Collaborative Research: Cooperation in Multi-Dyadic Civil Conflicts

国内冲突给相关国家和受影响的更广泛地区带来巨大的人力、经济和政治成本。当冲突涉及多个反叛组织时尤其如此（即，多二元冲突）。该项目通过评估政府与叛军的合作如何为解决冲突和减少暴力创造机会，提高了对多元国内冲突动态和解决方案的认识。它有助于政策制定者和学者之间关于战争动态和冲突后和平的性质和可持续性的持续辩论，谈到国家安全和目前卷入世界各地内战的国家的繁荣和福利的基本问题。该项目提供了与主要内战数据集兼容的新颖的跨国数据，为推进国内冲突的前沿研究提供了宝贵的工具。此外，该项目还为本科生和研究生提供培训和指导机会。了解对手之间合作的动态将改善对稳定性的评估和预测。pi对合作的综合方法提供了对合作何时标志着冲突动态和长期和平前景的有意义转变的见解。该项目涉及三个核心目标：(1)创建一个关于多二元内战中战斗人员之间合作的综合数据集，(2)评估过去合作对未来合作和冲突解决的影响，以及(3)评估过去合作对未来冲突的影响。为了实现目标1，pi创建了内战中的合作：多二元冲突数据集（CCW-MC）。《常规武器公约》涵盖了冷战后经历了最复杂国内冲突的12个国家。这些新的数据将捕捉到二元合作从最初的请求到最后执行的全部演变过程。利用这些数据，pi测试了新的理论期望（目标2和目标3），即过去的合作尝试的遗产，无论是在政府-反叛集团集团内部，还是从观察国内其他集团中收集到的，如何影响随后的合作和冲突结果。通过网络分析，pi研究了其他活跃的反叛组织对合作与冲突演变的影响。通过参与这些内战的多二元背景，该项目在理论化和评估合作的作用时明确考虑了每个二元背景。本项目通过考察政府-反叛集团集团内部和集团之间在多派系内战中合作的演变，解决了一个关键的缺口。它将提供关于合作如何在二代之间扩散，过去的合作尝试如何影响后续的合作努力，以及一种形式的合作如何影响另一种形式的后续合作的新见解。该项目通过将合作视为一个反复的过程来推进冲突管理和解决的研究，该过程揭示了在战场上无法揭示的有关战斗人员可信度的独特信息。该奖项反映了美国国家科学基金会的法定使命，并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，被认为值得支持。

Civil conflicts impose significant human, economic, and political costs on the countries involved and the broader regions affected. This is particularly true when conflicts involve multiple rebel groups (i.e., multi-dyadic conflicts). This project advances knowledge of multi-dyadic civil conflict dynamics and resolution by evaluating how government-rebel cooperation shapes opportunities for conflict resolution and violence reduction. It contributes to ongoing debates among policymakers and scholars about the dynamics of war and the nature and sustainability of post-conflict peace, speaking to fundamental questions of national security and the prosperity and welfare of countries currently embroiled in civil conflicts around the world. The project provides novel cross-country data compatible with leading civil war datasets, providing a valuable tool to advance cutting-edge research on civil conflict. Moreover, The project also provides training and mentorship opportunities for undergraduate and graduate students. Understanding the dynamics of cooperation between adversaries will improve evaluation and forecasting of (in)stability. The PIs integrative approach to cooperation provides insights into when cooperation signals meaningful shifts in conflict dynamics and prospects for long-term peace.The project involves three central aims: (1) to create a comprehensive dataset on cooperation between combatants in multi-dyadic civil wars, (2) to assess the impact of past cooperation on future cooperation and conflict resolution, and (3) to evaluate the impact of past cooperation on future conflict. To achieve aim 1, the PIs create the Cooperation in Civil War: Multi-Dyadic Conflicts Dataset (CCW-MC). CCW-MC covers 12 countries that have experienced the most complex civil conflicts in the post-cold war period. These novel data will capture the full evolution of dyadic cooperation from initial requests to final implementation. Using these data, the PIs test novel theoretical expectations (aims 2 and 3) about how the legacies of past cooperative attempts, both within a government-rebel group dyad and gleaned from observing other dyads in the country, influence subsequent cooperative and conflictual outcomes. Using network analysis, the PIs examine the influence of other active rebel groups on this evolution of cooperation and conflict. By engaging with the multi-dyadic context of these civil wars, the project explicitly takes each dyad's context into consideration when theorizing about and evaluating the role of cooperation. This project addresses a critical gap by examining the evolution of cooperation within and across government-rebel group dyads in multi-dyadic civil conflicts. It will provide novel insights into how cooperation diffuses across dyads, how past attempts at cooperation influence subsequent cooperative efforts, and how cooperation of one form influences subsequent cooperation of another form. The project advances research on conflict management and settlement by treating cooperation as an iterative process that reveals unique information about combatant credibility that cannot be revealed on the battlefield.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.