We gratefully acknowledge support from
the Simons Foundation and member institutions.
Full-text links:

Download:

Current browse context:

cs.GT

Change to browse by:

References & Citations

DBLP - CS Bibliography

Bookmark

(what is this?)
CiteULike logo BibSonomy logo Mendeley logo del.icio.us logo Digg logo Reddit logo

Computer Science > Computer Science and Game Theory

Title: Selling Data to a Competitor (Extended Abstract)

Authors: Ronen Gradwohl (Ariel University), Moshe Tennenholtz (Technion)
Abstract: 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.
Comments: In Proceedings TARK 2023, arXiv:2307.04005. A full version of this paper, containing all proofs, appears at arXiv:2302.00285
Subjects: Computer Science and Game Theory (cs.GT); Theoretical Economics (econ.TH)
Journal reference: EPTCS 379, 2023, pp. 318-330
DOI: 10.4204/EPTCS.379.26
Cite as: arXiv:2307.05078 [cs.GT]
  (or arXiv:2307.05078v1 [cs.GT] for this version)

Submission history

From: EPTCS [view email]
[v1] Tue, 11 Jul 2023 07:24:26 GMT (56kb)

Link back to: arXiv, form interface, contact.