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Reduced demand uncertainty and the sustainability of collusion: How AI could affect competition

O’Connor, Jason and Nathan Wilson ()

Information Economics and Policy, 2021, vol. 54, issue C

Abstract: We model how a technology that perfectly predicts one of two stochastic demand shocks alters the character and sustainability of collusion. Our results show that mechanisms that reduce firms’ uncertainty about the true level of demand have ambiguous welfare implications for consumers and firms alike. An exogenous improvement in firms’ ability to predict demand may make collusion possible where it was previously unsustainable or more profitable where it previously existed. However, an increase in transparency also may make collusion impracticable where it had been possible. The intuition for this ambiguity is that greater clarity about the true state of demand raises the payoffs both to colluding and to cheating. Our findings on the ambiguous welfare implications of reduced uncertainty contribute to the emerging literature on how algorithms, artificial intelligence (AI), and “big data” in market intelligence applications may affect competition.

Keywords: Artificial Intelligence; Uncertainty; Collusion; Price Discrimination; Antitrust (search for similar items in EconPapers)
JEL-codes: K12 L13 L40 (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1016/j.infoecopol.2020.100882

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