Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/251173 
Erscheinungsjahr: 
2022
Schriftenreihe/Nr.: 
DICE Discussion Paper No. 382
Verlag: 
Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE), Düsseldorf
Zusammenfassung: 
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.
Schlagwörter: 
Algorithm
Collusion
Demand forecasting
Unobservable actions
Secretprice cutting
JEL: 
L41
L13
D43
ISBN: 
978-3-86304-381-0
Dokumentart: 
Working Paper

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