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Preference modelling lies at the intersection of economics, decision theory, machine learning and statistics. By understanding individuals' preferences and how they make choices, we can build products that closely match their expectations,…

Machine Learning · Computer Science 2026-05-19 Alessio Benavoli , Dario Azzimonti

Consider the seller's problem of finding optimal prices for her $n$ (divisible) goods when faced with a set of $m$ consumers, given that she can only observe their purchased bundles at posted prices, i.e., revealed preferences. We study…

Computer Science and Game Theory · Computer Science 2018-10-09 Ziwei Ji , Ruta Mehta , Matus Telgarsky

Algorithmic recommendation based on noisy preference measurement is prevalent in recommendation systems. This paper discusses the consequences of such recommendation on market concentration and inequality. Binary types denoting a…

Theoretical Economics · Economics 2025-10-21 Andreas Haupt

Literature involving preferences of artificial agents or human beings often assume their preferences can be represented using a complete transitive binary relation. Much has been written however on different models of preferences. We review…

Artificial Intelligence · Computer Science 2018-01-17 Olivier Cailloux , Sébastien Destercke

This paper provides tests for detecting sample selection in nonparametric conditional quantile functions. The first test is an omitted predictor test with the propensity score as the omitted variable. As with any omnibus test, in the case…

Econometrics · Economics 2021-01-08 Valentina Corradi , Daniel Gutknecht

A suggestion for an observational test of the difference between quantum mechanics and noncontextual hidden variables theories requires the measurement of a product of two commuting observables without measuring either observable…

Quantum Physics · Physics 2007-05-23 R. L. Schafir

We present an extension-based approach for computing and verifying preferences in an abstract argumentation system. Although numerous argumentation semantics have been developed previously for identifying acceptable sets of arguments from…

Artificial Intelligence · Computer Science 2024-03-27 Quratul-ain Mahesar , Nir Oren , Wamberto W. Vasconcelos

In this work, we design differentially private hypothesis tests for the following problems in the general linear model: testing a linear relationship and testing for the presence of mixtures. The majority of our hypothesis tests are based…

Statistics Theory · Mathematics 2022-06-30 Daniel Alabi , Salil Vadhan

We consider active learning under incentive compatibility constraints. The main application of our results is to economic experiments, in which a learner seeks to infer the parameters of a subject's preferences: for example their attitudes…

Computer Science and Game Theory · Computer Science 2019-11-15 Federico Echenique , Siddharth Prasad

Diversification represents the idea of choosing variety over uniformity. Within the theory of choice, desirability of diversification is axiomatized as preference for a convex combination of choices that are equivalently ranked. This…

Economics · Quantitative Finance 2016-10-07 Enrico G. De Giorgi , Ola Mahmoud

The ability to uncover preferences from choices is fundamental for both positive economics and welfare analysis. Overwhelming evidence shows that choice is stochastic, which has given rise to random utility models as the dominant paradigm…

General Economics · Economics 2018-11-07 Carlos Alos-Ferrer , Ernst Fehr , Nick Netzer

This paper studies optimal hypothesis testing for nonregular econometric models with parameter-dependent support. We consider both one-sided and two-sided hypothesis testing and develop asymptotically uniformly most powerful tests based on…

Statistics Theory · Mathematics 2025-10-07 Yuya Shimizu , Taisuke Otsu

I propose two U-statistics to test coefficients in generalized linear models. One of them is used to deal with global hypothesis and the other one to test with the nuisance parameter. Both the statistics proposed are within high-dimensional…

Applications · Statistics 2013-12-03 Gong Zi Jiang Nan

A preferential domain is a collection of sets of preferences which are linear orders over a set of alternatives. These domains have been studied extensively in social choice theory due to both its practical importance and theoretical…

Computer Science and Game Theory · Computer Science 2019-02-26 Palash Dey , Swaprava Nath , Garima Shakya

Social choice becomes easier on restricted preference domains such as single-peaked, single-crossing, and Euclidean preferences. Many impossibility theorems disappear, the structure makes it easier to reason about preferences, and…

Computer Science and Game Theory · Computer Science 2025-03-25 Edith Elkind , Martin Lackner , Dominik Peters

This paper investigates the (conditional) quasi-likelihood ratio test for the threshold in MA models. Under the hypothesis of no threshold, it is shown that the test statistic converges weakly to a function of the centred Gaussian process.…

Statistics Theory · Mathematics 2007-06-13 Shiqing Ling , Howell Tong

We study the Popular Matching problem in multiple models, where the preferences of the agents in the instance may change or may be unknown/uncertain. In particular, we study an Uncertainty model, where each agent has a possible set of…

Computer Science and Game Theory · Computer Science 2025-06-06 Gergely Csáji

We are interested in testing general linear hypotheses in a high-dimensional multivariate linear regression model. The framework includes many well-studied problems such as two-sample tests for equality of population means, MANOVA and…

Methodology · Statistics 2018-10-05 Haoran Li , Alexander Aue , Debashis Paul

In this paper, the Gaussian quasi likelihood ratio test (GQLRT) for non-Bayesian binary hypothesis testing is generalized by applying a transform to the probability distribution of the data. The proposed generalization, called…

Methodology · Statistics 2017-11-22 Nir Halay , Koby Todros , Alfred O. Hero

Convex combinations of i.i.d. random variables without a finite mean can behave in a strikingly different way from the finite-mean case: as the weight vector becomes more balanced, the resulting combination may become stochastically larger,…

Methodology · Statistics 2026-03-10 Tommaso Lando , Paulo Eduardo Oliveira