Unintended Consequences of Algorithmic Interventions: Modeling Regulatory "Blowback" in Platform Markets

Wang & Zhou (2025) and Lischka & Garz (2021) show that regulatory actions can disperse or shift strategic behavior, but often stop short of modeling second-order, systemic responses—like platforms or users developing workarounds, "gaming" compliance, or shifting to alternative collusive strategies. This research would build dynamic, multi-level game-theoretic models to predict and empirically test these "blowback" effects, challenging the core assumption that regulations simply reduce undesirable behaviors. By anticipating and modeling the adaptive, possibly perverse, responses to well-intentioned interventions, this work could inform smarter, more resilient policy and platform design.

References:

1. Clickbait news and algorithmic curation: A game theory framework of the relation between journalism, users, and platforms. Juliane A. Lischka, Marcel Garz (2021). New Media & Society.
2. Can Government Incentive and Penalty Mechanisms Effectively Mitigate Tacit Collusion in Platform Algorithmic Operations?. Yanan Wang, Yaodong Zhou (2025). Syst..