TY - JOUR
T1 - Selling Data to a Competitor (Extended Abstract)
AU - Gradwohl, Ronen
AU - Tennenholtz, Moshe
N1 - Publisher Copyright:
© R. Gradwohl & M. Tennenholtz.
PY - 2023/7/11
Y1 - 2023/7/11
N2 - We study the costs and benefits of selling data to a competitor. Although selling all consumers’ data may decrease total firm profits, there exist other selling mechanisms—in which only some consumers’ data is sold—that render both firms better off. We identify the profit-maximizing mechanism, and show that the benefit to firms comes at a cost to consumers. We then construct Pareto-improving mechanisms, in which each consumers’ welfare, as well as both firms’ profits, increase. Finally, we show that consumer opt-in can serve as an instrument to induce firms to choose a Pareto-improving mechanism over a profit-maximizing one.
AB - We study the costs and benefits of selling data to a competitor. Although selling all consumers’ data may decrease total firm profits, there exist other selling mechanisms—in which only some consumers’ data is sold—that render both firms better off. We identify the profit-maximizing mechanism, and show that the benefit to firms comes at a cost to consumers. We then construct Pareto-improving mechanisms, in which each consumers’ welfare, as well as both firms’ profits, increase. Finally, we show that consumer opt-in can serve as an instrument to induce firms to choose a Pareto-improving mechanism over a profit-maximizing one.
UR - http://www.scopus.com/inward/record.url?scp=85168711175&partnerID=8YFLogxK
U2 - 10.4204/EPTCS.379.26
DO - 10.4204/EPTCS.379.26
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AN - SCOPUS:85168711175
SN - 2075-2180
VL - 379
SP - 318
EP - 330
JO - Electronic Proceedings in Theoretical Computer Science, EPTCS
JF - Electronic Proceedings in Theoretical Computer Science, EPTCS
T2 - 19th Conference on Theoretical Aspects of Rationality and Knowledge, TARK 2023
Y2 - 28 June 2023 through 30 June 2023
ER -