Nothing Special   »   [go: up one dir, main page]

  EconPapers    
Economics at your fingertips  
 

Information and Transparency: Using Machine Learning to Detect Communication

David Brown, Daniel O. Cajueiro (), Andrew Eckert () and Douglas Silveira
Additional contact information
Daniel O. Cajueiro: University of Brasilia
Andrew Eckert: University of Alberta, Department of Economics, Postal: 8-14 HM Tory Building, Edmonton, AB, T6G 2H4

No 2023-6, Working Papers from University of Alberta, Department of Economics

Abstract: Information and data transparency have been shown to have an important impact on competitive behavior and market outcomes. Market transparency can enhance competition by allowing firms to respond efficiently to a changing market environment. However, a high degree of information can facilitate coordination by enhancing communication and the monitoring of rival behavior. A recent example highlighting concerns over the use of publicly available information to communicate across firms involves the Alberta wholesale electricity market. This market used to release anonymized information on firms’ pricing strategies in near real-time. Allegations were raised that firms were using unique patterns in their prices to reveal their identities to rival firms and coordinate on higher prices. This paper uses machine learning techniques to investigate how firms could use anonymized publicly available information to communicate with their rivals. These techniques can be employed as a possible screen to evaluate whether publicly available information can be used to identify rival behavior and facilitate coordination. Based on these results, regulators can determine if the degree of market transparency is detrimental to market competition.

Keywords: Machine Learning; Electricity; Market Power; Competition Policy (search for similar items in EconPapers)
JEL-codes: D43 L13 L50 L94 Q40 (search for similar items in EconPapers)
Pages: 30 pages
Date: 2023-05-23
New Economics Papers: this item is included in nep-big, nep-cmp, nep-com and nep-reg
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://sites.ualberta.ca/~econwps/2023/wp2023-06.pdf Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ris:albaec:2023_006

Access Statistics for this paper

More papers in Working Papers from University of Alberta, Department of Economics Contact information at EDIRC.
Bibliographic data for series maintained by Joseph Marchand ().

 
Page updated 2024-11-25
Handle: RePEc:ris:albaec:2023_006