论文标题
对抗亚马逊的竞争
Coopetition Against an Amazon
论文作者
论文摘要
本文研究了竞争者之间的合作数据共享，以预测消费者的口味。我们设计了最佳数据共享方案，既是因为它们仅相互竞争，又是他们与亚马逊竞争的同时 - 一家拥有更多，更好的数据的公司。我们表明，根据信息结构的属性，可以是最佳或近似最佳的数据，这些方案 - 概率地诱导竞争对手之间的完整数据共享或将数据从一个竞争对手转移到另一个竞争对手的阈值规则。我们还提供条件，当公司面对更强大的外部竞争时，他们共享更多数据，并描述逆转这一结论的情况。
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.
