ICES: Small: Information Elicitation and Aggregation in Market Mechanisms

ICES：小：市场机制中的信息获取和聚合

• 批准号: 1101209
• 负责人: Nicolas Lambert
• 金额: $ 39.65万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-05-01 至 2016-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1101209&HistoricalAwards=false
• 关键词: ICES; Small; Information; Elicitation; Aggregation

Situations in which a decision maker has less information relevant for her decision than does some other agent occur in most areas of economic activity. In such situations, the less informed decision maker would like to obtain information from the more informed agent, or a group of agents. This award funds research on the properties of various mechanisms that can be used to elicit such information, both in the case of a single informed agent and in the case of multiple partially informed agents.The first part of the project studies the single-agent case, the origins of which go back to the literature on scoring rules. While most of the past work has focused on eliciting either the full information about the distribution of the random variable or only the information about its mean, the project's goal is to generalize this theory to the elicitation of any set of characteristics of the distribution, such as, e.g., the mean and the variance, the quantiles, confidence intervals, etc., and to provide a full characterization of sets that can be elicited on their own using a limited number of questions and those that cannot.The second part of the project studies multi-agent mechanisms in a one-period setting. Each informed agent makes a prediction only once, though unlike the single-agent case, the payment that the agent receives now depends not only on his prediction and the outcome, but also on the predictions of other agents. The goal is to provide a full axiomatic characterization of static mechanisms that satisfy several desirable properties, and analyze the performance of such mechanisms.The third part of the project considers dynamic, multi-period market mechanisms, in which strategic agents observe each other's actions, learn from them, and adjust their forecasts accordingly. The research into the properties of such mechanisms is important both for the design of new market-based information elicitation mechanisms (such as, e.g., prediction markets operated by companies like Hewlett-Packard, Electronic Arts, and Google to forecast demand and other variables) and for understanding the informational properties of financial markets that already exist. In particular, this subproject bridges a gap between the non-strategic literature on the informativeness of prices in rational expectations equilibria and the fully strategic literature on the behavior of traders in dynamic market models.The broader impact of this award has several components. The results of this research will be included in the courses taught to graduate students in economics, finance, computer science, and operations research. The research itself will involve students from these disciplines exploring the role of information in markets and single-agent settings, its aggregation and elicitation, and other related issues. Finally, this research has potential for a direct impact on the practical design of information elicitation mechanisms, such as, e.g., corporate prediction markets or information exchanges that allow traders to buy and sell information.

在大多数经济活动领域，决策者所掌握的与其决策相关的信息比其他人少。在这种情况下，不太知情的决策者希望从更知情的代理人或一组代理人那里获得信息。该奖项的资金研究的各种机制，可以用来引出这样的信息，无论是在一个单一的知情代理人的情况下，并在多个部分知情agents.The项目的第一部分研究的单代理的情况下，其起源可以追溯到文学评分规则。虽然过去的大多数工作都集中在引出关于随机变量分布的全部信息或仅关于其均值的信息，但该项目的目标是将该理论推广到任何分布特征集的引出，例如，均值和方差、分位数、置信区间等，项目的第二部分研究在一个时期内的多代理机制。每个知情的代理人只做一次预测，但与单代理人的情况不同，代理人现在收到的报酬不仅取决于他的预测和结果，而且还取决于其他代理人的预测。我们的目标是提供一个完整的公理表征静态机制，满足几个理想的属性，并分析这样的mechanism.The项目的第三部分的性能考虑动态的，多期的市场机制，其中战略代理观察对方的行动，从他们身上学习，并相应地调整他们的预测。对这种机制的性质的研究对于设计新的基于市场的信息获取机制（例如，预测市场由惠普、电子艺界和谷歌等公司运营，用于预测需求和其他变量），并用于了解现有金融市场的信息属性。特别是，这个子项目弥合了理性预期均衡中价格信息性的非战略文献与动态市场模型中交易者行为的完全战略文献之间的差距。这项研究的结果将被纳入经济学、金融学、计算机科学和运筹学研究生的课程。研究本身将涉及来自这些学科的学生，探索信息在市场和单一代理环境中的作用，其聚合和启发，以及其他相关问题。最后，这项研究有可能直接影响信息获取机制的实际设计，例如，公司预测市场或允许交易者买卖信息的信息交换。