🤖 AI Summary
This study addresses the ineffectiveness of platform governance assessments caused by overlooking participants’ adaptive responses. We propose a theoretical framework conceptualizing governance as an intervention within an adaptive system. By developing a multi-agent best-response model and a full-platform simulator, we validated a dynamic evaluation framework across 72 case studies. Experimental results demonstrate that the simulator achieves an adaptation quality score of 0.836 and attains a 100% win rate against all baselines, significantly outperforming traditional static methods. This research overcomes the limitations of static assessment paradigms, offering novel theoretical insights into complex governance interactions and providing a high-precision quantitative tool for evaluating dynamic platform ecosystems.
📝 Abstract
Digital platforms govern by changing rules: rankings, monetization thresholds, moderation standards, verification systems, disclosure requirements, appeal processes, and access policies. These interventions are rarely absorbed passively. Creators, sellers, advertisers, moderators, users, developers, and strategic operators adapt to the new reward surface. This paper develops a platform-adaptation model for evaluating governance interventions as transitions in adaptive multi-actor information systems. The model represents actor best response, strategic gaming opportunity, moderation burden, user-incentive movement, enforcement response, externality formation, and downstream platform stability. We evaluate the model on 72 external public platform-governance cases covering media monetization, ranking systems, verification, delivery platforms, marketplaces, app stores, community platforms, and creator ecosystems. Across 9 methods and 648 method-case evaluations, the full platform-adaptation simulator achieves mean adaptation quality of 0.836338, compared with 0.669731 for a risk-register baseline, 0.589457 for causal-loop analysis, 0.492750 for generic governance critique, 0.369492 for engagement-only optimization, and 0.331965 for baseline policy review. Paired comparisons show a win rate of 1.00 against all tested baselines and channel ablations. The contribution is an information-systems theory and measurement framework showing why platform governance evaluation fails when it treats policy rules as static controls rather than interventions into adaptive actor-response fields.