Mechanism design for data science
Mechanism design for data science
复制标题
数据科学的机制设计
DOI：
10.1145/2600057.2602881
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Denis Nekipelov
中科院分区：
文献类型：
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作者：
Shuchi Chawla;Jason D. Hartline;Denis Nekipelov
数据科学的承诺是，如果来自系统的数据可以被记录和理解，那么这种理解就有可能被用来改进系统。然而，行为和经济数据与科学数据不同，因为它对系统是主观的。当系统发生变化时，行为也会发生变化，并且为了预测任何给定系统变化的行为或针对系统变化进行优化，生成数据的行为模型必须从数据中推断出来。这种推断的容易程度通常也取决于系统。简单地说，忽略行为的系统不允许对行为生成模型的任何推断，该行为生成模型可用于预测响应于行为的系统中的行为。为了实现数据科学在经济系统中的承诺，设计此类系统的理论还必须包含所需的推理属性。以收入最大化拍卖商为例。如果拍卖师知道出价人价值的分布，那么她可以运行第一价格拍卖，并根据分布调整保留价。在一些温和的分配假设下，在适当的保留价下，第一价格拍卖是收入最优的[Myerson 1981]。请注意，在大多数情况下，具有保留价的第一价格拍卖的历史出价数据中不会包含价值低于保留价的出价人的出价。因此，拍卖师无法进行数据分析，从而推断出低于保留价的出价人价值的分布特性。然而，随着时间的推移，潜在投标人的数量可能会发生变化，最优保留价也会降低。这种变化在拍卖师的数据中可能完全被忽视。在拍卖中优化收入的两个主要工具是保留价（如上所述）和熨烫。这两种工具都会导致池化行为（即，具有不同价值观的投标人采取相同的行动），经济推断无法区分这些集合的投标人。为了保持长期良好拍卖所需的分配知识，拍卖师必须通过运行非收入最优拍卖来牺牲短期收入。
The promise of data science is that if data from a system can be recorded and understood then this understanding can potentially be utilized to improve the system. Behavioral and economic data, however, is different from scientific data in that it is subjective to the system. Behavior changes when the system changes, and to predict behavior for any given system change or to optimize over system changes, the behavioral model that generates the data must be inferred from the data. The ease with which this inference can be performed generally also depends on the system. Trivially, a system that ignores behavior does not admit any inference of a behavior generating model that can be used to predict behavior in a system that is responsive to behavior. To realize the promise of data science in economic systems, a theory for the design of such systems must also incorporate the desired inference properties. Consider as an example the revenue-maximizing auctioneer. If the auctioneer has knowledge of the distribution of bidder values then she can run the first-price auction with a reserve price that is tuned to the distribution. Under some mild distributional assumptions, with the appropriate reserve price the first-price auction is revenue optimal [Myerson 1981]. Notice that the historical bid data for the first-price auction with a reserve price will in most cases not have bids for bidders whose values are below the reserve. Therefore, there is no data analysis that the auctioneer can perform that will enable properties of the distribution of bidder values below the reserve price to be inferred. It could be, nonetheless, that over time the population of potential bidders evolves and the optimal reserve price lowers. This change could go completely unnoticed in the auctioneer's data. The two main tools for optimizing revenue in an auction are reserve prices (as above) and ironing. Both of these tools cause pooling behavior (i.e., bidders with distinct values take the same action) and economic inference cannot thereafter differentiate these pooled bidders. In order to maintain the distributional knowledge necessary to be able to run a good auction in the long term, the auctioneer must sacrifice the short-term revenue by running a non-revenue-optimal auction.