Value of Information Gained From Data Mining in the Context of Information Sharing
用博弈论框架研究两个零售竞争对手之间共享数据挖掘信息的公平价值，提出了离散和连续变量下的信息共享与定价方法。
This paper uses a game-theoretic framework to suggest the fair value for information extracted via data mining and shared between two retail-market competitors. For mutual benefit, the two players each owning a privileged information set (a collection of data or database) may want to share or pool all or part of the information contained within their respective databases. Assume that each player is equipped with a data mining technique which extracts information from the data. We first model the information sharing as a cooperative game. Then, we use results from the cost sharing literature to provide information sharing methods when data can be quantified either as discrete or as continuous variables. In the latter case, we provide a means for obtaining decision rules for pricing shared information.