PRIN 2017
Many decisions in modern societies have a very complex scientific basis. Clinicians have to choose between different drugs for treating a patient. Central bankers have to forecast the evolution of financial markets, and to control the amount of money that circulates in a society. Physicists have to evaluate the impact of continued CO2 emissions for life on the planet. All these decisions are based on the forecasts of scientific models, and sometimes, their predictions reach a great degree of exactness (e.g., in identifying high-risk hospital patients and allocating resources efficiently).
In short, how does science based on uncertain models contribute to good decisions?
Our project investigates the interface between modeling and decision-making. We develop an understanding of how scientific models function, how they advance our knowledge despite their intrinsic uncertainty, and how they are interpreted in a decision context. More specifically, we focus on the following three questions, which correspond to our main targets:
1. How can highly idealized and intrinsically uncertain scientific models be successful in prediction?
2. Why can we trust and accept scientific models in spite of their intrinsic uncertainty and how should we factor in this uncertainty?
3. How should we synthesize actuarial, model-based judgment with human expertise in making practical decisions?
In answering these three questions, our projects integrates foundational philosophical analysis (e.g., rational criteria for theory acceptance), formal and conceptual analysis, and case studies about construction and use of models in a number of relevant scientific disciplines like financial economics and evidence-based medicine.