Arbeitspapier
Screen for collusive behavior: A machine learning approach
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: Hohenheim Discussion Papers in Business, Economics and Social Sciences ; No. 01-2024
- Klassifikation
-
Wirtschaft
Forecasting Models; Simulation Methods
Antitrust Law
Antitrust Policy and Public Enterprises, Nonprofit Institutions, and Professional Organizations
- Thema
-
Machine Learning
Cartel Screens
Fuel Retail Market
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Bantle, Melissa
- Ereignis
-
Veröffentlichung
- (wer)
-
Universität Hohenheim, Fakultät Wirtschafts- und Sozialwissenschaften
- (wo)
-
Stuttgart
- (wann)
-
2024
- Handle
- URN
-
urn:nbn:de:bsz:100-opus-23003
- Letzte Aktualisierung
-
10.03.2025, 11:41 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Bantle, Melissa
- Universität Hohenheim, Fakultät Wirtschafts- und Sozialwissenschaften
Entstanden
- 2024
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