Decision-making with E-admissibility given a finite assessment of
choices
- URL: http://arxiv.org/abs/2204.07428v1
- Date: Fri, 15 Apr 2022 11:46:00 GMT
- Title: Decision-making with E-admissibility given a finite assessment of
choices
- Authors: Arne Decadt and Alexander Erreygers and Jasper De Bock and Gert de
Cooman
- Abstract summary: We study the implications for decision-making with E-admissibility.
We use the mathematical framework of choice functions to specify choices and rejections.
We provide an algorithm that computes this extension by solving linear feasibility problems.
- Score: 64.29961886833972
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Given information about which options a decision-maker definitely rejects
from given finite sets of options, we study the implications for
decision-making with E-admissibility. This means that from any finite set of
options, we reject those options that no probability mass function compatible
with the given information gives the highest expected utility. We use the
mathematical framework of choice functions to specify choices and rejections,
and specify the available information in the form of conditions on such
functions. We characterise the most conservative extension of the given
information to a choice function that makes choices based on E-admissibility,
and provide an algorithm that computes this extension by solving linear
feasibility problems.
Related papers
- Extending choice assessments to choice functions: An algorithm for computing the natural extension [45.238324742678124]
We study how to infer new choices from prior choices using the framework of choice functions.
In particular, we define the natural (most conservative) extension of a given choice assessment to a coherent choice function.
arXiv Detail & Related papers (2024-07-30T20:10:59Z) - End-to-End Learning for Fair Multiobjective Optimization Under
Uncertainty [55.04219793298687]
The Predict-Then-Forecast (PtO) paradigm in machine learning aims to maximize downstream decision quality.
This paper extends the PtO methodology to optimization problems with nondifferentiable Ordered Weighted Averaging (OWA) objectives.
It shows how optimization of OWA functions can be effectively integrated with parametric prediction for fair and robust optimization under uncertainty.
arXiv Detail & Related papers (2024-02-12T16:33:35Z) - Predict-Then-Optimize by Proxy: Learning Joint Models of Prediction and
Optimization [59.386153202037086]
Predict-Then- framework uses machine learning models to predict unknown parameters of an optimization problem from features before solving.
This approach can be inefficient and requires handcrafted, problem-specific rules for backpropagation through the optimization step.
This paper proposes an alternative method, in which optimal solutions are learned directly from the observable features by predictive models.
arXiv Detail & Related papers (2023-11-22T01:32:06Z) - Generalizing Bayesian Optimization with Decision-theoretic Entropies [102.82152945324381]
We consider a generalization of Shannon entropy from work in statistical decision theory.
We first show that special cases of this entropy lead to popular acquisition functions used in BO procedures.
We then show how alternative choices for the loss yield a flexible family of acquisition functions.
arXiv Detail & Related papers (2022-10-04T04:43:58Z) - Bounding Counterfactuals under Selection Bias [60.55840896782637]
We propose a first algorithm to address both identifiable and unidentifiable queries.
We prove that, in spite of the missingness induced by the selection bias, the likelihood of the available data is unimodal.
arXiv Detail & Related papers (2022-07-26T10:33:10Z) - Black-box Selective Inference via Bootstrapping [5.960626580825523]
Conditional selective inference requires an exact characterization of the selection event, which is often unavailable except for a few examples like the lasso.
This work addresses this challenge by introducing a generic approach to estimate the selection event, facilitating feasible inference conditioned on the selection event.
arXiv Detail & Related papers (2022-03-28T05:18:21Z) - Choice Set Confounding in Discrete Choice [29.25891648918572]
Existing learning methods overlook how choice set assignment affects the data.
We adapt methods from causal inference to the discrete choice setting.
We show that accounting for choice set confounding makes choices observed in hotel booking more consistent with rational utility-maximization.
arXiv Detail & Related papers (2021-05-17T15:39:02Z) - Inference with Choice Functions Made Practical [1.1859913430860332]
We study how to infer new choices from previous choices in a conservative manner.
We use the theory of choice functions: a unifying mathematical framework for conservative decision making.
arXiv Detail & Related papers (2020-05-07T12:58:05Z) - Archimedean Choice Functions: an Axiomatic Foundation for Imprecise
Decision Making [0.9442139459221782]
We focus on two generalisations that apply to sets of probability measures: E-admissibility and maximality.
We provide a set of necessary and sufficient conditions on choice functions that uniquely characterises this rule.
A representation theorem for Archimedean choice functions in terms of coherent lower previsions lies at the basis of both results.
arXiv Detail & Related papers (2020-02-12T19:44:08Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.