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

[2608.15131] Platform Adaptation Under Governance Interventions: Actor Best-Response Modeling and an External Public-Case Benchmark Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Artificial Intelligence arXiv:2608.15131 (cs) [Submitted on 15 Aug 2026] Title: Platform Adaptation Under Governance Interventions: Actor Best-Response Modeling and an External Public-Case Benchmark Authors: Wesley Shu , Peng Wei View a PDF of the paper titled Platform Adaptation Under Governance Interventions: Actor Best-Response Modeling and an External Public-Case Benchmark, by Wesley Shu and 1 other authors View PDF HTML (experimental) 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. Subjects: Artificial Intelligence (cs.AI) ; Computers and Society (cs.CY) Cite as: arXiv:2608.15131 [cs.AI] (or arXiv:2608.15131v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.15131 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Peng Wei [ view email ] [v1] Sat, 15 Aug 2026 09:09:32 UTC (20 KB) Full-text links: Access Paper: View a PDF of the paper titled Platform Adaptation Under Governance Interventions: Actor Best-Response Modeling and an External Public-Case Benchmark, by Wesley Shu and 1 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.AI < prev | next > new | recent | 2026-08 Change to browse by: cs cs.CY References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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