Challenges, tasks, and opportunities in modeling agent-based complex systems

TL;DR: In this paper , the authors identify four major knowledge gaps that, to varying degrees, span the entire range of assessment frameworks: (1) causality among component systems and their subsystems is limited; (2) sustainability and resilience are too narrowly defined; (3) social systems are inadequately addressed; and (4) building and urban-scale assessments are poorly connected.

TL;DR: In this article , the authors propose an approach that leverages reinforcement learning and convolutional neural networks to equip agents with the intelligence of self-learning their behavior rules directly from data.

TL;DR: In this article , the authors provide a detailed overview of the relationship between ML and ABM approaches and provide a main scheme for utilizing ML techniques in ABMs. But the authors do not consider the use of data-driven ABM models.

TL;DR: A general framework is used to identify 10 subsystem variables that affect the likelihood of self-organization in efforts to achieve a sustainable SES.

TL;DR: Agent-based modeling is a powerful simulation modeling technique that has seen a number of applications in the last few years, including applications to real-world business problems, and its four areas of application are discussed by using real- world applications.

TL;DR: The capacity to transform at smaller scales draws on resilience from multiple scales, making use of crises as windows of opportunity for novelty and innovation, and recombining sources of experience and knowledge to navigate social-ecological transitions.

TL;DR: Synthesis of six case studies from around the world shows that couplings between human and natural systems vary across space, time, and organizational units and have legacy effects on present conditions and future possibilities.