Spatio-temporally graded causality: a model
Bartosz Jura

TL;DR
This paper introduces a novel model of causality that is spatio-temporally graded using fuzzy set theory, aiming to reconcile causality with continuous change and address related philosophical and measurement issues.
Contribution
It proposes a non-classical, fuzzy logic-based model of causality that varies in degree, integrating subjective time moments with local causality degrees, bridging classical and Bergsonian duration theories.
Findings
Causality can be modeled as a fuzzy, graded relation.
Subjective moments of time are represented as fuzzy sets.
The model offers potential solutions to measurement and causality problems.
Abstract
In this paper we consider a claim that in the natural world there is no fact of the matter about the spatio-temporal separation of events. In order to make sense of such a notion and construct useful models of the world, it is proposed to use elements of a non-classical logic. Specifically, a model is proposed here, according to which causality can be considered to be spatio-temporally graded. It is outlined how this can be described using the formalism of fuzzy sets theory, with the degree of causality varying between 1, that is no separation between causes and effects, and 0, that is perfect separation between causes and their effects as in classical 'billiard balls' models of physical systems, namely such based on the notion of ideal mathematical point. Our model posits that subjective moments of time are like fuzzy sets, with their extension determined by local degrees of causality,…
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Taxonomy
TopicsData Management and Algorithms · Cognitive Science and Mapping
