Modelplasticity and Abductive Decision Making

Abstract

`All models are wrong but some are useful' (George Box 1979). But, how to find those useful ones starting from an imperfect model? How to make informed data-driven decisions equipped with an imperfect model? These fundamental questions appear to be pervasive in virtually all empirical fields -- including economics, finance, marketing, healthcare, climate change, defense planning, and operations research. This article presents a modern approach (builds on two core ideas: abductive thinking and density-sharpening principle) and practical guidelines to tackle these issues in a systematic manner.

Final accepted version. The supplementary section contains some notes on the connections and differences between the Bayesian statistical approach vs. the Abductive statistical approach to model misspecification, robustness, and decision-making