Collaborative Research on the Expected Utility Theory of War

战争预期效用理论的合作研究

• 批准号: 9975115
• 负责人: D. Scott Bennett
• 金额: $ 5.66万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-08-15 至 2001-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9975115&HistoricalAwards=false
• 关键词: Collaborative; Research; Expected; Utility; Theory

The researchers significantly revise the data-generating and management software program EUGene (Expected Utility and Data Generation), originally developed under an NSF award, in order to 1) add functions that will improve its usefulness as a data management tool, and 2) address new theoretical research questions concerning the expected utility theory of war and comparative testing of international conflict theories.EUGene is a software package that generates data for variables used to test a version of an expected utility theory of war and dispute initiation. Specifically, EUGene is the first publicly-available program that generates tau-b alliance similarity scores, risk attitude scores, expected utility data, and international interaction game equilibria for all states (or dyads) and years from 1816 to 1984. In addition, EUGene serves as a data management tool for creating data sets to be used in the quantitative analysis international relations, and is particularly useful for creating data sets with the directed-dyad-year as the unit of analysis. Until now, creating such data sets has been cumbersome and difficult. The researchers have used EUGene in conducting the largest analyses of the expected utility theory of war to date (analyzing nearly 700,000 directed dyad-years of data. They also have used it to build a data set to analyze 11 competing theories of international conflict on the population of all directed interstate dyads.EUGene is expanded in a number of ways that benefits the international relations community. First, the researchers implement a new measure of alliance similarity, the "S" score which has been argued to be a better measure for analyzing alliance similarity than tau-b, on which current expected utility data are based. Next, the software are updated to allow users to add any data set (of a suitable format) to those provide with the program, making EUGene useful for a wide variety of data set development projects. Finally, the researchers implement a number of options that have been requested by users, including the ability to generate non-directed dyad data sets, to update expected utility data as new alliance data is made available, and to track versions of data for replication.With the updated data software, the researchers build new data sets that facilitate significant new research projects. Three main substantive projects result from this research. First, using newly generated data the researchers expand on comparative tests of theories of international conflict conducted previously. In particular, with added data the researchers test a three-stage econometric model of conflict that integrates trade, political institution, and non-governmental organization. Second, the researchers explore the nature of preference stability across the international system, developing an evolutionary model of preferences that helps to explain earlier findings of an inconsistent fit of the expected utility theory of war across regions and over time. Third, by fully implementing the "S" score measure of alliance similarity and using it to develop new risk scores and expected utility data, the researchers fully evaluate the effect of this new measure for prior and future empirical research. This research enhances substantially our understanding of this important topic.

研究人员对最初在NSF奖下开发的数据生成和管理软件程序Eugene(预期效用和数据生成)进行了重大修订，以1)添加功能，提高其作为数据管理工具的有用性，以及2)解决有关战争预期效用理论和国际冲突理论比较测试的新理论研究问题。EUgene是一个软件包，为用于测试战争和争端引发的预期效用理论版本的变量生成数据。具体地说，尤金是第一个公开的程序，它生成了从1816年到1984年的所有状态(或二元)的tau-b联盟相似性分数、风险态度分数、预期效用数据和国际互动博弈均衡。此外，Eugene是一种数据管理工具，用于创建用于定量分析国际关系的数据集，特别适用于创建以有向二元年为分析单位的数据集。到目前为止，创建这样的数据集一直既繁琐又困难。研究人员使用尤金对战争的预期效用理论进行了迄今为止最大规模的分析(分析了近70万个定向二元数据)。他们还利用它建立了一个数据集，以分析关于所有定向州际双亲人口的11种相互竞争的国际冲突理论。EUgene以多种方式扩展，使国际关系界受益。首先，研究人员采用了一种新的联盟相似性度量方法--S分数，该分数被认为是分析联盟相似性的一种比基于当前预期效用数据的tau-b更好的度量。接下来，对软件进行更新，以允许用户在程序提供的数据集的基础上添加任何数据集(格式合适)，使尤金公司对各种数据集开发项目有用。最后，研究人员实现了用户要求的多个选项，包括生成非定向DYAD数据集的能力、随着新联盟数据的可用而更新预期效用数据的能力、以及跟踪用于复制的数据版本的能力。通过更新的数据软件，研究人员建立新的数据集，以促进重要的新研究项目。这项研究产生了三个主要的实质性项目。首先，使用新生成的数据，研究人员对以前进行的国际冲突理论的比较测试进行了扩展。特别是，通过增加数据，研究人员测试了一个三阶段的冲突计量经济学模型，该模型整合了贸易、政治制度和非政府组织。其次，研究人员探索了整个国际体系中偏好稳定性的本质，开发了偏好的进化模型，有助于解释早先发现的跨地区和随时间推移的战争预期效用理论不一致的结果。第三，通过全面实施联盟相似性的S得分度量，并利用它来开发新的风险得分和预期效用数据，研究人员充分评估了这一新度量在之前和未来的实证研究中的效果。这项研究大大加深了我们对这一重要主题的理解。