Discussion of "Bayesian Models and Methods in Public Policy and Government Settings" by S. E. Fienberg

TL;DR

This paper discusses the strengths and limitations of Bayesian models in public policy, emphasizing that no single inferential approach is universally correct due to real-world complexities and uncertainties.

Contribution

It provides a critical perspective on Bayesian methods, highlighting their usefulness and inherent limitations in policy-related data analysis.

Findings

Bayesian models are powerful but have limitations in complex policy contexts.

No universal inferential strategy exists for all real-world questions.

The analogy with George Box emphasizes the practical utility over perfect correctness.

Abstract

Fienberg convincingly demonstrates that Bayesian models and methods represent a powerful approach to squeezing illumination from data in public policy settings. However, no school of inference is without its weaknesses, and, in the face of the ambiguities, uncertainties, and poorly posed questions of the real world, perhaps we should not expect to find a formally correct inferential strategy which can be universally applied, whatever the nature of the question: we should not expect to be able to identify a "norm" approach. An analogy is made between George Box's "no models are right, but some are useful," and inferential systems [arXiv:1108.2177].