Efficiency and Stability in Economic Environments with Asymmetric Information

项目摘要这项提案由三个项目组成。第一个问题涉及在个人拥有与他人相关的私人信息的环境中交换的稳定性问题。当信息完整时，经济学家很清楚哪些配置表现出稳定性，因为一旦配置确立，代理人将不再追求进一步的交易。然而，到目前为止，当涉及的特工拥有私人信息时，还没有一个达成一致的稳定概念。在这种设置中引入了不同的稳定性概念，每种概念的特征都是它允许的代理之间的通信结构。我们建议，尝试将这种沟通结构内化，从而确定一个单一的、更有成效的稳定概念，可能是有帮助的。这项研究的任何成功都将对交易机构的设计产生实际影响。例如，在最近的FCC频谱拍卖中，拍卖设计可能没有最好地考虑到转售的可能性。在许多政府采购活动中也出现了类似的转售问题。当这种转售是可取的时，它表明手头的机构(即联邦通信委员会拍卖；或政府采购规则)产生的结果不稳定。本研究试图提供一个框架，在该框架内可以设计机构，使其产生的结果不会受到有利操纵的影响。这不仅将提高效率，还可能导致出售国有实体的收入增加。第二个项目涉及美国每年将新实习生与医院配对的过程，该过程使用了一种被称为盖尔-沙普利算法的版本。简而言之，医院会按优先顺序列出一些他们愿意聘用的空缺职位的候选人，而实习生也会按优先顺序列出他们愿意与之匹配的医院。根本的问题是：GS算法产生的匹配是稳定的吗？从这个意义上说，没有实习生和医院的配对比他们的GS算法匹配更倾向于彼此？这个项目旨在表明，在大型匹配市场(如医院/实习生市场)中，代理人的偏好是私人信息，但是在市场两侧的代理人之间独立产生的，当采用Gale-Shapley算法时，对一个人报告的偏好进行战略操纵的空间很小。更准确地说，当市场很大，实习生只能向算法提交相对较短的名单时(实际上就是这种情况)，那么实习生最好提交真实的名单。(令人惊讶的是，在其他情况下，将一家较差的医院放在他的名单上的上级医院之上会对实习生有利；但医院提交真实名单总是最好的)。这一理论结果表明，GS算法的结果确实具有关键的稳定性，这是保持其使用的先决条件。第三个项目以执行理论中的信息和知识问题为中心。一个例子说明了这个问题。考虑一下一个相当常见的情况，即合伙企业正在被解散。共有财产在合伙人之间应当如何分配？(合伙人可能是一对正在离婚的夫妻，他们的财产就是他们的财产，或者合伙人可能是律师事务所的成员，其资产至少有一部分是他们的客户。)在许多情况下，合伙人不希望简单地出售资产并根据其所有权份额分享收益，而是一个或多个合伙人希望通过偿还其他合伙人来保持对资产的占有。哪些合伙人应该保持对资产的占有，他们应该向其他合伙人赔偿多少？这两个答案都取决于每个合伙人对资产的内在价值，只有该合伙人才知道这个价值。确定正确的分配和补偿并不是一项简单的任务。已经出现了各种机构来处理这个问题，包括“切而选择”方法，即一个合伙人提出一个价格，另一个合伙人必须决定是以这个价格买入(从另一方)还是(向另一方)出售资产。尽管普遍使用这种方案，但它有一些不受欢迎的特性：当资产的价值是私人信息时，这种方法可能会导致“错误的”合伙人最终拥有资产。我们寻求一个尽可能简单的方案，在合伙企业解散的情况下实现高效和无嫉妒的结果。由于我们之前的一些研究已经得出了一个产生高效(尽管可能不是没有嫉妒)结果的方案，我们认为目前(更重要的)问题也得到解决只是个时间问题。

Project Abstract There are three projects making up this proposal. The first deals with the question of the stability of exchange in environments in which individuals possess private information that is relevant to others. When information is complete, economists understand well which allocations exhibit stability in the sense that agents will pursue no further trade once the allocation is established. However, to date there is no single agreed-upon notion of stability when the agents involved possess private information. Different notions of stability have been introduced in this setting and each is characterized by the structure of communication between the agents that it allows. We suggest that it might be helpful to attempt to endogenize this communication structure, thereby settling on a single, more fruitful notion of stability. Any success in this research would have practical implications for the design of trading institutions. For example, in the recent FCC spectrum auctions, the auction design may not have best taken into account the potential for resale. Similar resale issues arise as well in many government procurement activities. When such resale is desirable, it signals an instability in the outcome generated by the institution at hand (i.e., the FCC auction; or government procurement rules). The present research seeks to provide a framework within which institutions can be designed so that the outcomes they produce are not susceptible to advantageous manipulation. This will not only enhance efficiency, but it may lead to increased revenues from the sale of government-owned entities as well. The second project concerns the yearly process of matching new interns to hospitals in the US, which is carried out using a version of what is known as the Gale-Shapley algorithm. In brief, hospitals list a number of candidates, in order of preference, that they would be willing to hire for positions that are open, and interns list, again in order of preference, the hospitals they would be willing to be matched with. The essential question is this: Is the matching produced by the GS algorithm stable, in the sense that no intern-hospital pair prefers one another to their GS-algorithm matches? This project is directed at showing that in large matching markets (such as the hospital/intern market) in which the preferences of agents are private information, but are independently generated across agents on both sides of the market, there is very little room for strategic manipulation of one's reported preferences when the Gale-Shapley algorithm is employed. More precisely, it appears that when the market is large, and interns can only submit relatively short lists to the algorithm (which is the case in practice), then it is best for interns to submit truthful lists. (Surprisingly, in other circumstances it can benefit an intern to place an inferior hospital above a superior one on his list; but it is always best for hospitals to submit truthful lists). This theoretical result implies that the outcome of the GS algorithm does indeed possess the crucial stability property that is a prerequisite for maintaining its use. The third project centers on the issue of information and knowledge in the theory of implementation. An example illustrates the problem. Consider the rather common setting in which a partnership is being dissolved. How ought the jointly-owned assets be distributed among the partners? (The partners may be a divorcing husband and wife whose assets are their possessions, or the partners may be members of a law firm, whose assets are, at least in part, their clients.) In many instances, the partners do not wish to simply sell the assets and share the proceeds according to their ownership shares, but rather one or more of the partners wishes to maintain possession of the assets by paying off the other partners. Which partners should maintain possession of the assets, and how much should they compensate the others? Both answers depend upon the intrinsic value each partner places on the assets, a value known only to that partner. Determining the correct allocation and compensation is no simple task. Various institutions have arisen for handling this problem, including the `cut and choose` method, where one partner proposes a price, and the other must decide whether to buy (from the other) or sell (to the other) the assets at that price. Despite its prevalent use, this scheme has undesirable properties: when the values placed on the assets are private information, this method can result in the `wrong` partner ending up with the assets. We seek as simple a scheme as possible that can achieve an efficient and envy-free outcome in a partnership dissolution context. Since some of our previous research has already resulted in a scheme that yields an efficient (although perhaps not envy-free) outcome, we believe it is only a matter of time before the present (more important) problem is also solved.