CAREER: Foundataions of Markets as Information Aggregation Mechanisms

职业：市场作为信息聚合机制的基础预测市场是为信息聚合而设计的市场。为了实现其目标，预测市场提供了一种合同，其未来收益与感兴趣的事件的结果挂钩。市场参与者通过交易合约来表达他们关于事件的信息。因此，市场价格可能包含所有参与者的信息，并近似代表对事件的共识预测。预测市场作为一种高效的信息聚合工具在实践中显示出巨大的潜力。然而，它们可能受到操纵。参与者有动机对他们的信息撒谎，以便在市场上获得更多利润或在市场外获得奖励。如果进行操纵，信息汇总可能会失败，市场价格的可信度也会受到质疑。另一个挑战是操作组合预测市场的计算问题。当该机制变得更具表现力，参与者可以透露他们关于结果组合的信息时，操作市场的过程可能变得难以计算，这使得这种组合预测市场不切实际。这个项目的目标是对预测市场设计中出现的问题进行基础性研究，这些问题是由于参与者的自身利益和表达的需要而产生的。陈博士将以计算机科学理论和博弈论为主要研究方法，建立预测市场的战略和计算基础，从而设计出不仅在理论上合理，而且在实际应用中适用于复杂现实环境中的信息聚合的市场机制。本项目围绕两个主题展开：抗操纵预测市场和计算效率组合预测市场。抗操纵预测市场的研究探讨了操纵何时出现，操纵如何影响信息聚合，以及如何设计预测市场机制来明确控制操纵的影响。它将回答人们是否可以信任预测市场所传递的信息这一最根本的问题，并为信息聚合设计强大的市场机制。计算有效的组合预测市场研究考察了组合预测市场的表达性和计算复杂性之间的权衡，设计了易处理的组合预测市场，并为难处理的组合预测市场开发了有效的近似算法。这将有助于将组合预测市场从理论工件转移到实际设备，以聚合更丰富的信息。此项目不依赖于任何特定的应用程序域。因此，在这个项目中设计的机制可以应用于许多环境中的信息聚合，包括企业，政府和社会。这一项目的结果还将有助于就预测市场的可能监管问题制定政策。

CAREER: Foundations of Markets as Information Aggregation MechanismsPrediction markets are markets designed for information aggregation. To achieve its goal, a prediction market offers a contract whose future payoff is tied to outcomes of an event of interest. Participants of the market express their information about the event through trading the contract. The market price hence potentially incorporates the information of all participants and approximately represents a consensus forecast for the event. Prediction markets have shown great potential as highly effective information aggregation tools in practice. However, they may be subject to manipulation. Participants have incentives to lie about their information in order to seize more profit in the market or be rewarded outside the market. With manipulation, information aggregation may fail and the credibility of market prices is put into question. Another challenge is the computational problem of operating combinatorial prediction markets. When the mechanism becomes more expressive and participants can reveal their information on combinations of outcomes, the process of operating a market may become computationally intractable, which makes such combinatorial prediction markets impractical to use. The goal of this project is to perform a foundational study on issues that arise in prediction market designbecause of the self-interest of participants and the need of expressiveness. Using computer science theory and game theory as the main research approaches, Dr. Chen will establish the strategic and computational foundations for prediction markets so as to design market mechanisms that are not only theoretically sound but also practically applicable for information aggregation in complex real-world settings.This project is centered around two themes: manipulation-resistant prediction markets and computationally efficient combinatorial prediction markets. The research on manipulation-resistant prediction markets investigates when manipulation arises, how manipulation affects information aggregation, and how to design prediction market mechanisms to explicitly control the impact of manipulation. It will provide answers to the most fundamental question of whether people can trust theinformation conveyed by prediction markets and design robust market mechanisms for information aggregation. The research on computationally efficient combinatorial prediction markets examines the tradeoff between expressiveness and computational complexity of combinatorial prediction markets, designs tractable combinatorial prediction markets, and develops efficient approximation algorithms for intractable combinatorial prediction markets. It will help to transfer combinatorial prediction markets from theoretical artifacts to practical devices for aggregating richer information. This project does not depend on any specific application domain. Hence, mechanisms designed in this project can be applied to aggregate information in many settings, including business, government, and society. Results of this project will also help to inform policy making on the possible regulation of prediction markets.