Platform Adaptation Under Governance Interventions: Actor Best-Response Modeling and an External Public-Case Benchmark

This research introduces a platform-adaptation model designed to evaluate the impact of governance interventions on digital platforms. It highlights that platform rule changes, such as adjustments to rankings or moderation standards, are not passively received but actively lead to adaptation by various actors within the ecosystem. The model assesses how actors best respond to new rules, identifying strategic gaming opportunities, changes in user incentives, and potential impacts on platform stability.

Why it matters

Understanding how various actors on digital platforms respond to governance changes is critical for maintaining platform integrity, fostering trust, and ensuring long-term operational viability. This model provides a structured approach to anticipate and mitigate unintended consequences of policy implementations, which is essential for effective strategic planning and risk management in platform-based environments.

Key insights

• Digital platform governance interventions involve changes to rules across various domains, including rankings, monetization, moderation, and access.
• These interventions are met with active adaptation by a diverse set of actors, including creators, sellers, advertisers, moderators, users, and developers.
• The proposed model evaluates governance interventions by modeling actor best responses and strategic gaming opportunities.
• The model considers factors such as moderation burden, user incentive shifts, enforcement responses, externality formation, and downstream platform stability.
• The model's effectiveness is evaluated using 72 external public platform cases.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2608.15131