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

2026-01-30

AI Summary

This study addresses the inductive risk arising from the empirical underdetermination of scientific theories, which can undermine the credibility of evidence-based public policy. By integrating the econometric framework of partial identification with philosophical insights on underdetermination, the project develops a formal approach that quantifies predictive uncertainty when no single theory is uniquely supported by the evidence. Leveraging set estimation and robust decision criteria under ambiguity, the method avoids committing to any single theoretical model a priori. Instead, it provides a rigorous, operational pathway for policy design that explicitly accounts for scientific uncertainty. This approach significantly enhances the robustness and credibility of social outcome predictions and policy choices in contexts where empirical evidence insufficiently determines theoretical commitments.

Technology Category

• Reasoning under Uncertainty: Decision/Utility Theory
• Game Theory and Economic Paradigms: Imperfect Information
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Security and Privacy: Large-scale security measurements

Abstract

Controversy about the significance of underdetermination of theories persists in the philosophy and conduct of science. The issue has practical import when scientific research is used to inform decision making, because scientific uncertainty yields inductive risk. Seeking to enhance communication between philosophers and researchers who analyze public policy, this paper describes econometric analysis of partial identification. 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 credible assumptions are combined to predict population outcomes. Combining study of partial identification with criteria for reasonable decision making under ambiguity yields coherent practical 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.

Problem

• underdetermination
• inductive risk
• partial identification
• decision making
• scientific uncertainty

Innovation

• partial identification
• underdetermination
• inductive risk
• decision making under ambiguity
• econometric analysis