Fabian Lorig.

Title

Agent-based Policy Modelling – Enhancing Decision Making using Social Simulations

About

Anticipating the effects of policies is challenging, as their success depends on how the affected individuals respond to the interventions. Social systems consist of distributed interacting parts, making it difficult for traditional models to fully capture the complexity of human behavior and to predict the dynamics of the system. As a result, policies may not always achieve their intended outcome, be ineffective, or even lead to unintended consequences.

Agent-Based Simulations offer a bottom-up approach to modeling societal dynamics using artificial populations consisting of so called agents, which imitate human behavior and decision making. This provides policy makers with a safe environment to test policy interventions under different conditions before implementing them in the real world. By simulating potential cascading or secondary effects as well as long-term system dynamics, Agent-Based Simulations can help to enhance policy robustness.

This lecture will explore the potential of Agent-Based Simulations to support policy making across different domains such as public transportation, crisis response, and healthcare, without the risk of harming people. We will discuss real-world applications and address methodological challenges that must be overcome to realize the full potential of Agent-based Policy Modelling.

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