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

越来越多地，复杂系统的工程需要考虑复杂的组件网络和它们在不同的社会和技术环境中的相互作用。通过允许在给定的设计下探索结果的潜在空间，模拟可以帮助设计和测试社会技术系统。基于智能体的模型作为一种构建复杂系统模型的方法已经得到了发展，并取得了巨大的成功。代理可以被设计为表示系统组件，并以令人难以置信的详细程度指定它们之间的交互。虽然流行，但由于一系列关键挑战，该方法支持复杂系统工程的全部潜力尚未实现。首先，相对缺乏将基于智能体的模型校准为理论的稳健方法。其次，缺乏可靠的方法来提取粗粒度的系统级信息，因为它出现在基于代理的模拟中。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，当智能体数量众多且行为明确时，基于智能体的模型计算效率低下。总之，这些障碍限制了基于代理的建模的能力，使其无法实现预测、支持决策以及促进复杂系统的设计、控制和优化。这个项目的主要目标是在这些约束之外扩展基于代理的建模的可扩展性。这将通过开发新的计算方法来融合基于代理的建模、不确定性测量和量化以及模式提取的数学来实现。该项目将扩展基于代理的建模能力，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个可以应用于复杂社会行为系统的原型方案，但该项目在各种实质性领域具有潜在的相关性。事实上，我们的中心目标之一是提供一种粘合剂，可以在跨应用领域的基于代理的模拟的各种方案之间建立桥梁。这对于将基于agent的建模整合到更大的数学建模和计算“生态”中非常有用，从根本上扩展了可以在模拟中提出的问题和可以探索的系统的范围，同时更好地将模拟与现实世界的动态联系起来。

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.