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尤金进行了重大修改（预期效用和数据生成），最初是在NSF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有用性，和2）解决了关于战争的预期效用理论和国际冲突理论的比较测试的新的理论研究问题。尤金是一个软件包，生成用于测试战争和争端引发的预期效用理论版本的变量的数据。 具体来说，尤金是第一个公开可用的程序，它可以生成1816年至1984年所有州（或二元组）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，尤金还可作为数据管理工具，用于创建用于定量分析国际关系的数据集，特别是用于创建以有向二元年为分析单位的数据集。 到目前为止，创建这样的数据集既麻烦又困难。 研究人员使用尤金对迄今为止战争的预期效用理论进行了最大规模的分析（分析了近70万个有向二元年的数据）。 他们还用它来建立一个数据集，以分析11个相互竞争的国际冲突理论对所有有向州际二元组的人口。 首先，研究人员实施了一种新的联盟相似性度量方法，即“S”得分，该得分被认为是一种比tau-b更好的联盟相似性分析方法，而tau-b是当前预期效用数据的基础。 接下来，软件进行更新，允许用户将任何数据集（合适格式）添加到程序中，使尤金可用于各种数据集开发项目。 最后，研究人员实现了一些用户要求的选项，包括生成非定向二元数据集的能力，更新预期的效用数据，因为新的联盟数据可用，并跟踪数据的版本进行复制。 这项研究产生了三个主要的实质性项目。 首先，研究人员使用新生成的数据扩展了以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三阶段的冲突计量经济学模型，该模型整合了贸易，政治机构和非政府组织。 其次，研究人员探索了整个国际体系中偏好稳定性的本质，开发了一个偏好的进化模型，有助于解释早期发现的不同地区和不同时间的战争预期效用理论的不一致拟合。 第三，通过全面实施联盟相似性的“S”评分度量，并使用它来开发新的风险评分和期望效用数据，研究者充分评估了这一新的度量方法对先前和未来实证研究的影响。这项研究大大加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.