# Making Decisions under Model Misspecification

arXiv: https://arxiv.org/abs/2008.01071 · HTML: https://mlsanity.com/paper/2008.01071/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2020.

## Built on by (most cited first, 3 of 3)

- [Priors in Bayesian Deep Learning: A Review](https://mlsanity.com/paper/2105.06868/) (2021)
- [A view on model misspecification in uncertainty quantification](https://mlsanity.com/paper/2210.16938/) (2022)
- [Mapping the Technological Future: A Topic, Sentiment, and Emotion Analysis in Social Media Discourse](https://mlsanity.com/paper/2407.17522/) (2024)

## Abstract (arXiv)

We use decision theory to confront uncertainty that is sufficiently broad to incorporate "models as approximations." We presume the existence of a featured collection of what we call "structured models" that have explicit substantive motivations. The decision maker confronts uncertainty through the lens of these models, but also views these models as simplifications, and hence, as misspecified. We extend the max-min analysis under model ambiguity to incorporate the uncertainty induced by acknowledging that the models used in decision-making are simplified approximations. Formally, we provide an axiomatic rationale for a decision criterion that incorporates model misspecification concerns.