Modelplasticity and Abductive Decision Making

TL;DR

This paper introduces a modern approach combining abductive thinking and density-sharpening to identify useful models and make informed decisions despite model imperfections across various empirical fields.

Contribution

It proposes a systematic framework leveraging abductive reasoning and density-sharpening to improve decision-making with imperfect models.

Findings

Provides practical guidelines for model selection and decision-making.

Applies the approach to diverse empirical fields.

Enhances understanding of model usefulness despite inaccuracies.

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.