Project StraKoSim (BMBF)
Joint project in cooperation with Saarland University
PIs: Dr. Berend Barkela, Prof. Dr. Michaela Maier, Prof. Dr. Stephan Winter, Prof. Dr. Georg Wenzelburger
Staff: Aidar Zinnatullin, Sebastian Hemesath, Lukas Fock, Signe Filler
This joint project investigates the strategic communication of scientific uncertainties in societal debates and their influence on political decision-making using the example of scientific simulation models.
Scientific modeling and simulations help to better understand complex societal challenges. During the Covid-19 pandemic, for example, simulations were used to estimate the spread of the virus and evaluate the effectiveness of containment measures. However, scientific models are only as good as the assumptions and data on which they are based – therefore, uncertainties must always be taken into account when interpreting the results. In political debates, such uncertainties could be interpreted differently by different stakeholders from science, politics, business, civil society and the media: Those who are stronger supporters of strict containment measures, will probably rate the uncertainties of a prediction in favor of such measures lower than vice versa.
So far, there has been little research on such politicized communication about scientific uncertainties in science communication. Therefore, this project will explore public debates about the uncertainty of simulation models using the example of the Covid-19 pandemic, energy security and biodiversity. Content analyses will be used to investigate whether and how different stakeholders strategically interpret scientific uncertainties. The reasons for such different interpretations will be analyzed in experimental studies. Furthermore, policy process analyses will examine the consequences of such debates for political decision-making. Finally, workshops will be developed to raise awareness for misrepresentations of uncertainties among different stakeholders and to improve their skills to communicate about scientific uncertainties.