ICES: Small: Information Elicitation and Aggregation in Market Mechanisms

在大多数经济活动领域，决策者与其决策相关的信息比其他代理人少。在这种情况下，信息较少的决策者希望从信息较多的代理或一组代理那里获得信息。该奖项资助对各种机制属性的研究，这些机制可用于在单个知情代理和多个部分知情代理的情况下获取此类信息。该项目的第一部分研究了单智能体的情况，其起源可以追溯到关于评分规则的文献。虽然过去的大部分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平均值的信息，但该项目的目标是将该理论推广到引出分布的任何一组特征，例如，平均值和方差，分位数，置信区间等。并提供一个集合的完整特征，这些集合可以用有限数量的问题自行引出，而那些不能。第二部分研究单周期环境下的多智能体机制。每个知情的代理只做一次预测，尽管与单代理的情况不同，代理现在收到的支付不仅取决于他的预测和结果，还取决于其他代理的预测。目标是提供满足几个理想特性的静态机制的完整公理化表征，并分析这些机制的性能。项目的第三部分考虑了动态的、多周期的市场机制，在这个机制中，战略主体观察彼此的行为，从中学习，并相应地调整他们的预测。研究这些机制的属性对于设计新的基于市场的信息激发机制（例如，由惠普、艺电和b谷歌等公司运营的预测市场，以预测需求和其他变量）和理解已经存在的金融市场的信息属性都很重要。特别是，这个子项目弥合了关于理性预期均衡中价格信息的非战略性文献和关于动态市场模型中交易者行为的完全战略性文献之间的差距。该奖项的广泛影响有几个组成部分。这项研究的结果将包括在经济学、金融学、计算机科学和运筹学研究生的课程中。研究本身将涉及来自这些学科的学生，他们将探索信息在市场和单一代理环境中的作用、信息的聚合和获取，以及其他相关问题。最后，这项研究有可能对信息引出机制的实际设计产生直接影响，例如，公司预测市场或允许交易者买卖信息的信息交换。

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.