Moral Hazard, Learning and Temporal Dependence: Dynamic Incentive Problems in Firms and Markets

道德风险、学习和时间依赖：企业和市场的动态激励问题

• 批准号: 1629055
• 负责人: Venkataraman Bhaskar
• 金额: $ 27.15万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1629055&HistoricalAwards=false
• 关键词: Moral; Hazard; Learning; Temporal; Dependence

Incentive problems are pervasive in modern societies - they arise within firms, in the interaction between suppliers and consumers, and in online markets. This project examines how incentive problems arise in dynamic environments, and the mechanisms that may be used in order to achieve socially efficient outcomes. The first part of the project will examine dynamic incentive problems that arise when a firm introduces new technology. The efficacy of the technology may be uncertain, and both firm and workers will learn about its effects over time. The PIs will examine how these incentive problems may impede innovation, and the mechanisms that firms may use in order to mitigate inefficiency. A second part examines the interaction between supermarkets and their consumers. Supermarkets have an incentive to know the consumer's past shopping behavior, and the project will examine how the privacy of the consumer's transactions improves her bargaining position. The third part of the project will examine the functioning of illegal online drugs markets such as Silk Road and its successors. Despite the fact that these markets are illegal, are operated by criminals and buyers and sellers are anonymous, they appear to function exceedingly well. The project will examine how these markets overcome the severe moral hazard problems, and the mechanisms that law enforcement agencies may use in order to hinder illegal online trade. The analysis of incentive problems usually involves two dichotomous classes of model: moral hazard (or hidden action) models and hidden information models. This project examines how moral hazard in dynamic environments gives rise to hidden information. This phenomenon may arise when firm and workers are learning about an uncertain technology or when the future costs/benefits to an agent depend upon her current actions, and these actions are unobserved by the principal or by third parties. The proposed papers deal with a diversity of economic situations, from incentive provision within firms to the pricing strategies of supermarkets. They are unified by a common methodological theme and insight. The action of an agent today affects her preferences and payoffs tomorrow. Third parties, such as the principal or a future supplier to the consumer, do not observe the agent's action. This gives rise to a form of endogenous asymmetric information. Asymmetric information may only be latent, i.e. it may not arise on the equilibrium path, as is the case where the agent's equilibrium strategy is a pure strategy. Nonetheless, its possibility affects the incentive problem. When the agent randomizes, so that the equilibrium is in mixed strategies, asymmetric information arises on the path of play, giving rise to a mechanism design or screening problem. The novelty here is that the distribution of types is endogenous. This project will examine how hidden action with dynamic consequences aggravates agency problems in long-term relationships; how the nature of technology affects the choice of organizational form; how inter-temporal price competition between firms such as supermarkets affects the efficiency of allocations. The analysis will focus on situations where parties cannot make long term commitments and will rely on insights and techniques from the theory of dynamic games, combined with those from contract theory and mechanism design. This project also has a second component, that is motivated by the rise of online illegal drugs markets, such as Silk Road. Moral hazard problems would seem to be severe, given that agents are anonymous and have no legal recourse. How are moral hazard problems solved in competitive markets? How do these solutions affect price mark-ups and the extent of price dispersion? Can moral hazard be leveraged in order to hinder the illegal drugs trade? The research in this section builds on the theory of repeated games, played in the context of competitive markets. It also has an empirical component, that builds on data that is being collected by the PI and his collaborators since August 2013, accessing and scraping data from dark-net websites.

激励问题在现代社会中普遍存在--它们出现在企业内部、供应商与消费者之间的互动中以及在线市场中。本项目研究激励问题如何在动态环境中出现，以及可能用于实现社会有效成果的机制。 该项目的第一部分将研究动态激励问题时出现的公司引进新技术。 技术的有效性可能是不确定的，随着时间的推移，公司和工人都将了解其影响。 PI将研究这些激励问题如何阻碍创新，以及公司可能使用的机制，以减轻效率低下。第二部分考察了超市与消费者之间的互动。超级市场有动机了解消费者过去的购物行为，该项目将研究消费者的隐私如何被破坏。 交易改善了她的谈判地位。 该项目的第三部分将研究非法在线毒品市场的运作，如丝绸之路及其后续市场。尽管这些市场是非法的，由犯罪分子经营，买家和卖家都是匿名的，但它们似乎运作得非常好。该项目将研究这些市场如何克服严重的道德风险问题，以及执法机构为阻止非法网上贸易而可能使用的机制。激励问题的分析通常涉及两类二分法模型：道德风险（或隐藏行为）模型和隐藏信息模型。这个项目研究动态环境中的道德风险如何产生隐藏信息。当企业和工人正在学习一种不确定的技术时，或者当代理人的未来成本/收益取决于她当前的行为时，这种现象可能会出现，而这些行为不被委托人或第三方观察到。拟议的文件涉及各种不同的经济情况，从公司内部的奖励规定到超级市场的定价战略。它们被一个共同的方法论主题和见解所统一。一个代理人今天的行为会影响她明天的偏好和收益。第三方，如委托人或消费者的未来供应商，不观察代理人的行为。这就产生了一种内在的信息不对称。不对称信息可能只是潜在的，也就是说，它可能不会出现在均衡路径上，就像代理人的均衡策略是纯策略的情况一样。尽管如此，它的可能性影响激励问题。当代理人随机化，使均衡处于混合策略时，在博弈路径上出现信息不对称，从而产生机制设计或筛选问题。这里的新奇在于类型的分布是内生的。这个项目将研究如何隐藏的行动与动态后果加剧代理问题的长期关系;技术的性质如何影响组织形式的选择;如何跨时间的价格竞争，如超市公司之间的影响分配效率。分析将集中在当事人不能作出长期承诺的情况下，并将依赖于动态博弈理论的见解和技术，结合合同理论和机制设计。 该项目还有第二个组成部分，其动机是网上非法毒品市场的兴起，如丝绸之路。道德风险问题似乎很严重，因为代理人是匿名的，没有法律的追索权。在竞争性市场中如何解决道德风险问题？这些解决方案如何影响价格加价和价格分散的程度？能否利用道德风险来阻止非法毒品贸易？本节的研究建立在重复博弈理论的基础上，在竞争性市场的背景下进行。它还有一个经验组件，建立在PI及其合作者自2013年8月以来收集的数据基础上，从暗网网站访问和抓取数据。