Emerging Field “Decision Support”

This EF aims to support the development of impact-oriented mathematical models and methods that bridge data science, uncertainty quantification, analysis, control, and decision theory. Key research areas include integrating diverse data sources through both data-driven and mechanistic modeling, representing and quantifying model uncertainty, identifying decision tipping points under external influences like misinformation, enhancing decision processes, and communicating findings effectively to public sector decision makers without formal mathematical backgrounds. Applications of this work stream include public health, opinion dynamics, and green growth, among others.

Previous denomination: Decision Support in the Public Sector (EF6)

Scientists in Charge: Tobias Breiten, Stefan Flasche, Max von Kleist

• • EF-Dec-Supp-1: Bayesian Perspective on Optimal Control under Uncertainty
    Tobias Breiten, Anna Maria Hartkopf, Max von Kleist, Claudia Schillings
  • EF-Dec-Supp-2: Coevolution of Opinion and Epidemic dynamics for Decision Support
    Nataša Djurdjevac Conrad, Max von Kleist, Claudia Schillings, Christof Schütte
  • EF-Dec-Supp-3: Moving Sensor and Actuator Design for Dynamical Systems
    Tobias Breiten, Daniel Walter

Completed projects:

• • EF6-1: Heterogeneous Data Integration to Infer SARS-CoV-2 Variant-Specific Immunity for Risk Assessment and Vaccine Design
    Max von Kleist, Claudia Schillings, René Henrion
  • EF6-2: Deliberation Processes in Citizens’ Assemblies: An Optimal Experimental Design Perspective
    Sven Wang, Jobst Heitzig, Claudia Schillings
  • EF6-3: Labour in an Agent-Based Understanding of Green Growth
    Sarah Wolf