Many of the world's most pressing issues, such as climate change, pandemics, financial market stability and fake news, are emergent phenomena that result from the interaction between a large number of strategic or learning agents. Understanding these systems is thus a crucial frontier for scientific and technology development that has the potential to permanently improve the safety and living standards of humanity. Agent-Based Modelling (ABM) (also known as individual-based modelling) is an approach toward creating simulations of these types of complex systems by explicitly modelling the actions and interactions of the individual agents contained within. However, current methodologies for calibrating and validating ABMs rely on human expert domain knowledge and hand-coded behaviours for individual agents and environment dynamics.Recent progress in AI has the potential to offer exciting new approaches to learning, calibrating, validation, analysing and accelerating ABMs. This interdisciplinary workshop is meant to bring together practitioners and theorists to boost ABM method development in AI, and stimulate novel applications across disciplinary boundaries and continents - making ICLR the ideal venue.Our inaugural workshop will be organised along two axes. First, we seek to provide a venue where ABM researchers from a variety of domains can introduce AI researchers to their respective domain problems. To this end, we are inviting a number of high-profile speakers across various application domains. Second, we seek to stimulate research into AI methods that can scale to large-scale agent-based models with the potential to redefine our capabilities of creating, calibrating, and validating such models. These methods include, but are not limited to, simulation-based inference, multi-agent learning, causal inference and discovery, program synthesis, and the development of domain-specific languages and tools that allow for tight integration of ABMs and AI approaches.