Modeling for policy: Challenges for technology assessment from new prognostic methods

Andreas Kaminski; Gabriele Gramelsberger; Dirk Scheer

doi:10.14512/tatup.32.1.10

Authors

Andreas Kaminski Department of Philosophy, Technical University of Darmstadt, Darmstadt (DE) andreas.kaminski@tu-darmstadt.de
Gabriele Gramelsberger Human Technology Center, RWTH Aachen University, Aachen (DE) gramelsberger@humtec.rwth-aachen.de
Dirk Scheer Institute for Technology Assessment and Systems Analysis, Karlsruhe Institute of Technology, Karlsruhe (DE) https://orcid.org/0000-0002-7472-8331 dirk.scheer@kit.edu

DOI:

https://doi.org/10.14512/tatup.32.1.10

Keywords:

computer-based modeling, technology assessment, artificial intelligence, decision-making, prognostic methods

Abstract

Modeling for policy has become an integral part of policy making and technology assessment. This became particularly evident to the general public when, during the COVID-19 pandemic, forecasts of infection dynamics based on computer simulations were used to evaluate and justify policy containment measures. Computer models are also playing an increasing role in technology assessment (TA). Computer simulations are used to explore possible futures related to specific technologies, for example, in the area of energy systems analysis. Artificial intelligence (AI) models are also becoming increasingly important. The results is a mix of methods where computer simulations and machine learning converge, posing particular challenges and opening up new research questions. This Special topic brings together case studies from different fields to explore the current state of computational models in general and AI methods in particular for policy and TA.