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.
Data brokers co-opetition
Leonardo Madio
;
2022
Abstract
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.File | Dimensione | Formato | |
---|---|---|---|
cesifo1_wp7523.pdf
accesso aperto
Descrizione: CESifo Working Paper
Tipologia:
Published (publisher's version)
Licenza:
Creative commons
Dimensione
592.57 kB
Formato
Adobe PDF
|
592.57 kB | Adobe PDF | Visualizza/Apri |
Gu_Madio_Reggiani.pdf
accesso aperto
Tipologia:
Published (publisher's version)
Licenza:
Creative commons
Dimensione
442.35 kB
Formato
Adobe PDF
|
442.35 kB | Adobe PDF | Visualizza/Apri |
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.