Agent-Based Coevolution of Heuristic Behavior and Network Structure: Examining Transparency, Reputation, and Monitoring

Digital platforms rely on sustained user contribution, review, and moderation, yet these activities are privately costly and vulnerable to free-riding, gaming, and reputational distortion. Drawing on evolutionary game theory, public-goods dilemmas, and reputation-based cooperation, this paper develops an agent-based model of the coevolution of heuristic behavior and network structure in a platform-governed digital environment, combining boundedly rational learning, costly and endogenous ties, imperfect reputation, and platform-governance mechanisms—transparency, reward intensity, monitoring, and sanctioning. The model is used as a controlled computational experiment to examine when platform governance sustains commons quality and when it produces unintended behavioral or structural consequences. Within the model, transparency functions as a central enabling condition for productive platform dynamics. Rewards alone do not prevent commons deterioration or the dominance of gaming/extraction when transparency is absent; when transparency is higher, rewards become more actionable, high-quality contribution and review activity increase, gaming declines, and commons quality improves. This transparency–reward complementarity indicates that transparency makes the governance environment learnable while rewards make productive participation viable. At the same time, governance does not operate as a simple linear intervention: transparent regimes can improve reputation validity and suppress extraction, but may also reshape density, selectivity, resource pressure, and access to viable network positions. Robustness checks with fixed network topologies confirm the behavioral mechanism while showing that its full relational consequences require endogenous network coevolution; a one-factor-at-a-time sensitivity screen indicates the regime pattern is not dependent on a narrow baseline calibration. As a generative, non-calibrated simulation, these findings describe plausible governance mechanisms and trade-offs within the tested parameter space, rather than predictions for a specific platform.