Collusive Outcomes Without Collusion
arxiv(2024)
摘要
We develop a model of algorithmic pricing that shuts down every channel for
explicit or implicit collusion while still generating collusive outcomes. We
analyze the dynamics of a duopoly market where both firms use pricing
algorithms consisting of a parameterized family of model specifications. The
firms update both the parameters and the weights on models to adapt
endogenously to market outcomes. We show that the market experiences recurrent
episodes where both firms set prices at collusive levels. We analytically
characterize the dynamics of the model, using large deviation theory to explain
the recurrent episodes of collusive outcomes. Our results show that collusive
outcomes may be a recurrent feature of algorithmic environments with
complementarities and endogenous adaptation, providing a challenge for
competition policy.
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