Research on the Game Dynamics of Optional Public Goods

The public goods game (PGG) serves as a powerful framework for investigating human cooperative behavior. While prevailing theories emphasize imitation as the primary driver of strategy updating, evidence from an empirical study on voluntary spatial PGG revealed that human decision adjustments correlate more closely with historical payoffs than with imitative behavior. The objective of this study is to validate this conclusion and to quantify the optimal strategy-updating parameters through computational modeling. We develop an agent-based model on a square lattice with periodic boundary conditions, integrating prospect theory with a mixed-strategy evolutionary framework. The model systematically examines the effects of the proportion of self-adjustment (POS), mutation rate (MIS), and sensitivity coefficient (SEN) on the stationary distribution of cooperators, defectors, and loners. Our main findings demonstrate that individuals update strategies primarily through self-adjustment based on historical payoffs, with imitation playing merely an auxiliary role. The optimal self-adjustment proportion is approximately 0.9, and low sensitivity coefficients and mutation rates favor the emergence of cooperation. These results provide quantitative support for the memory-based self-regulation mechanism and offer managerial insights into promoting cooperation in social dilemma situations.