EAGER: Demonstration of Scaling Impact on Coalition Formation in Agent-based Simulation

EAGER：在基于代理的模拟中展示对联盟形成的规模影响

• 批准号: 2333570
• 负责人: Andrew Collins
• 金额: $ 19.85万
• 依托单位: Old Dominion University Research Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2333570&HistoricalAwards=false
• 关键词: EAGER; Demonstration; Scaling; Impact; Coalition

This EArly-concept Grants for Exploratory Research (EAGER) award will advance the field of Modeling and Simulation through a series of computational experiments focused on social simulation. Social simulations are used to understand better the large-scale, complex problems that the nation faces such as the obesity epidemic and the housing foreclosure crisis. The significance of this research lies in its potential to help provide a deeper understanding of these complex issues, enabling government officials to make more informed decisions when formulating new policies to tackle these societal challenges. Specifically, this award will examine how individual behavior changes with the number of decision-making agents active in the simulation. Through a partnership with a local medical school, the grant will help provide insight into the issues relating to the increasing placement of doctors in hospitals. This grant is accompanied by an educational plan to develop a cross-disciplinary course on modeling situations with multiple decision-makers at Old Dominion University, a minority-serving institution in southeastern Virginia.This grant will challenge the widespread assumption that behavioral models remain invariant regardless of the size of the social community under study by designing and analyzing a series of computational agent-based simulation modeling experiments. Specifically, the research goal is to demonstrate the impact of scale on coalition formation behavior within an agent-based simulation. A game-theoretical framework will be used for the development of the simulation’s conceptual model, the modeling scenario, and the test dataset. The experiments involve developing behavioral sub-models using machine learning at different scales. The computational experiments will be repeated for various scenarios, game types, and fitness functions. This research hypothesizes that the derived behavioral sub-models will differ substantially between scales in an explainable manner.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.

这项早期概念探索性研究资助 (EAGER) 奖项将通过一系列专注于社会模拟的计算实验来推动建模和模拟领域的发展。社会模拟被用来更好地理解国家面临的大规模、复杂的问题，如肥胖流行和住房止赎危机。这项研究的意义在于它有可能帮助人们更深入地了解这些复杂问题，使政府官员在制定新政策来应对这些社会挑战时能够做出更明智的决策。具体来说，该奖项将研究个人行为如何随着模拟中活跃的决策代理数量而变化。通过与当地医学院合作，这笔赠款将有助于深入了解与医院增加医生安置有关的问题。这笔赠款还附带一项教育计划，旨在开发一门跨学科课程，为弗吉尼亚州东南部的少数族裔服务机构奥多明尼恩大学的多个决策者进行建模。这项赠款将通过设计和分析一系列基于计算代理的模拟建模实验，挑战无论所研究的社会社区规模如何，行为模型保持不变的普遍假设。具体来说，研究目标是在基于代理的模拟中展示规模对联盟形成行为的影响。博弈论框架将用于开发模拟的概念模型、建模场景和测试数据集。这些实验涉及使用不同规模的机器学习来开发行为子模型。计算实验将针对各种场景、游戏类型和适应度函数进行重复。这项研究假设派生的行为子模型将以可解释的方式在不同尺度之间存在显着差异。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。