# Empirical Decision Theory

arXiv: https://arxiv.org/abs/2512.05677 · HTML: https://mlsanity.com/paper/2512.05677/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 90 references that are in the map.
- First posted on arXiv 5 Dec 2025.

## Builds on (most cited first, 16 of 16)

- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [Contrastive Search Is What You Need For Neural Text Generation](https://mlsanity.com/paper/2210.14140/) (2022)
- [Credal Bayesian Deep Learning](https://mlsanity.com/paper/2302.09656/) (2023)
- [Decoding Decoded: Understanding Hyperparameter Effects in Open-Ended Text Generation](https://mlsanity.com/paper/2410.06097/) (2024)
- [Statistical Comparisons of Classifiers by Generalized Stochastic Dominance](https://mlsanity.com/paper/2209.01857/) (2022)
- [Decision-Making with Belief Functions: a Review](https://mlsanity.com/paper/1808.05322/) (2018)
- [Accounting for Gaussian Process Imprecision in Bayesian Optimization](https://mlsanity.com/paper/2111.08299/) (2021)
- [Robust Statistical Comparison of Random Variables with Locally Varying Scale of Measurement](https://mlsanity.com/paper/2306.12803/) (2023)
- [Statistical Multicriteria Benchmarking via the GSD-Front](https://mlsanity.com/paper/2406.03924/) (2024)
- [Information efficient learning of complexly structured preferences: Elicitation procedures and their application to decision making under uncertainty](https://mlsanity.com/paper/2110.12879/) (2021)
- [Towards Better Open-Ended Text Generation: A Multicriteria Evaluation Framework](https://mlsanity.com/paper/2410.18653/) (2024)
- [Statistical Multicriteria Evaluation of LLM-Generated Text](https://mlsanity.com/paper/2506.18082/) (2025)
- [Multi-Target Decision Making under Conditions of Severe Uncertainty](https://mlsanity.com/paper/2212.06832/) (2022)
- [A Statistical Case Against Empirical Human-AI Alignment](https://mlsanity.com/paper/2502.14581/) (2025)
- [Contributions to the Decision Theoretic Foundations of Machine Learning and Robust Statistics under Weakly Structured Information](https://mlsanity.com/paper/2501.10195/) (2025)
- [Towards Case-Based Preference Elicitation: Similarity Measures on Preference Structures](https://mlsanity.com/paper/1301.7379/) (2013)

## Abstract (arXiv)

Analyzing decision problems under uncertainty commonly relies on idealizing assumptions about the describability of the world, with the most prominent examples being the closed world and the small world assumption. Most assumptions are operationalized by introducing states of the world, conditional on which the decision situation can be analyzed without any remaining uncertainty. Conversely, most classical decision-theoretic approaches are not applicable if the states of the world are inaccessible. We propose a decision model that retains the appeal and simplicity of the original theory, but completely overcomes the need to specify the states of the world explicitly. The main idea of our approach is to address decision problems in a radically empirical way: instead of specifying states and consequences prior to the decision analysis, we only assume a protocol of observed act--consequence pairs as model primitives. We show how optimality in such empirical decision problems can be addressed by using protocol-based empirical choice functions and discuss three approaches for deriving inferential guarantees: (I) consistent statistical estimation of choice sets, (II) consistent statistical testing of choice functions with robustness guarantees, and (III) direct inference for empirical choice functions using credal sets. We illustrate our theory with a proof-of-concept application comparing different prompting strategies in generative AI models.