A Minimal Model of Bounded Trade-Off Screening in Multi-Attribute Choice
- URL: http://arxiv.org/abs/2606.13201v1
- Date: Thu, 11 Jun 2026 11:12:37 GMT
- Title: A Minimal Model of Bounded Trade-Off Screening in Multi-Attribute Choice
- Abstract summary: We propose a bounded trade-off reasoning framework in which decisions are governed by a screening process that evaluates the balance between gains and losses across attributes.<n>We show that this mechanism produces preference patterns that differ from standard utility-based models and captures context-dependent variation in trade-off behavior.<n>These results establish bounded trade-off screening as a plausible computational mechanism for multi-attribute choice and generate testable predictions for future behavioral studies.
- Score: 12.68567713768202
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Human decision-making often involves choosing between multi-attribute alternatives, yet classical models assume fully compensatory utility aggregation despite evidence that people reject options with poor performance on critical attributes. We propose a bounded trade-off reasoning framework in which decisions are governed by a screening process that evaluates the balance between gains and losses across attributes. The model introduces a trade-off tolerance parameter that controls acceptable imbalance and can vary across contexts. Through simulation, we show that this mechanism produces preference patterns that differ from standard utility-based models and captures context-dependent variation in trade-off behavior. These results establish bounded trade-off screening as a plausible computational mechanism for multi-attribute choice and generate testable predictions for future behavioral studies.
Related papers
- Selective Ensemble Based on Preference-Directed Multi-Objective Bandits [90.75513823660775]
We formalize a sequential decision problem under partially specified linear preferences.<n>We then propose a preference-directed upper confidence bound (PrefUCB) algorithm.<n> Experiments on large pre-trained model selective ensemble tasks and online asset allocation under institutional mandates validate our method.
arXiv Detail & Related papers (2026-06-20T07:52:46Z) - Superficial Beliefs in LLM Decision-Making [17.52079171010035]
We ask whether large language models (LLMs) merely imitate rationales when choosing between two options.<n>We compare the attribute a model says mattered most with the attribute that best explains its choice under a behavioural model fit to prior decisions.<n>We interpret this as evidence for superficial belief'' in LLM decision-making.
arXiv Detail & Related papers (2026-06-09T15:54:35Z) - On Equivariant Model Selection through the Lens of Uncertainty [49.137341292207]
Equivariant models leverage prior knowledge on symmetries to improve predictive performance, but misspecified architectural constraints can harm it instead.<n>We compare frequentist (via Conformal Prediction), Bayesian (via the marginal likelihood), and calibration-based measures to naive error-based evaluation.<n>We find that uncertainty metrics generally align with predictive performance, but Bayesian model evidence does so inconsistently.
arXiv Detail & Related papers (2025-06-23T13:35:06Z) - Recourse under Model Multiplicity via Argumentative Ensembling
(Technical Report) [17.429631079094186]
We name recourse-aware ensembling, and identify several desirable properties which methods for solving it should satisfy.
We show theoretically and experimentally that argumentative ensembling satisfies properties which the existing methods lack, and that the trade-offs are minimal wrt accuracy.
arXiv Detail & Related papers (2023-12-22T22:33:39Z) - Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimation [24.65301562548798]
We study the problem of model selection in causal inference, specifically for conditional average treatment effect (CATE) estimation.
We conduct an empirical analysis to benchmark the surrogate model selection metrics introduced in the literature, as well as the novel ones introduced in this work.
arXiv Detail & Related papers (2022-11-03T16:26:06Z) - Evaluating Sensitivity to the Stick-Breaking Prior in Bayesian
Nonparametrics [85.31247588089686]
We show that variational Bayesian methods can yield sensitivities with respect to parametric and nonparametric aspects of Bayesian models.
We provide both theoretical and empirical support for our variational approach to Bayesian sensitivity analysis.
arXiv Detail & Related papers (2021-07-08T03:40:18Z) - Characterizing Fairness Over the Set of Good Models Under Selective
Labels [69.64662540443162]
We develop a framework for characterizing predictive fairness properties over the set of models that deliver similar overall performance.
We provide tractable algorithms to compute the range of attainable group-level predictive disparities.
We extend our framework to address the empirically relevant challenge of selectively labelled data.
arXiv Detail & Related papers (2021-01-02T02:11:37Z) - Accounting for Unobserved Confounding in Domain Generalization [107.0464488046289]
This paper investigates the problem of learning robust, generalizable prediction models from a combination of datasets.
Part of the challenge of learning robust models lies in the influence of unobserved confounders.
We demonstrate the empirical performance of our approach on healthcare data from different modalities.
arXiv Detail & Related papers (2020-07-21T08:18:06Z) - Semi-nonparametric Latent Class Choice Model with a Flexible Class
Membership Component: A Mixture Model Approach [6.509758931804479]
The proposed model formulates the latent classes using mixture models as an alternative approach to the traditional random utility specification.
Results show that mixture models improve the overall performance of latent class choice models.
arXiv Detail & Related papers (2020-07-06T13:19:26Z) - Decision-Making with Auto-Encoding Variational Bayes [71.44735417472043]
We show that a posterior approximation distinct from the variational distribution should be used for making decisions.
Motivated by these theoretical results, we propose learning several approximate proposals for the best model.
In addition to toy examples, we present a full-fledged case study of single-cell RNA sequencing.
arXiv Detail & Related papers (2020-02-17T19:23:36Z)
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.