Game Theory for Data Science: Eliciting Truthful Information
Game Theory for Data Science: Eliciting Truthful Information
复制标题
数据科学的博弈论：获取真实信息
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Goran Radanovic
中科院分区：
文献类型：
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作者：
B. Faltings;Goran Radanovic
智能系统通常依赖于信息代理提供的数据，例如传感器数据或众包的人类计算。提供准确和相关的数据需要付出高昂的努力，而工程师可能并不总是愿意提供这些努力。因此，重要的是不仅要核实数据的正确性，而且要提供激励措施，以便提供高质量数据的代理得到奖励，而不提供高质量数据的代理则受到低奖励的阻碍。我们涵盖了不同的设置和他们承认的假设，包括感知、人类计算、同行评分、评论和预测。我们考察了不同的激励机制，包括适当的评分规则、预测市场和同行预测、贝叶斯真理血清、同行真理血清、相关协议，以及它们各自适用的环境。作为另一种选择，我们还考虑了声誉机制。我们通过在预测平台、社区感知和同伴评分中的应用实例来补充博弈论分析。
Intelligent systems often depend on data provided by information agents, for example, sensor data or crowdsourced human computation. Providing accurate and relevant data requires costly effort that agents may not always be willing to provide. Thus, it becomes important not only to verify the correctness of data, but also to provide incentives so that agents that provide high-quality data are rewarded while those that do not are discouraged by low rewards. We cover different settings and the assumptions they admit, including sensing, human computation, peer grading, reviews, and predictions. We survey different incentive mechanisms, including proper scoring rules, prediction markets and peer prediction, Bayesian Truth Serum, Peer Truth Serum, Correlated Agreement, and the settings where each of them would be suitable. As an alternative, we also consider reputation mechanisms. We complement the game-theoretic analysis with practical examples of applications in prediction platforms, community sensing, and peer grading.