Abstract Summary
DEFINITION I Agent-based modeling (ABM) is a simulation method. It models individual agents and their interactions to analyze and predict complex systems, offering insights for informed decision-making. Therefore it can be employed in the disciplines of urban planning and data science to understand complicated phenomena in urban environments. PURPOSE I This special session is designed as a learning track in order to introduce the participants to ABM and the potential use cases. Some cases are useful for user oriented urban management which aims to fill the gap between urban environment specialists and behavioral researchers. Some others are used to predict future scenarios and inform and advise policymakers. SPECIFIC KNOWLEDGE I The agents in an ABM are usually programmed to follow simple rules, but when they interact with each other, they can create complex and unpredictable behavior. This is why ABMs are often used to study complex systems because they can help researchers understand how small actions by individuals can have large-scale effects on the system as a whole. Another application is conducting tests with various scenarios to evaluate how the system responds. TECHNIQUES AND PRACTICES I In an ABM, the system being studied is broken down into individual units called agents. Each agent has its own set of rules and behaviors that determine how it interacts with other agents and the environment. In a simulation of a city, agents might represent for example individual people, households, or cars. The session will question how the design of an agent happens and try to come up with new approaches. OBJECTIVES & LEARNING OUTCOMES I The main objectives of this learning track are to introduce a powerful social simulation technique, agent-based modeling, and to demonstrate its use and applicability in research and policy-making. In addition, it should raise the conversation about existing research methodologies introducing ABMs and create the space for participants to brainstorm together on different use cases in their own fields. TEACHING STRATEGIES The workshop would consist of 1. The introductory lecture on the ABM principles by Erkinai Derkenbaeva 2. Presentations of projects that use ABMs in urban contexts including the models by Petar Koljensic, Weronika Sojka, Erkinai Derkenbaeva, and Tanya Tsui. 3. An interactive Q&A session session for additional explanations and discussions. Participants will be able to try to propose ideas for their urban challenges based on the ABM approach.TARGET AUDIENCE I The session aims to address students and professionals willing to understand the basic principles of ABMs and introduce them to their work. There is no limitation regarding the field of research. This workshop can also be addressed to the local authorities and policymakers in order to showcase the possibilities of complex systems simulations.
