EAGER/Collaborative Research: Accelerating Innovation in Agent-Based Simulations: Application to Complex Socio-Behavioral Phenomena

EAGER/协作研究：加速基于代理的模拟创新：在复杂社会行为现象中的应用

• 批准号: 1002469
• 负责人: Yannis Kevrekidis
• 金额: $ 3万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-02-01 至 2011-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1002469&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Research; Accelerating; Innovation

Increasingly, the engineering of complex systems requires consideration of an intricate web of components and their interaction in diverse social and technical environments. Simulation can assist in designing and testing socio-technical systems by allowing the potential space of outcomes to be explored under given designs. Agent-based models have been developed as a method for building models of complex systems, with great success. Agents may be designed to represent system components and to specify the interactions between them in an incredible level of detail. While popular, the full potential of the methodology to support engineering of complex systems has not been reached, however, because of a set of key challenges. First, there exists a relative lack of robust methods for calibrating agent-based models to theory. Second, there is a paucity of reliable approaches for extracting coarse-grained, system level information as it emerges in agent-based simulations. Third, there is a dearth of schemes for handling uncertainty in the application of agent-based rules to system behavior. Fourth, computation of agent-based models is inefficient when agents are numerous in volume and richly-specified in behavior. Together, these impediments constrain the ability of agent-based modeling to enable prediction, to support decisions, and to facilitate the design, control, and optimization of complex systems. The main objective of this project is to broaden the extensibility of agent-based modeling beyond these constraints. This will be achieved by developing novel computational methods to fuse agent-based modeling, uncertainty measurement and quantification, and mathematics for pattern-extraction. This project will expand the capabilities of agent-based modeling in supporting the design, engineering, and testing of complex systems. Our initial focus is to develop a prototype scheme that can be applied to complex socio-behavioral systems, but the project is of potential relevance across a diverse array of substantive areas. Indeed, one of our central aims is to provide the glue that can bridge diverse schemes for agent-based simulation across application areas. This could be incredibly useful in reconciling agent-based modeling into a larger ?ecology? of mathematical modeling and computation, fundamentally expanding the range of questions that can be posed and systems that can be explored in simulation, while better linking simulation to real-world dynamics.

复杂系统的工程越来越多地需要考虑复杂的组件网络及其在各种社会和技术环境中的相互作用。模拟可以通过在给定设计下探索结果的潜在结果来帮助设计和测试社会技术系统。基于代理的模型已被开发为建立复杂系统模型的方法，并取得了巨大成功。可以设计代理代表系统组件并以令人难以置信的细节级别指定它们之间的相互作用。尽管很受欢迎，但由于一系列关键挑战，该方法的全部支持支持复杂系统的工程的全部潜力尚未达到。首先，存在相对缺乏可靠的方法来将基于剂的模型校准理论。其次，在基于代理的模拟中出现时出现的粗粒，系统级信息的可靠方法很少。第三，在将基于代理的规则应用于系统行为时，缺乏处理不确定性的方案。第四，当代理在体积众多并在行为上占有丰富的指定时，基于代理模型的计算效率低下。这些障碍共同限制了基于代理的建模能够实现预测，支持决策并促进复杂系统的设计，控制和优化的能力。该项目的主要目的是将基于代理的建模的可扩展性扩大到这些约束之外。这将通过开发新的计算方法来融合基于代理的建模，不确定性测量和定量以及用于模式拔除的数学。该项目将扩大基于代理建模的功能，以支持复杂系统的设计，工程和测试。我们最初的重点是开发一种可以应用于复杂的社会行为系统的原型方案，但是该项目具有各种实质领域的潜在相关性。的确，我们的中心目标之一是提供可以在跨应用领域桥接基于代理的模拟的各种方案的胶水。这在将基于代理的建模与更大的生态学相结合时可能非常有用？在数学建模和计算中，从根本上扩展了可以提出的问题范围，并可以在模拟中探索的系统，同时更好地将仿真链接到现实世界动态。