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We report an inconsistency found in probability theory (also referred to as measure-theoretic probability). For probability measures induced by real-valued random variables, we deduce an "equality" such that one side of the "equality" is a…

General Mathematics · Mathematics 2017-03-01 Guang-Liang Li , Victor O. K. Li

In this paper we investigate the potential for persuasion arising from the quantum indeterminacy of a decision-maker's beliefs, a feature that has been proposed as a formal expression of well-known cognitive limitations. We focus on a…

Physics and Society · Physics 2018-05-25 Vladimir I. Danilov , Ariane Lambert-Mogiliansky

Traditional interpretations of probability, whether frequentist or subjective, make no reference to the concept of energy. In this paper, we propose that assigning hypothetical energy levels to the outcomes of a random variable can yield…

Physics and Society · Physics 2025-05-29 Yair Neuman , Yochai Cohen

We first show that there are practical situations in for instance forensic and gambling settings, in which applying classical probability theory, that is, based on the axioms of Kolmogorov, is problematic. We then introduce and discuss…

Probability · Mathematics 2015-12-07 Timber Kerkvliet , Ronald Meester

Conformal prediction is a statistically rigorous method for quantifying uncertainty in models by having them output sets of predictions, with larger sets indicating more uncertainty. However, prediction sets are not inherently actionable;…

Machine Learning · Computer Science 2025-02-17 Jesse C. Cresswell , Bhargava Kumar , Yi Sui , Mouloud Belbahri

Model alignment is currently applied in a vacuum, evaluated primarily through standardised benchmark performance. The purpose of this study is to examine the effects of alignment on populations of models through time. We focus on the…

Artificial Intelligence · Computer Science 2026-04-08 Jonathan Elsworth Eicher

In the present article we consider the conjunction fallacy, a well known cognitive heuristic experimentally tested in cognitive science, which occurs for intuitive judgments in situations of bounded rationality. We show that the quantum…

General Physics · Physics 2007-08-30 Riccardo Franco

This survey reviews recent developments in revealed preference theory. It discusses the testable implications of theories of choice that are germane to specific economic environments. The focus is on expected utility in risky environments;…

Theoretical Economics · Economics 2019-12-04 Federico Echenique

We present a formal measure of argument strength, which combines the ideas that conclusions of strong arguments are (i) highly probable and (ii) their uncertainty is relatively precise. Likewise, arguments are weak when their conclusion…

Artificial Intelligence · Computer Science 2017-03-10 Niki Pfeifer , Hanna Pankka

We consider a social choice setting with agents that are partitioned into disjoint groups, and have metric preferences over a set of alternatives. Our goal is to choose a single alternative aiming to optimize various objectives that are…

Computer Science and Game Theory · Computer Science 2021-07-13 Elliot Anshelevich , Aris Filos-Ratsikas , Alexandros A. Voudouris

Evidential reasoning in expert systems has often used ad-hoc uncertainty calculi. Although it is generally accepted that probability theory provides a firm theoretical foundation, researchers have found some problems with its use as a…

Artificial Intelligence · Computer Science 2013-04-15 Robert Fung , Chee Yee Chong

Mathematical Theory of Evidence (MTE), a foundation for reasoning under partial ignorance, is blamed to leave frequencies outside (or aside of) its framework. The seriousness of this accusation is obvious: no experiment may be run to…

Artificial Intelligence · Computer Science 2018-12-10 Mieczysław A. Kłopotek , Sławomir T. Wierzchoń

We determine the quality of randomized social choice mechanisms in a setting in which the agents have metric preferences: every agent has a cost for each alternative, and these costs form a metric. We assume that these costs are unknown to…

Artificial Intelligence · Computer Science 2016-09-27 Elliot Anshelevich , John Postl

This paper examines the relationship between Shafer's belief functions and convex sets of probability distributions. Kyburg's (1986) result showed that belief function models form a subset of the class of closed convex probability…

Artificial Intelligence · Computer Science 2013-04-11 Paul K. Black

We design an expansion of Belnap--Dunn logic with belief and plausibility functions that allow non-trivial reasoning with inconsistent and incomplete probabilistic information. We also formalise reasoning with non-standard probabilities and…

Confirmation bias is a cognitive bias that adversely affects management decisions, and mathematical modelling is an aid to its detailed understanding. Bias in opinion update about the value of a parameter is modelled here assuming that…

Other Statistics · Statistics 2022-02-08 Rose D Baker

Expectation is a central notion in probability theory. The notion of expectation also makes sense for other notions of uncertainty. We introduce a propositional logic for reasoning about expectation, where the semantics depends on the…

Artificial Intelligence · Computer Science 2007-05-23 Joseph Y. Halpern , Riccardo Pucella

Calibration is a common experimental physics problem, whose goal is to infer the value and uncertainty of an unobservable quantity Z given a measured quantity X. Additionally, one would like to quantify the extent to which X and Z are…

High Energy Physics - Phenomenology · Physics 2023-09-26 Rikab Gambhir , Benjamin Nachman , Jesse Thaler

In the single winner determination problem, we have n voters and m candidates and each voter j incurs a cost c(i, j) if candidate i is chosen. Our objective is to choose a candidate that minimizes the expected total cost incurred by the…

Computer Science and Game Theory · Computer Science 2021-11-18 Haripriya Pulyassary , Chaitanya Swamy

This paper examines the concept of a combination rule for belief functions. It is shown that two fairly simple and apparently reasonable assumptions determine Dempster's rule, giving a new justification for it.

Artificial Intelligence · Computer Science 2013-03-08 Nic Wilson
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