Data brokers co-opetition
Author(s) -
Yiquan Gu,
Leonardo Madio,
Carlo Reggiani
Publication year - 2021
Publication title -
oxford economic papers
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 69
eISSN - 1464-3812
pISSN - 0030-7653
DOI - 10.1093/oep/gpab042
Subject(s) - value (mathematics) , competition (biology) , computer science , data sharing , business , medicine , ecology , alternative medicine , pathology , machine learning , biology
Data brokers share consumer data with rivals and, at the same time, compete with them for selling. We propose a ‘co-opetition’ game of data brokers and characterize their optimal strategies. When data are ‘sub-additive’ with the merged value net of the merging cost being lower than the sum of the values of individual datasets, data brokers are more likely to share their data and sell them jointly. When data are ‘super-additive’, with the merged value being greater than the sum of the individual datasets, competition emerges more often. Finally, data sharing is more likely when data brokers are more efficient at merging datasets than data buyers.
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