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

Wesley Shu; Peng Wei

cs.AIAug 15, 2026

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

Authors: Wesley Shu, Peng Wei

Organizations: The Institute of Energetic Paradigm · College of Plant Protection, Southwest University

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.

Figures & tables

Method	Cases	Overall	Creator	Gaming	Moderation	Stability
Full platform-adaptation simulator	72	0.836338	0.857607	0.856807	0.854240	0.793115
No moderation-burden channel	72	0.729647	0.827756	0.826956	0.610945	0.549820
No strategic-gaming channel	72	0.716135	0.827179	0.590129	0.823813	0.526438
Risk-register baseline	72	0.669731	0.641012	0.713245	0.732419	0.651604
Causal-loop baseline	72	0.589457	0.597036	0.561924	0.586646	0.592449
Generic governance critique	72	0.492750	0.485318	0.467397	0.500529	0.501272

Table 1: External public-case benchmark aggregate results.

Comparison	Mean gain	Win rate	t statistic	Min	Max
Full model vs baseline platform policy review	0.504374	1.00	668.16	0.489055	0.517360
Full model vs engagement only optimizer	0.466846	1.00	667.45	0.451755	0.478433
Full model vs generic governance critique	0.343588	1.00	467.26	0.331810	0.355264
Full model vs static cost benefit	0.395834	1.00	601.01	0.382143	0.405996
Full model vs risk register baseline	0.166607	1.00	290.94	0.155235	0.177158
Full model vs causal loop baseline	0.246881	1.00	385.59	0.234569	0.258390

Table 2: Paired comparisons against the full platform-adaptation simulator.

Method	Mean	95 percent CI
Full platform-adaptation simulator	0.836338	[0.834914, 0.837822]
No moderation-burden channel	0.729647	[0.726655, 0.732904]
No strategic-gaming channel	0.716135	[0.713204, 0.718859]
Risk-register baseline	0.669731	[0.668365, 0.671116]
Causal-loop baseline	0.589457	[0.588078, 0.590901]
Generic governance critique	0.492750	[0.491057, 0.494391]

Table 3: Bootstrap 95 percent confidence intervals for overall platform-adaptation quality.

Domain	Full model	Risk register	Causal loop	Engagement only
community platform	0.836131	0.672008	0.586358	0.375220
creator monetization	0.834768	0.669516	0.589766	0.369750
creator platform	0.835802	0.670249	0.589864	0.371126
delivery platform	0.837464	0.670163	0.583123	0.371292
digital marketplace	0.841829	0.670968	0.589935	0.370444
marketplace ranking	0.836930	0.669234	0.589348	0.368711

Table 4: Domain-level performance summary.

Appendix figures & tables2 assets

Supplementary material from the paper’s appendix.

Appendix

Rubric dimension	Operational meaning
Creator adaptation accuracy	Identifies how creators, sellers, developers, workers, or other supply-side actors adapt to the intervention.
Moderation-burden accuracy	Identifies review volume, ambiguity, appeal burden, enforcement inconsistency, or operational moderation load created by the rule.
User-incentive accuracy	Identifies changes in user trust, attention, participation, switching, reporting, or consumption incentives.
Engagement-dynamics accuracy	Distinguishes visible engagement movement from stability-enhancing platform outcomes.
Strategic-gaming accuracy	Identifies loopholes, metric gaming, evasion, or optimization paths created by the rule.
Externality-prediction accuracy	Identifies displaced costs, harms, trust erosion, ecosystem instability, or burden shifted to non-benefiting actors.

Appendix

Table 5

Case	Domain	Policy type	Expected best response	Expected failure mode
p15_ext_001	media monetization	ranking_change	loophole_search	incentive_gaming
p15_ext_002	media monetization	monetization_rule	strategic_reoptimization	moderation_backlog
p15_ext_003	media monetization	moderation_threshold	strategic_reoptimization	moderation_backlog
p15_ext_004	media ranking	ranking_change	strategic_reoptimization	incentive_gaming
p15_ext_005	media ranking	monetization_rule	strategic_reoptimization	moderation_backlog
p15_ext_006	media ranking	moderation_threshold	strategic_reoptimization	incentive_gaming

Appendix

Table 6: Summary of the 72 external public benchmark cases. The evidence package contains full source URLs and row-level scores.

Policy Evaluation

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