Algorithmic "Red Herrings": Detecting and Mitigating Unexpected Strategic Manipulation in Platform Pricing Games

Most current models, like Guo et al. (2024) and Liu (2024), focus on major, predictable strategies in platform pricing or content competition. But what about those rare, outlier behaviors—like coordinated flash-mob purchasing or viral anti-collusion campaigns—where agents subvert platform expectations, creating losses or undermining discriminatory pricing? This research would systematically study such "red herring" strategies as deviations from equilibrium, using evolutionary game theory and realistic agent-based simulations. The goal: understand their impact on stability, profit, and fairness, and propose regulatory and algorithmic defenses. This idea is especially novel because it focuses on edge-case, disruptive strategies that are often ignored in mainstream modeling, potentially exposing new vulnerabilities and mitigation techniques for platforms and regulators.

References:

1. An Evolutionary Game-Based Regulatory Path for Algorithmic Price Discrimination in E-Commerce Platforms. Yan Guo, Jiajun Lin, Weiqing Zhuang (2024). Mathematics.
2. Game Theory Analysis of Competitive Dynamics in the Streaming Industry: A Comparative Study of Netflix and Disney+. Hanru Liu (2024). Advances in Economics, Management and Political Sciences.