Collaborative Research on the Expected Utility Theory of War

研究人员对数据生成和管理软件程序EUGene（预期效用和数据生成）进行了重大修改，该软件最初是在美国国家科学基金会的奖励下开发的，目的是：1)增加功能，提高其作为数据管理工具的实用性；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。尤金是一个软件包，它为变量生成数据，用于测试战争和争端引发的预期效用理论的一个版本。具体来说，EUGene是第一个公开可用的程序，它可以生成从1816年到1984年所有州（或双组）和年份的tau-b联盟相似性得分、风险态度得分、预期效用数据和国际互动博弈均衡。此外，EUGene还可以作为一种数据管理工具，用于创建用于定量分析国际关系的数据集，尤其适用于创建以定向双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是繁琐而困难的。研究人员利用尤金对战争的预期效用理论进行了迄今为止规模最大的分析（分析了近70万份定向两千年的数据）。他们还用它建立了一个数据集来分析11个相互竞争的国际冲突理论，这些理论涉及所有定向的州际二元体的人口。尤金在许多方面得到了扩展，使国际关系界受益。首先，研究人员实施了一种新的联盟相似性度量，即“S”得分，该得分被认为是比tau-b更好的分析联盟相似性的度量，后者是当前预期效用数据的基础。接下来，软件被更新，允许用户添加任何数据集（适当的格式）到程序提供的数据集，使EUGene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一些选项，包括生成非定向双元数据集的能力，在新的联盟数据可用时更新预期的效用数据，以及跟踪数据版本以进行复制。有了更新的数据软件，研究人员建立了新的数据集，促进了重要的新研究项目。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，研究人员扩展了先前进行的国际冲突理论的比较测试。特别是，通过增加数据，研究人员测试了一个包含贸易、政治制度和非政府组织的三阶段冲突计量经济模型。其次，研究人员探索了整个国际体系中偏好稳定性的本质，开发了一个偏好的进化模型，该模型有助于解释早期的发现，即不同地区和不同时间的战争预期效用理论不一致。第三，通过全面实施联盟相似度的“S”得分测度，并利用其开发新的风险评分和期望效用数据，充分评价这一新测度对前期和未来实证研究的效果。这项研究大大增强了我们对这一重要课题的理解。

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.