Michael Belfrage

I am a quantitative researcher dedicated to interdisciplinary work which my background reflects, with a BA in Political Science and an MSc in Computational Social Science. Now, I am enrolled as a WASP-HS PhD student in Computer Science at the Internet of Things and People, here at Malmö University, and get to continue with interdisciplinary work which I am very grateful and excited about! I am an outgoing person who likes reading, new ideas, and having interesting conversations. I have a soft spot for complex systems and dynamic models, network science, and other graph-theory-related work.

Realizing the Potential of Agent-Based Social Simulation for Public Policy

My research project is a part of the larger overarching [WASP-HS project: Realizing the Potential of Agent-Based Social Simulation] ( funded by the Wallenberg Foundations. Together with my supervisors Paul Davidsson and Fabian Lorig and my PhD colleague Emil Johansson, we aim to make computer simulations a reliable and more accessible tool for policy analysis. Our ambition is to understand social systems used by the public and how Agent-Based Social Simulation (ABSS) – a subset of Agent-Based Models focusing solely on social systems – could be leveraged to its full potential for policymaking. This, we hope to achieve, by identifying the mechanisms of public systems and the policy instruments which could be used to regulate them. By using ABSS to simulate the public system of interest, different 'policy-experiments' can be performed by manipulating these mechanisms in-silico. Empirically evaluating policy options is often infeasible, if not impossible, as it requires a lot of time, funds, and personnel. Without accessible analytical tools to provide insights into the system, decision-makers may have little choice but to engage directly with the target system without any information. The aim is to allow decision-makers to explore different policy options before implementing them in the real world. By performing these policy-experiments in safe, simulated environments, unintended consequences associated with different policy interventions can be identified and avoided.

Funding: [The Wallenberg AI, Autonomous Systems and Software Program – Humanities and Society] (