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

TL;DR

This paper introduces a game-theoretic model and simulation algorithm to analyze how inequality evolves among strategic agents in complex social networks, providing insights into systemic drivers of inequality.

Contribution

It presents a novel, replicable algorithm and model for quantifying inequality dynamics in complex networks, addressing a gap in empirical and theoretical research.

Findings

Inequality dynamics are consistent across different network types.

Simple models can reveal complex drivers of inequality.

The Gini coefficient effectively measures inequality 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.