Agent-Based Modeling (ABM) is a powerful technique for simulating real-world systems, including organizations, transportation networks, supply chains, financial markets, war-zones, and ecological environments. These models are populated with artificial agents designed to replicate the behavior of real-world actors such as customers, traders, vehicles, aircrafts, militias, and Agentic AIs.

This workshop will teach you how to build, calibrate, and deploy large-scale agent-based models to solve real-world problems. The workshop is open to participants from industry and academia. Proficiency in programming with Python, C++, Java, or another general-purpose programming language is a prerequisite.

The workshop will be lead by Robert Axtell of George Mason University. Axtell, a student of Nobel Laureate Herbert Simon, is the one of the pioneers of large-scale agent-based modelling. He has been affiliated with the Santa Fe Institute, MIT, Oxford, and the Brookings Institution. Axtell’s agent-based models have been used to allocate workers in Disneyland, reduce tick size in NASDAQ, understand the growth of firms in a large economy, and even figure out why the Anasazi civilisation declined. Other speakers at the workshop include Anil Nelakanti of Amazon, Tarun Rambha of IISc, Parth Shah of ISPP, Vipin Veetil of IIM Kozhikode, and Rakesh Warier of NIT Calicut. These speakers will share their expertise in areas as wide as modeling traffic flows, managing supply-chain disruptions, allocating online advertisement space, and developing novel public-policy solutions.

To know more about agent-based modeling, see the following article in the Harvard Business Review https://hbr.org/2002/03/predicting-the-unpredictable