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The aim of "A glance beyond the quantum model" [arXiv:0907.0372] to modernize the Correspondence Principle is compromised by an assumption that a classical model must start with the idea of particles, whereas in empirical terms particles…

Quantum Physics · Physics 2010-02-01 Peter Morgan

Combining abstract to laboratory projected quantum states a general analysis of headline quantum phenomena is presented. Standard representation mode is replaced; instead quantum states sustained by elementary material constituents occupy…

Quantum Physics · Physics 2014-04-03 O. Tapia

Probability theory as a physical theory is, in a sense, the most general physics theory available, more encompassing than relativity theory and quantum mechanics, which comply with probability theory. Taking this simple fact seriously, I…

Popular Physics · Physics 2019-10-02 Louis Vervoort

We introduce a new dataset of logical entailments for the purpose of measuring models' ability to capture and exploit the structure of logical expressions against an entailment prediction task. We use this task to compare a series of…

Neural and Evolutionary Computing · Computer Science 2018-02-26 Richard Evans , David Saxton , David Amos , Pushmeet Kohli , Edward Grefenstette

To make precise the sense in which the operational predictions of quantum theory conflict with a classical worldview, it is necessary to articulate a notion of classicality within an operational framework. A widely applicable notion of…

Quantum Physics · Physics 2021-03-03 David Schmid , John Selby , Elie Wolfe , Ravi Kunjwal , Robert W. Spekkens

Behavioural economics provides labels for patterns in human economic behaviour. Probability weighting is one such label. It expresses a mismatch between probabilities used in a formal model of a decision (i.e. model parameters) and…

Theoretical Economics · Economics 2020-05-04 Ole Peters , Alexander Adamou , Mark Kirstein , Yonatan Berman

In literature on imprecise probability little attention is paid to the fact that imprecise probabilities are precise on a set of events. We call these sets systems of precision. We show that, under mild assumptions, the system of precision…

Statistics Theory · Mathematics 2023-06-06 Rabanus Derr , Robert C. Williamson

A central question for knowledge representation is how to encode and handle uncertain knowledge adequately. We introduce the probabilistic description logic ALCP that is designed for representing context-dependent knowledge, where the…

Artificial Intelligence · Computer Science 2016-07-01 Rafael Peñaloza , Nico Potyka

We describe a mathematical language for determining all possible patterns of contextuality in the dependence of stochastic outputs of a system on its deterministic inputs. The central notion is that of all possible couplings for…

Mathematical Physics · Physics 2015-01-27 Ehtibar N. Dzhafarov , Janne V. Kujala

Abstraction is a powerful idea widely used in science, to model, reason and explain the behavior of systems in a more tractable search space, by omitting irrelevant details. While notions of abstraction have matured for deterministic…

Artificial Intelligence · Computer Science 2020-01-14 Vaishak Belle

By probabilistic logic I mean a normative theory of belief that explains how a body of evidence affects one's degree of belief in a possible hypothesis. A new axiomatization of such a theory is presented which avoids a finite additivity…

Artificial Intelligence · Computer Science 2013-04-10 Romas Aleliunas

Understanding natural language requires common sense, one aspect of which is the ability to discern the plausibility of events. While distributional models -- most recently pre-trained, Transformer language models -- have demonstrated…

Computation and Language · Computer Science 2021-04-22 Ian Porada , Kaheer Suleman , Adam Trischler , Jackie Chi Kit Cheung

Although the foundations of quantum and classical physics are much different, it is often difficult to pinpoint which features of a particular system are intrinsically "quantum". Perhapse, the most clear-cut distinction between "classical"…

Quantum Physics · Physics 2015-02-05 Piotr Szańkowski

The purpose of this paper is to develop further the main concepts of Phenomena Dynamic Logic (P-DL) and Cognitive Dynamic Logic (C-DL), presented in the previous paper. The specific character of these logics is in matching vagueness or…

Logic in Computer Science · Computer Science 2016-11-17 Evgenii Vityaev , Boris Kovalerchuk , Leonid Perlovsky , Stanislav Smerdov

We seek to find normative criteria of adequacy for nonmonotonic logic similar to the criterion of validity for deductive logic. Rather than stipulating that the conclusion of an inference be true in all models in which the premises are…

Artificial Intelligence · Computer Science 2007-05-23 Henry E. Kyburg , Choh Man Teng

Recent success of Bayesian methods in neuroscience and artificial intelligence gives rise to the hypothesis that the brain is a Bayesian machine. Since logic, as the laws of thought, is a product and practice of the human brain, it leads to…

Artificial Intelligence · Computer Science 2021-01-28 Hiroyuki Kido

The framework of generalized probabilistic theories is a powerful tool for studying the foundations of quantum physics. It provides the basis for a variety of recent findings that significantly improve our understanding of the rich physical…

Quantum Physics · Physics 2014-08-14 Peter Janotta , Haye Hinrichsen

We provide self-contained proof of a theorem relating probabilistic coherence of forecasts to their non-domination by rival forecasts with respect to any proper scoring rule. The theorem appears to be new but is closely related to results…

Machine Learning · Statistics 2016-11-15 Joel Predd , Robert Seiringer , Elliott H. Lieb , Daniel Osherson , Vincent Poor , Sanjeev Kulkarni

Two major difficulties in using default logics are their intractability and the problem of selecting among multiple extensions. We propose an approach to these problems based on integrating nommonotonic reasoning with plausible reasoning…

Artificial Intelligence · Computer Science 2013-04-08 Piero P. Bonissone , David A. Cyrluk , James W. Goodwin , Jonathan Stillman

We study the problem of assessing the robustness of counterfactual explanations for deep learning models. We focus on $\textit{plausible model shifts}$ altering model parameters and propose a novel framework to reason about the robustness…

Machine Learning · Computer Science 2024-07-11 Luca Marzari , Francesco Leofante , Ferdinando Cicalese , Alessandro Farinelli
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