Coopetition Against an Amazon

Ronen Gradwohl, Moshe Tennenholtz

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations


This paper studies cooperative data-sharing between competitors vying to predict a consumer’s tastes. We design optimal data-sharing schemes both for when they compete only with each other, and for when they additionally compete with an Amazon—a company with more, better data. We show that simple schemes—threshold rules that probabilistically induce either full data-sharing between competitors, or the full transfer of data from one competitor to another—are either optimal or approximately optimal, depending on properties of the information structure. We also provide conditions under which firms share more data when they face stronger outside competition, and describe situations in which this conclusion is reversed.

Original languageEnglish
Title of host publicationAlgorithmic Game Theory - 15th International Symposium, SAGT 2022, Proceedings
EditorsPanagiotis Kanellopoulos, Maria Kyropoulou, Alexandros Voudouris
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages19
ISBN (Print)9783031157134
StatePublished - 2022
Event15th International Symposium on Algorithmic Game Theory, SAGT 2022 - Colchester, United Kingdom
Duration: 12 Sep 202215 Sep 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13584 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference15th International Symposium on Algorithmic Game Theory, SAGT 2022
Country/TerritoryUnited Kingdom


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