Collusion by algorithm: The role of unobserved actions

Simon Martin and Alexander Rasch

No 382, DICE Discussion Papers from Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE)

Abstract: We analyze the effects of better algorithmic demand forecasting on collusive profits. We show that the comparative statics crucially depend on the whether actions are observable. Thus, the optimal antitrust policy needs to take into account the institutional settings of the industry in question. Moreover, our analysis reveals a dual role of improving forecasting ability when actions are not observable. Deviations become more tempting, reducing profits, but also uncertainty concerning deviations is increasingly eliminated. This results in a u-shaped relationship between profits and prediction ability. When prediction ability is perfect, the "observable actions" case emerges.

Keywords: Algorithm; Collusion; Demand forecasting; Unobservable actions; Secretprice cutting (search for similar items in EconPapers)
JEL-codes: D43 L13 L41 (search for similar items in EconPapers)
Date: 2022
New Economics Papers: this item is included in nep-com, nep-ind, nep-law and nep-reg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:zbw:dicedp:382

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