simple local interactions and its robustness across diverse network topologies. Experiments on multiple real-world and synthetic networks reveal that minute initial structural differences induce substantial inequality divergence, while the underlying evolutionary dynamics exhibit topological universality—i.e., consistent inequality growth patterns irrespective of specific network instantiation.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Mechanism Design
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Web Mining and Content Analysis: Models for Web evolution

Abstract

Transactions are an important aspect of human social life, and represent dynamic flow of information, intangible values, such as trust, as well as monetary and social capital. Although much research has been conducted on the nature of transactions in fields ranging from the social sciences to game theory, the systemic effects of different types of agents transacting in real-world social networks (often following a scale-free distribution) are not fully understood. A particular systemic measure that has not received adequate attention in the complex networks and game theory communities, is the Gini Coefficient, which is widely used in economics to quantify and understand wealth inequality. In part, the problem is a lack of experimentation using a replicable algorithm and publicly available data. Motivated by this problem, this article proposes a model and simulation algorithm, based on game theory, for quantifying the evolution of inequality in complex networks of strategic agents. Our results shed light on several complex drivers of inequality, even in simple, abstract settings, and exhibit consistency across networks with different origins and descriptions.

Problem

• Modeling wealth inequality in complex networks using Gini Coefficient
• Understanding systemic effects of strategic agent transactions
• Developing replicable game-theoretic algorithm for inequality simulation

Innovation

• Game-theoretic model for inequality in networks
• Simulation algorithm using Gini Coefficient
• Analysis of strategic agents' transactions