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Algorithmic decisions in critical domains such as hiring, college admissions, and lending are often based on rankings. Given the impact of these decisions on individuals, organizations, and population groups, it is essential to understand…

Artificial Intelligence · Computer Science 2025-07-29 Venetia Pliatsika , Joao Fonseca , Kateryna Akhynko , Ivan Shevchenko , Julia Stoyanovich

We provide a formal, simple and intuitive theory of rational decision making including sequential decisions that affect the environment. The theory has a geometric flavor, which makes the arguments easy to visualize and understand. Our…

Machine Learning · Computer Science 2012-02-10 Peter Sunehag , Marcus Hutter

We consider entailment problems involving powerful constraint languages such as guarded existential rules, in which additional semantic restrictions are put on a set of distinguished relations. We consider restricting a relation to be…

Databases · Computer Science 2019-03-21 Antoine Amarilli , Michael Benedikt , Pierre Bourhis , Michael Vanden Boom

LLM-based explainable recommenders can produce fluent explanations that are factually correct, yet still justify items using attributes that conflict with a user's historical preferences. Such preference-inconsistent explanations yield…

Artificial Intelligence · Computer Science 2026-05-08 Chengkai Wang , Baisong Liu

We study probabilistically informative (weak) versions of transitivity, by using suitable definitions of defaults and negated defaults, in the setting of coherence and imprecise probabilities. We represent p-consistent sequences of defaults…

Probability · Mathematics 2015-03-16 Angelo Gilio , Niki Pfeifer , Giuseppe Sanfilippo

The paper deals with partial and weak preference relations defined on infinite-dimensional vector spaces and compatible with algebraic operations. By a partial preference we mean an asymmetric and transitive binary relation, while a weak…

Optimization and Control · Mathematics 2024-01-17 V. V. Gorokhovik

This paper considers the complexity and properties of KLM-style preferential reasoning in the setting of propositional logic with team semantics and dependence atoms, also known as propositional dependence logic. Preferential team-based…

Artificial Intelligence · Computer Science 2025-05-14 Kai Sauerwald , Arne Meier , Juha Kontinen

WIMPs, weakly-interacting massive particles, have been leading candidates for particle dark matter for decades, and they remain a viable and highly motivated possibility. In these lectures, I describe the basic motivations for WIMPs,…

High Energy Physics - Phenomenology · Physics 2023-06-14 Jonathan L. Feng

We consider the challenge of preference elicitation in systems that help users discover the most desirable item(s) within a given database. Past work on preference elicitation focused on structured models that provide a factored…

Artificial Intelligence · Computer Science 2012-07-19 Ronen I. Brafman , Carmel Domshlak , Tanya Kogan

Arrow's Theorem concerns a fundamental problem in social choice theory: given the individual preferences of members of a group, how can they be aggregated to form rational group preferences? Arrow showed that in an election between three or…

Probability · Mathematics 2021-09-27 Frederic Koehler , Elchanan Mossel

In this paper, we study the effect of preferences in abstract argumentation under a claim-centric perspective. Recent work has revealed that semantical and computational properties can change when reasoning is performed on claim-level…

Artificial Intelligence · Computer Science 2022-04-29 Michael Bernreiter , Wolfgang Dvorak , Anna Rapberger , Stefan Woltran

Selective rationalization has become a common mechanism to ensure that predictive models reveal how they use any available features. The selection may be soft or hard, and identifies a subset of input features relevant for prediction. The…

Computation and Language · Computer Science 2019-12-17 Mo Yu , Shiyu Chang , Yang Zhang , Tommi S. Jaakkola

We study the robustness of Bayesian persuasion to uncertainty about the receiver's preferences. We analyze two conceptually distinct notions: continuity, in which only the modeler lacks precise knowledge, but where the model's predictions…

Theoretical Economics · Economics 2026-05-28 Ronen Gradwohl , Fengming Hu , Rann Smorodinsky

Possibility theory offers a framework where both Lehmann's "preferential inference" and the more productive (but less cautious) "rational closure inference" can be represented. However, there are situations where the second inference does…

Artificial Intelligence · Computer Science 2013-02-18 Salem Benferhat , Didier Dubois , Henri Prade

Answer Set Programming (ASP) is a popular framework for modeling combinatorial problems. However, ASP cannot easily be used for reasoning about uncertain information. Possibilistic ASP (PASP) is an extension of ASP that combines…

Artificial Intelligence · Computer Science 2020-02-19 Kim Bauters , Steven Schockaert , Martine De Cock , Dirk Vermeir

The separability problem is formulated in terms of a characterization of a single entanglement witness. More specifically, we show that any (in general multipartite) state \varrho is separable if and only if a specially constructed…

Quantum Physics · Physics 2016-08-17 Piotr Badziąg , Paweł Horodecki , Ryszard Horodecki , Remigiusz Augusiak

Arrow's Impossibility Theorem is a seminal result of Social Choice Theory that demonstrates the impossibility of ranked-choice decision-making processes to jointly satisfy a number of intuitive and seemingly desirable constraints. The…

Theoretical Economics · Economics 2026-04-07 Ori Livson , Mikhail Prokopenko

The purpose of this article is to formulate a number of probabilistic hidden-variable theorems, to provide proofs in some cases, and counterexamples to some conjectured relationships. The first theorem is the fundamental one. It asserts the…

Quantum Physics · Physics 2008-02-03 Patrick Suppes , J. Acacio de Barros , Gary Oas

Choice functions constitute a simple, direct and very general mathematical framework for modelling choice under uncertainty. In particular, they are able to represent the set-valued choices that appear in imprecise-probabilistic decision…

Artificial Intelligence · Computer Science 2019-05-22 Jasper De Bock , Gert de Cooman

A large literature has grown up around the proposed use of 'weak measurements' (i.e., unsharp measurements followed by post-selection) to allegedly provide information about hidden ontological features of quantum systems. This paper…

Quantum Physics · Physics 2017-05-16 R. E. Kastner