Theory-based Measurement of Varieties of Power Using a Novel Semi-supervised IRT Model

使用新型半监督 IRT 模型对各种功率进行基于理论的测量

• 批准号: 2148904
• 负责人: David Siegel
• 金额: $ 9.08万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2148904&HistoricalAwards=false
• 关键词: Theory; based; Measurement; Varieties; Power

The concept of state power has been invoked to explain everything from decisions to begin or end wars to trade policy to decisions nations make within their own borders. Yet, despite the prevalence and importance of the concept, it has been challenging to measure power in any consistent manner. What set of characteristics might compel a nation to go along with the preferences of other nations? And can we measure those characteristics ahead of time, in order to better predict the behavior of states as they respond to their own and other states’ absolute and relative power? Such predictions are essential to securing the national defense. This project will produce a theoretically-informed set of measures of power by using new semi-supervised machine learning techniques on a wide range of data sources gathered for the purpose. That set of measures will aid policy-makers in assessing the power dynamics between states, helping decision-making and enhancing security.In both scientific and policy circles, a wide range of theoretical conceptions of power are employed, most notably hard and soft power. Measurements of power, however, typically only capture some of its theoretical uses, while at the same time including aspects that might be unrelated to the use of power in question. When international relations scholars discuss “power,” for instance, they could be referring to very specific types of power, and their conclusions, measures, and models may not carry over to other types. For example, military power has been operationalized by the size of the army and the Composite Index of National Capability (CINC) score, but neither are likely useful as measures when elements of soft power are in play. This project offers several improvements in the ability to measure power. One, it provides a series of theoretically-informed measures of types of power. Two, it gathers a large dataset of sources that inform those different measures of power. Three, it provides a flexible tool—a semi-supervised machine learning algorithm making use of a modified Bayesian Item Response Theory model—to transparently construct alternate measures of power. That opens the door to studies that consistently assess across contexts the role of different types of power in international relations and allows stakeholders to communicate clearly about the aspects of power most relevant to them.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

国家权力的概念被用来解释一切事情，从开始或结束战争的决定，到贸易政策，再到国家在本国境内做出的决定。然而，尽管这一概念十分普遍和重要，但以任何一致的方式衡量权力一直是一项挑战。什么样的特征会迫使一个国家沿着其他国家的偏好？我们是否可以提前测量这些特征，以便更好地预测国家在应对本国和其他国家的绝对和相对权力时的行为？这种预测对于确保国防安全至关重要。该项目将通过使用新的半监督机器学习技术在为此目的收集的广泛数据源上产生一组理论上知情的功率测量。这一系列措施将帮助决策者评估国家之间的权力动态，帮助决策和加强安全。在科学和政策领域，广泛的权力理论概念被采用，最主要的是硬权力和软权力。然而，对功率的测量通常只捕捉其理论用途的一部分，而同时包括可能与所讨论的功率使用无关的方面。例如，当国际关系学者讨论“权力”时，他们可能指的是非常具体的权力类型，他们的结论、措施和模型可能无法推广到其他类型。例如，军事实力是通过军队规模和国家能力综合指数（CINC）得分来实现的，但当软实力因素发挥作用时，两者都不太可能作为衡量标准。该项目在测量功率的能力方面提供了几项改进。第一，它提供了一系列理论上的权力类型的衡量标准。第二，它收集了一个大型的数据集，这些数据集包含了不同的权力衡量标准。第三，它提供了一个灵活的工具-一个半监督机器学习算法，利用修改后的贝叶斯项目反应理论模型-透明地构建替代措施的权力。这为研究打开了一扇大门，这些研究可以持续评估不同类型的权力在国际关系中的作用，并允许利益相关者就与他们最相关的权力方面进行清晰的沟通。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。