A Multi Criteria Study of Collusion Risk Factors

Department of Management, Università Ca' Foscari Venezia Working Paper No. 2016 / 21

Applications of Artificial Intelligence and Neural Systems to Data Science. Smart Innovation, Systems and Technologies, vol. 360, Esposito, A., Faundez-Zanuy, M., Morabito, F. C., & Pasero, E. (Eds), 2023, Springer Singapore: https://doi.org/10.1007/978-981-99-3592-5_22

11 Pages Posted: 5 Jan 2017 Last revised: 7 Sep 2023

See all articles by Andrea Ellero

Andrea Ellero

Ca Foscari University of Venice - Department of Management

Paola Ferretti

Ca Foscari University of Venice - Dipartimento di Economia

Elena Zocchia

Ca Foscari University of Venice

Date Written: December 1, 2016

Abstract

Market features can be considered as forerunners of the European Commission’s actions aimed at recognizing collusive behaviours. To identify information that might support the Commission in the exercise of its role of antitrust authority we propose a multi criteria approach. Its focus is on the manufacturing sector and the aim is also to prevent undesired behaviours. Market sector features, such as price-cost margin or market entrance rate, are linked to the likelihood of a collusive behaviour in the sector in terms of “implications rules” by means of Dominance-based Rough Set Approach. Data come from institutional sources concerning different manufacturing sectors from five countries (France, Germany, Italy, Spain and United Kingdom) from 2000 to 2010.

This is a preprint of the following chapter:
Problematic Merging and Cartels: A Collusion Risk Factors Analysis, published in Applications of Artificial Intelligence and Neural Systems to Data Science. Smart Innovation, Systems and Technologies, vol. 360, edited by Anna Esposito, Marcos Faundez-Zanuy, Francesco Carlo Morabito, Eros Pasero, 2023, Springer Singapore, reproduced with permission of Springer Singapore. The final authenticated version is available online at: https://doi.org/10.1007/978-981-99-3592-5_22

Keywords: collusion prevention, manufacturing sector, dominance based rough set approach, multi-criteria.

JEL Classification: M40

Suggested Citation

Ellero, Andrea and Ferretti, Paola and Zocchia, Elena, A Multi Criteria Study of Collusion Risk Factors (December 1, 2016). Department of Management, Università Ca' Foscari Venezia Working Paper No. 2016 / 21, Applications of Artificial Intelligence and Neural Systems to Data Science. Smart Innovation, Systems and Technologies, vol. 360, Esposito, A., Faundez-Zanuy, M., Morabito, F. C., & Pasero, E. (Eds), 2023, Springer Singapore: https://doi.org/10.1007/978-981-99-3592-5_22, Available at SSRN: https://ssrn.com/abstract=2893247 or http://dx.doi.org/10.2139/ssrn.2893247

Andrea Ellero (Contact Author)

Ca Foscari University of Venice - Department of Management ( email )

San Giobbe, Cannaregio 873
Venice, 30121
Italy

Paola Ferretti

Ca Foscari University of Venice - Dipartimento di Economia ( email )

Cannaregio 873
Venice, 30121
Italy
+39-041-2346923 (Phone)

HOME PAGE: http://www.unive.it

Elena Zocchia

Ca Foscari University of Venice ( email )

Dorsoduro 3246
Venice, Veneto 30123
Italy

Do you have negative results from your research you’d like to share?

Paper statistics

Downloads
72
Abstract Views
704
Rank
585,002
PlumX Metrics