La Fluidoscenza: Paradigma Dinamico della Conoscenza Contemporanea (Fluidoscence: Dynamic Paradigm of Contemporary Knowledge)

Nella tradizione filosofica occidentale la conoscenza è stata prevalentemente concepita come insieme di contenuti stabili, organizzati in strutture definitorie e sistemi concettuali relativamente fissi. Da Platone a Kant, il sapere è stato inteso come entità da fondare, delimitare e stabilizzare. Tra XIX e XXI secolo tale paradigma statico è stato progressivamente eroso da approcci che privilegiano il carattere dinamico, relazionale ed emergente del sapere (Eraclito, Nietzsche, Bergson, Morin). La presente tesi introduce il concetto di fluidoscenza: modello epistemologico secondo cui la conoscenza non è entità statica, bensì processo emergente da sistemi in trasformazione continua nel tempo. Obiettivo duplice: collocarla nel panorama teorico contemporaneo e dimostrarne la capacità di integrare biologia, linguaggio, sistemi complessi, termodinamica, cosmologia e intelligenza artificiale in una cornice unitaria. -English Abstract- This thesis introduces Fluidoscence (Fluidoscenza), a new epistemological paradigm according to which knowledge is not a static entity but an emergent dynamic process arising from systems in continuous transformation over time. Drawing on Heraclitus’ panta rhei, complex systems theory, Prigogine’s dissipative structures, thermodynamics, and contemporary artificial intelligence, the work formalizes the concept through the equation of cognitive flow $ F(t) = \frac{\Delta C(t)}{\Psi(t)} $ where $ \Delta C(t) $ represents cognitive variation and $ \Psi(t) $ the overall friction (internal contradictions + executive resistance) of the system. The paradigm introduces the distinction between Internal Conformity (CI) and Executive Conformity (CE), redefining truth as dynamic conformity between logical structure and real-world executability. It also provides an ontological formalization of the human being as a fluidoscent being: an open dynamic system resulting from the non-linear integration of internal (biological-cognitive) and external (environmental-social-symbolic) flows. Special attention is devoted to applications in education, social systems, and artificial intelligence. In particular, the model proposes the development of “native fluidoscent” AI architectures (inspired by Neural ODEs and Liquid Neural Networks) as a way to overcome the risks of pathological crystallization (over-alignment and mode collapse) typical of current Large Language Models, fostering instead more resilient, adaptive and genuinely creative AI systems.