Recursive Differentiation Arithmetic

This paper introduces <i>Recursive Differentiation Arithmetic</i> (RDA), a formal system that redefines the foundations of arithmetic, geometry, and computation in terms of ontological differentiation rather than set-theoretic or numerical primitives. Instead of assuming numbers, space, or time as given, RDA constructs these structures from stabilized differences within a field of potentiality. The basic elements of RDA are <i>differentiation nodes</i>, which emerge through recursive operations of unfolding and composition. We show how natural numbers arise as a special case of recursive differentiation, and how key physical and informational concepts—such as time, space, energy, and observation—can be reinterpreted ontologically within this framework. A novel model of artificial intelligence is also proposed, where learning and reasoning are modeled as structural transformations of differentiation, rather than statistical adjustment of parameters. RDA thus provides a unified and ontologically grounded perspective on mathematics, physics, and cognition, with implications for the foundations of logic, computation, and artificial mind.

Publication Details

Journal
Figshare
Published
2025-01-01
DOI
https://doi.org/10.6084/m9.figshare.28927412.v1
Primary Topic
Computability, Logic, AI Algorithms
Type
article
Field-Weighted Citation Impact
0.00
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Recursive Differentiation Arithmetic

Figshare
Computability, Logic, AI Algorithms
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Recursive Differentiation Arithmetic

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Abstract

This paper introduces <i>Recursive Differentiation Arithmetic</i> (RDA), a formal system that redefines the foundations of arithmetic, geometry, and computation in terms of ontological differentiation rather than set-theoretic or numerical primitives. Instead of assuming numbers, space, or time as given, RDA constructs these structures from stabilized differences within a field of potentiality. The basic elements of RDA are <i>differentiation nodes</i>, which emerge through recursive operations of unfolding and composition. We show how natural numbers arise as a special case of recursive differentiation, and how key physical and informational concepts—such as time, space, energy, and observation—can be reinterpreted ontologically within this framework. A novel model of artificial intelligence is also proposed, where learning and reasoning are modeled as structural transformations of differentiation, rather than statistical adjustment of parameters. RDA thus provides a unified and ontologically grounded perspective on mathematics, physics, and cognition, with implications for the foundations of logic, computation, and artificial mind.

Figshare
Openalex Percentile: Top 51%
Computability, Logic, AI Algorithms
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