Simulating and Quantifying Inequality in Strategic Agent Networks

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 strategic agents transacting in real-world social networks (often following a scale-free distribution) are not fully understood. An influential economic measure that has not received adequate attention in the complex networks and game theory communities, is the Gini Coefficient, which is widely used to quantify and understand wealth inequality. In this paper, we define a network model called a strategic agent network (SAN) and present a methodological framework based on game theory for investigating questions of inequality using SANs. We briefly comment on results obtained from a preliminary experimental investigation using a real-world dataset based on Bitcoin.

Keywords: Game theory, network science, simulation

DOI: 10.54941/ahfe1004025