Michael Belfrage
Doctoral student

Presentation

Michael Belfrage

I am a Computational Social Scientist dedicated to interdisciplinary work which my background reflects, with a BSc in Political Science and an MSc in Computational Social Science. Currently, I am pursuing a WASP-HS PhD in Computer Science at the Faculty of Technology and Society (TS) here at Malmö University where I am also a part of the Internet of Things and People (IoTaP) Research Center. My research primarily focuses on applying computational methods to political science applications. I have a genuine passion for complex systems and Agent-based Models, as well as network science and other graph-theory-related work.

My other responsibilities include holding positions of trust within the university. I represent the PhD-students at the faculty level on the Board for Research and Research Education for TS, and all PhD-students at MAU on the university level through the Doctoral Student Union.

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 policy-modelling and Agent-based Simulations a more accessible and reliable tool for policy analysis. Our ambition is to understand how Agent-based Social Simulation (ABSS) – a powerful simulative paradigm capable of imitating human behaviour – and artificial intelligence could be leveraged to facilitate evidence-based policy making in complex social systems. This, we hope to achieve, by identifying policy instruments that can change the causal chains in complex social systems to achieve preferable system-level outcomes. By using ABSS to simulate any complex social 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. Our 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 prescriptions can be identified and diverted.