Reckoning with uncertainty: social sciences lessons for mathematical modellingPublication Date: 2022
Saltelli A. (2022). Reckoning with uncertainty, social sciences lessons for mathematical modelling. In Dhersin, J-S., Kaper, H., Ndifon, W., Roberts, F., Rousseau, C. and Ziegler, G. M. (Editors), Mathematics for action: supporting science-based decision-making, UNESCO [62236]. (Online)
Mathematical models can serve society well, as in the example of meteorological forecast models. But not all models are useful. Simple rules can benefit both models and their relationship with society. Model results are conditional on modeling assumptions. The potential outcomes that models project depend on the assumptions they make. Even the best models are affected by uncertainties that aren’t always easy to recognize, understand, acknowledge, or communicate. Opacity about uncertainty damages trust. Modelers need to more effectively and transparently communicate the proper uses and limitations of their models to decision-makers and the public. Likewise, modelers need to communicate an appreciation for, and the public needs to accept, what the numbers in those models really mean and do not mean.