Algorithmic Collusion Without Threats
Eshwar Ram Arunachaleswaran, Natalie Collina, Sampath Kannan, Aaron, Roth, Juba Ziani

TL;DR
This paper demonstrates that supra-competitive prices can emerge in algorithmic pricing even without explicit threats, challenging traditional views on the necessity of threats for collusion.
Contribution
It shows that no-regret algorithms can lead to monopoly-like prices without explicit threat strategies, expanding the understanding of algorithmic collusion.
Findings
Supra-competitive prices arise even without threat encoding.
Any no-regret algorithm deployed by the first mover can lead to monopoly prices.
Strategies without explicit threats can form Nash equilibria in algorithmic pricing.
Abstract
There has been substantial recent concern that pricing algorithms might learn to ``collude.'' Supra-competitive prices can emerge as a Nash equilibrium of repeated pricing games, in which sellers play strategies which threaten to punish their competitors who refuse to support high prices, and these strategies can be automatically learned. In fact, a standard economic intuition is that supra-competitive prices emerge from either the use of threats, or a failure of one party to optimize their payoff. Is this intuition correct? Would preventing threats in algorithmic decision-making prevent supra-competitive prices when sellers are optimizing for their own revenue? No. We show that supra-competitive prices can emerge even when both players are using algorithms which do not encode threats, and which optimize for their own revenue. We study sequential pricing games in which a first mover…
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Taxonomy
TopicsEthics and Social Impacts of AI · Computability, Logic, AI Algorithms · Blockchain Technology Applications and Security
MethodsSparse Evolutionary Training
