Coping with Inductive Risk When Theories are Underdetermined: Decision Making with Partial Identification

TL;DR

This paper explores how partial identification in econometrics addresses underdetermined theories and informs credible policy decisions amid scientific uncertainty and inductive risk.

Contribution

It introduces mathematical tools for analyzing partial identification and demonstrates their importance for policy-making under scientific uncertainty.

Findings

Partial identification reveals significant underdetermination and inductive risk.

Tools for characterizing scientific uncertainty improve policy prediction.

Coherent decision-making approaches can be developed without choosing among underdetermined theories.

Abstract

Controversy about the significance of underdetermination of theories persists in the philosophy and conduct of science. The issue has practical import when research is used to inform decision making, because scientific uncertainty yields inductive risk. Seeking to enhance communication between philosophers and researchers who study public policy, this paper describes econometric analysis of partial identification and its use in welfare-economic policy analysis. Study of partial identification finds underdetermination and inductive risk to be highly consequential for credible prediction of important societal outcomes and, hence, for credible public decision making. It provides mathematical tools to characterize a broad class of scientific uncertainties that arise when available data and well-supported assumptions are combined to predict population outcomes. Combining study of partial identification with criteria for reasonable decision making under uncertainty yields coherent approaches to make policy choices without accepting one among multiple empirically underdetermined theories. The paper argues that study of partial identification warrants attention in philosophical discourse on underdetermination and inductive risk.