Efficiency and Stability in Economic Environments with Asymmetric Information

信息不对称经济环境中的效率和稳定性

• 批准号: 9709392
• 负责人: Philip Reny
• 金额: $ 15.38万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-08-01 至 2000-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9709392&HistoricalAwards=false
• 关键词: Efficiency; Stability; Economic; Environments; Asymmetric

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.

项目摘要本提案由三个项目组成。第一个问题涉及的是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。当信息是完全的，经济学家很好地理解了哪些分配表现出稳定性，即一旦分配建立，代理人将不再进行进一步的交易。然而，到目前为止，还没有一个单一的商定的概念的稳定性时，所涉及的代理人拥有私人信息。 不同的稳定性概念已经被引入到这种设置中，每个都以它所允许的代理之间的通信结构为特征。 我们认为，尝试将这种沟通结构内生化可能是有益的，从而建立一个单一的、更有成效的稳定概念。这项研究的任何成功都将对交易制度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类似的转售问题。 当这样的转售是可取的，它标志着一个不稳定的结果所产生的机构在手（即，FCC拍卖;或政府采购规则）。本研究试图提供一个框架，在这个框架内，可以设计制度，使它们产生的结果不容易受到有利的操纵。 这不仅将提高效率，而且还可能增加政府所有实体的销售收入。 第二个项目涉及美国每年将新实习生与医院匹配的过程，该过程使用所谓的Gale-Shapley算法进行。简而言之，医院按照优先顺序列出了一些候选人，他们愿意雇用这些候选人担任空缺职位，实习生也按照优先顺序列出了他们愿意与之匹配的医院。关键的问题是：GS算法产生的匹配是否稳定，从某种意义上说，没有一个实习医生和医院之间的配对比GS算法的匹配更喜欢对方？该项目是针对显示，在大型匹配市场（如医院/实习生市场），其中代理商的偏好是私人信息，但独立产生的代理商在市场的两边，有很少的空间战略操纵的一个人的报告偏好时，Gale-Shapley算法被采用。更准确地说，当市场很大时，实习生只能向算法提交相对较短的列表（实际情况就是这样），那么实习生最好提交真实的列表。（令人惊讶的是，在其他情况下，实习医生将一家下级医院排在上级医院的前面，对他有利;但医院最好提交真实的名单）。这一理论结果意味着GS算法的结果确实具有至关重要的稳定性，这是保持其使用的先决条件。 第三个项目集中在执行理论中的信息和知识问题。一个例子说明了这个问题。考虑一个相当常见的情况，即合伙关系正在解体。合伙人之间应如何分配共同拥有的资产？(The合伙人可以是离婚的丈夫和妻子，其资产是他们的财产，或者合伙人可以是律师事务所的成员，其资产至少部分是他们的客户。在许多情况下，合伙人并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的债务来保持对资产的占有。 哪些合伙人应该继续拥有这些资产，他们应该补偿其他合伙人多少？这两个答案都取决于每个合伙人赋予资产的内在价值，只有该合伙人才知道这种价值。 确定正确的分配和补偿并非易事。为处理这一问题，出现了各种各样的制度，包括“取舍”法，即一方提出价格，另一方必须决定是（从另一方）购买还是（向另一方）以该价格出售资产。 尽管这种方法被广泛使用，但它有一些不可取的性质：当资产的价值是私人信息时，这种方法可能导致“错误”的合作伙伴最终获得资产。我们寻求一个尽可能简单的计划，可以实现一个有效的和羡慕免费的结果，在合作伙伴关系解散的背景下。由于我们之前的一些研究已经产生了一个产生有效（尽管可能不是没有嫉妒）结果的方案，我们相信解决当前（更重要的）问题只是时间问题。