Should Demand Models Incorporate Competitor Prices? Oblivious Learning and Algorithmic Collusion
arXiv:2606.05363v1 Announce Type: cross Abstract: On a platform with many sellers, should a pricing algorithm explicitly model competitors' prices when learning demand? Classical learning arguments suggest an affirmative answer: ignoring competitors induces model misspecification and inefficiency. In contrast, recent work on algorithmic collusion suggests that strategic obliviousness -- deliberately ignoring competitor prices -- may facilitate collusive outcomes and improve profits. We study this modeling choice in a stylized competitive market with unknown noisy demand, in which multiple sell
The paper addresses a critical, current dilemma in AI-driven market strategies: whether to build 'oblivious' pricing algorithms to potentially facilitate collusion, amidst increasing scrutiny on algorithmic fairness and competition.
This research directly impacts the design and regulation of automated pricing systems, influencing market competition, consumer prices, and the profitability of digital platforms.
Understanding the trade-offs between modelling competitor prices and strategic obliviousness will inform best practices for AI-driven pricing and potentially lead to new regulatory frameworks.
- · Platforms with sophisticated AI pricing models
- · Academics studying algorithmic game theory
- · Consumers if anti-collusion measures are effective
- · Companies relying on outdated pricing strategies
- · Regulators without clear guidelines for algorithmic collusion
Increased focus on the design principles of AI pricing algorithms to prevent or enable algorithmic collusion.
Development of new regulatory frameworks or antitrust policies specifically targeting AI-driven market behaviors.
Long-term shifts in market structures and consumer trust based on the perceived fairness and transparency of automated pricing.
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Read at arXiv cs.LG