Arbeitspapier

Screen for collusive behavior: A machine learning approach

to related objects

The paper uses a machine learning technique to build up a screen for collusive behavior. Such tools can be applied by competition authorities but also by companies to screen the behavior of their suppliers. The method is applied to the German retail gasoline market to detect anomalous behavior in the price setting of the filling stations. Therefore, the algorithm identifies anomalies in the data-generating process. The results show that various anomalies can be detected with this method. These anomalies in the price setting behavior are then discussed with respect to their implications for the competitiveness of the market.

Language: Englisch

Bibliographic citation: Series: Hohenheim Discussion Papers in Business, Economics and Social Sciences ; No. 01-2024

Classification: Wirtschaft
Forecasting Models; Simulation Methods
Antitrust Law
Antitrust Policy and Public Enterprises, Nonprofit Institutions, and Professional Organizations

Subject: Machine Learning
Cartel Screens
Fuel Retail Market

Event: Geistige Schöpfung

(who): Bantle, Melissa

Event: Veröffentlichung

(who): Universität Hohenheim, Fakultät Wirtschafts- und Sozialwissenschaften

(where): Stuttgart

(when): 2024

Handle: http://hdl.handle.net/10419/285380

URN: urn:nbn:de:bsz:100-opus-23003

Last update: 10.03.2025, 11:41 AM CET

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Object type

Arbeitspapier

Associated

Bantle, Melissa
Universität Hohenheim, Fakultät Wirtschafts- und Sozialwissenschaften

Time of origin

2024

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