Controlling and Utilizing Uncertainty in the Health Sciences

Project Description

Uncertainty threatens the reliability of scientific results and inferences in the health sciences as well as the efficacy of public health policies. Uncertainty arises from the awareness that different flaws may affect evidence sampling, formalization, and reporting. The methodological literature offers a rich variety of instruments to address many sorts of uncertainty affecting scientific inference. These tools are extremely important and indispensable; yet, it is precisely because of the quantity, variety, and sophistication of these instruments, and because of their heterogeneous theoretical bases, that a foundational reflection on this sort of topic may considerably contribute to their correct implementation and wider use in policy-making and decision making in general.

Philosophy of science and epistemology have been long contributing to this theme comprehensively. These contributions may help science and methodology to progress in three ways:

1. Provide the rationale for the “correctness” or “truth-conduciveness” of the methodological techniques developed so far, in order to track and measure uncertainty.

2. Help develop additional tools for types of uncertainty which have not yet been “operationalized” by methodologists.

3. Build a comprehensive framework where such tools can be combined and put them into perspective (a taxonomy of uncertainty for a taxonomy of methods to track it).

Recent developments in formal epistemology and logic provide promising methods to address these issues. They enable one to represent the components of a scientific model, to determine its causal relations, and to operate formally on it, isolating the sources of uncertainty and weighing the impact of critical factors (biases, errors, etc.). The goal of this project is to draw on these developments in order to:

1. Analyze and categorize the different sources of uncertainty in the health sciences.

2. Draw on recent advances in Bayesian epistemology, causal modeling, logic, and AI to make these sources explicit and technically controllable.

3. Systematically explore the role of these sources of uncertainty for confirmation, further evolve these models, and develop new strategies to make scientific inferences more reliable.

Benefits: On the theoretical side, the project promises to improve the accuracy of our interpretation of scientific data and of the resulting models. On the practical side, having developed a principled way to minimize or utilize uncertainty in the interpretation of data, will also provide a rigorous method to reduce the risks in developing health policies based on data and results that are usually connotated by severe epistemic uncertainty. To make these results practically applicable, we develop a prototype software that allows scientists and policymakers to input known and unknown sources of uncertainty and to compute their effects on the confirmatory impact of the available evidence.

Project Members (Ancona Unit)

Project Members (Turin Unit)