Model Aptness, Explanation, and Prediction: What is decision-relevant information and how do we get it from scientific models?

Résumé

It is, perhaps, not even worth saying that scientific investigation has value in and of itself. However, what value does scientific investigation have beyond that putative intrinsic value? The standard response is that decisions and policies that are based on our best scientific theories will generally lead to better outcomes than those are not so informed. In other words, the pursuit of scientific goals also has instrumental value in its ability to provide decision-relevant information. Philosophers of science have identified two broad goals in science – explanation and prediction. Which of those goals, when achieved, is best suited for providing decision-relevant information to decision/policy makers? Moreover, given that much of science is model-based, which theory of model-aptness best supports the extraction of decision-relevant information? Given the nature of challenges facing the world today (e.g., global climate change, maintaining food-energy-water security, etc.) answering these questions is important. Dr. Michael Goldsby will explore these issues and point to how philosophers of science can help with this task.