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Autonomous Algorithmic Collusion: Economic Research and Policy Implications

Stephanie Assad, Emilio Calvano, Giacomo Calzolari, Robert Clark, Vincenzo Denicolò, Daniel Ershov, Justin Johnson, Sergio Pastorello, Andrew Rhodes, Lei Xu, Matthijs Wildenbeest. 2021. Oxford Review of Economic Policy, 37(3), 459-478.

Abstract

This article reviews economic research on autonomous algorithmic collusion and discusses its implications for competition policy. It connects theoretical, experimental, and empirical evidence to the institutional questions facing competition authorities.

Main Finding

The economic evidence points to specific mechanisms through which autonomous algorithms can soften competition, but the strength of those mechanisms depends on market and algorithm design.

Policy Relevance

Competition authorities need evidence on when algorithmic systems change competitive conduct, not only general claims about algorithmic collusion.

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