The Evolutionary Potential Gradient (EPG): A Foundational Framework for Studying Evolvability Capacity in Complex Systems
Year: 2026
Type: article
Abstract: Contemporary science has developed numerous theoretical frameworks for explaining the transformation of complex systems. Evolutionary theory, complexity science, cybernetics, information theory, organizational studies, and institutional adaptation research have provided powerful tools for understanding change, adaptation, and emergence. However, an important question remains insufficiently explored: Why do some systems develop a greater capacity to generate, integrate, and sustain future transformations? The Evolutionary Potential Gradient (EPG) proposes the hypothesis that complex systems differ not only in their current state, performance, or immediate adaptive capacity, but also in a distinct potential property: their ability to increase their own capacity for future transformation. The EPG introduces an additional analytical dimension: the study of evolvability capacity. This framework is structured around six fundamental dimensions: Generation; Selection; Integration; Memory; Plasticity; Orientation. According to the proposed model, the interaction between these dimensions may produce an emergent property: the capacity of a system to progressively develop the mechanisms that allow it to evolve further. To enable empirical investigation, the EPG introduces a synthetic indicator, the Evolutionary Potential Index (PEI), designed as a first attempt to operationalize this capacity. This paper does not present the EPG as an established theory, but rather as a scientific hypothesis and research program aimed at determining whether evolutionary potential constitutes a measurable and predictive dimension of complex systems. Keywords Complex systems; evolution; evolvability capacity; adaptation; innovation; learning; systems theory; artificial intelligence; evolutionary potential.
Author: IRAMBONA, Gael
Institution: Université de Picardie Jules Verne
Language: English
Topic: Complex Systems and Dynamics
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7171301498
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