Statistical Modeling of Networked Evolutionary Public Goods Games

TL;DR

This paper extends a statistical model to analyze multiple small dynamic networks in public goods games, enabling better inference of social dilemma dynamics and human behavior.

Contribution

It introduces an extension of STERGM to small networks, improving inference accuracy through direct computation instead of MCMC methods.

Findings

Uncovered new insights into cooperation and defection dynamics.

Validated the model with experimental data.

Demonstrated robustness in social dilemma analysis.

Abstract

Repeated small dynamic networks are integral to studies in evolutionary game theory, where networked public goods games offer novel insights into human behaviors. Building on these findings, it is necessary to develop a statistical model that effectively captures dependencies across multiple small dynamic networks. While Separable Temporal Exponential-family Random Graph Models (STERGMs) have demonstrated success in modeling a large single dynamic network, their application to multiple small dynamic networks with less than 10 actors, remains unexplored. In this study, we extend the STERGM framework to accommodate multiple small dynamic networks, offering an approach to analyzing such systems. Taking advantage of the small network sizes, our proposed approach improves accuracy in statistical inference through direct computation, unlike conventional approaches that rely on Markov Chain Monte Carlo methods. We demonstrate the validity of this framework through the analysis of a networked public goods experiment into individual decision-making about cooperation and defection. The resulting statistical inference uncovers novel insights into the dynamics of social dilemmas, showcasing the effectiveness and robustness of this modeling and approach.