Supercomplex Knowledge

Abstract

Contemporary science stands at a historical threshold. The accumulation of data, disciplinary hyper-fragmentation, and the economic instrumentalization of knowledge have revealed the limits of a paradigm that, although extraordinarily productive, now appears epistemically and ethically exhausted (Prigogine, From Being to Becoming; Morin, La Méthode).

In this context, Supercomplex Knowledge (SK) is proposed as a catalytic metatheory capable of integrating, accelerating, and reorganizing the dispersed conceptual flows of complexity theories. Unlike critical schools that defined themselves in opposition to the scientific mainstream, SK adopts a synergistic strategy: it incorporates the achievements of the experimental method, thermodynamics, cybernetics, data science, and artificial intelligence—the true engines of modern thought—and expands them into a new ontological and axiological combinatory framework.

Its theoretical core is constituted by the triad Energy Flows (EF), Structural Morphologies (SM), and Temporal Connectivity (TC), conceived as a relational and dynamic grammar of reality. This triad allows any phenomenon—physical, biological, social, or technological—to be translated into a single structural language in which energy circulates, forms emerge, and temporal links are calibrated according to their conditions of interaction.

On this basis, SK articulates six central theses that mark the transition from reductionist science to supercomplex governance: a model of knowledge oriented not only toward prediction and control, but also toward planetary survival and well-being, guided by explicit criteria of resilience, diversity, sustainability, and justice (Capra & Luisi; Maldonado).

Rather than replacing theories, SK reactivates them within a shared relational field. Its horizon is not rupture, but the expansion of the mainstream toward a more inclusive, evolutionary, and ethically oriented paradigm.

Development

The genealogy of complexity can be read as a succession of attempts to overcome mechanism without renouncing rigor. From the dissipative structures of Prigogine and Nicolis (1977), through non-equilibrium thermodynamics, to Maturana and Varela's (1984) autopoiesis and Kauffman's (2000) evolutionary models, twentieth-century science revealed the impossibility of purely linear knowledge. However, this expansive movement remained fragmented, lacking a coherent framework that could integrate its various findings.

SK arises as a response to that dispersion. Instead of adding yet another school, it offers a second-order metatheory that organizes existing theoretical diversity under a principle of dynamic coherence. If complexity described systems, SK describes how theories describe systems, granting a form of epistemological self-awareness to science itself (Morin; Maldonado).

Its catalytic nature means that it does not impose hierarchy but instead accelerates connections between disciplines and makes hidden emergences visible. In Latour's terms (2013), SK establishes an "ecology of modes of existence" that replaces substantialist ontology with a network of relations. From this perspective, the classical scientific method is not discarded but reinterpreted as a transdisciplinary strategy: observation and falsification are expanded through simulations, probabilistic models, and four-dimensional (4D) representations that allow for the integration of scales, temporalities, and multiple feedbacks. Conceptual and technical instruments—such as Adaptive Dynamic Maps (ADM) or the COMPLEX CUORE software—embody this transformation: moving from static measurement to evolutionary relational mapping.

Similarly, the philosophy of science regains its central role as a mediator between knowledge and meaning. Where positivism limited itself to recording facts, SK reclaims the act of interpreting the relations that make those facts possible. In dialogue with Donna Haraway (2008), knowledge ceases to be a "view from nowhere" and becomes a situated practice, aware of its own insertion within the system it analyzes.

Finally, SK redefines the purpose of science: to know in order to sustain the systemic coherence of life. Epistemology reconnects with axiology by integrating criteria of resilience, sustainability, and well-being as internal variables within models of prediction and decision-making. Knowledge ceases to be an instrument of domination and becomes an architecture of relational calibration among systems. In accordance with Capra and Luisi (2014), life is understood not as an object of study but as the operational context of knowledge. For SK, life, mind, and technology are manifestations of a single energetic, structural, and temporal grammar.

The Founding Interrogatives of Supercomplex Knowledge

These three interrogatives form a dynamic and axial triptych. They configure a framework where complexity and supercomplexity are more than sophisticated adjectives; they are an ontological requirement: the universe is complex (parts that irreducibly co-define each other) and supercomplex (capable of sustaining productive paradoxes without collapsing). These are not preliminary questions or problems to be solved immediately, but rather minimum ontological conditions for thinking about the universe without reducing it.

Central Theses of Supercomplex Knowledge

Abstract

The second chapter examines the trajectory of the modern scientific mainstream, from its foundational achievements to its current crisis of coherence. The experimental method, thermodynamics, cybernetics, and data science constituted the architectures of modern knowledge, yet their reductionist orientation prevented them from grasping the interactive and emergent dynamics of complex systems.

In contrast, Supercomplex Knowledge (SK) emerges as an integrative metatheory, capable of reorganizing the contributions of the mainstream within a new epistemological and axiological combinatorics.

More than a rupture, SK represents a methodological threshold: it expands modern science through principles of circular multicausality, multiscalar analysis, and complex constructivism, incorporating unprecedented categories—relational ontologies, adaptive dynamic maps, supercomplex equations—that allow the articulation of knowledge, ethics, and planetary survival within a single cognitive framework.

Development

Since Galileo and Newton, modern science adopted analytical simplification as its privileged method: isolating variables, formulating linear laws, and seeking the minimal explanatory unit (Koyré 1968). This path—a strategy of progressive specialization—proved extraordinarily productive for well-defined problems, enabling unprecedented technical advances. However, when it became the exclusive lens of knowledge, this method generated a countereffect: the inability to grasp the relational dynamics and irreducibility of complex systems (Prigogine and Stengers 1984). Its limit is not only methodological but ontological: it assumes that the world is composed of separate parts, when in reality it is woven by interdependent relations.

This gesture of separation—useful in its historical context—ended up dissolving the unity among energy, form, and time, which constitute the real fabric of systems. From that fragmentation arose modern ontological blindness: the tendency to see objects where there are processes, structures where there are flows, substances where there are interactions.

Ultimately, every system—whether in its internal (intrasystemic) dynamics or in its interaction with others (intersystemic)—manifests complex behaviors. The proposal of Supercomplex Knowledge is not to invalidate the achievements of the classical method, but to broaden its horizon, recognizing that contemporary problems (ecological crises, pandemics, structural inequalities) demand a multicausal, circular, and combinatory science capable of integrating stability and emergence.

For decades, the scientific mainstream did not reject complexity: it tolerated it. But tolerating is not integrating. Tolerating means keeping at a distance that which, if it entered the epistemological core, would force the rewriting of fundamental notions such as causality, isolated system, independent variable, linear prediction, and experimental method. Complexity remained confined to a safe perimeter—peripheral discourses, specific applications, fashionable metaphors—without affecting the analytical matrix inherited from the seventeenth century. The SK is born precisely when this distance becomes unsustainable: it does not accept a domesticated complexity, but rather integrates it as a constitutive principle of knowing, intervening, and designing systems in a universe where energy, morphology, and time are co-determined at every scale.

Had the modern method been complemented from its origins by relational perspectives, science would have better anticipated its own crises, generated deeper interdisciplinary innovations, and maintained a more human orientation. Supercomplex Knowledge inherits and reorganizes that legacy: it seeks to restore the lost continuity among energy flows, structural morphologies, and temporal connectivity.

1. Science and Social Context: A Historical Coevolution with Creative Tensions

The mode of knowing that took shape in modernity was not neutral: it evolved in synchrony with a mode of production. Since the seventeenth century, the expansion of capitalism and the institutionalization of scientific knowledge configured a coevolutionary alliance marked by fertile tensions. Francis Bacon conceived knowledge as power aimed at the domination of nature (Novum Organum, 1620); Max Weber analyzed scientific rationalization as an expression of the spirit of capitalism (The Protestant Ethic and the Spirit of Capitalism, 1905); and Karl Marx showed how science became a direct productive force capable of multiplying capital (Capital, 1867).

Within this interdependence, knowledge became administered social energy—a flow that fueled industrial and technological expansion. Modern epistemology was, in this sense, a technology of order and efficiency: it turned uncertainty into predictability, and life into resource.

In contrast to this paradigm of domination, voices of balance emerged. Robert Merton (1973) defended the internal norms of science—universalism, communalism, disinterestedness, and organized skepticism—as antidotes to the economic instrumentalization of knowledge. Later, Herbert Marcuse (One-Dimensional Man, 1964) and Michel Foucault (Discipline and Punish, 1975) revealed how scientific knowledge intertwined with power structures, defining what is normal and true according to utility. Ulrich Beck (1986) synthesized this ambivalence under the concept of the risk society, in which the same rationality that promises security generates new planetary vulnerabilities. Capital required a form of knowledge that legitimized accumulation, and science found in capital the means for its own expansion.

This relationship should not be viewed merely as dependency but as energetic coevolution: science provided form, capital supplied flow, and together they shaped a global structural morphology based on growth. Supercomplex Knowledge (SK) now proposes a new phase of this coevolution: to redirect the flows of cognitive, economic, and technological energy toward synergy and planetary well-being, rather than extraction and domination.

2. The Contributions of the Mainstream and Its Limits in the Face of Complexity

Modern science has bequeathed decisive contributions to contemporary thought. Within its experimental matrix, genuine conceptual gems were consolidated:

• The scientific method, which established a discipline of verification and falsification (Popper, The Logic of Scientific Discovery, 1959).
• Thermodynamics and cybernetics, which introduced the notions of energy flow, control, and feedback (Wiener, Cybernetics, 1948).
• Data science, which made it possible to extract patterns from large volumes of information and to systematize statistical correlation (Anderson, 2008).

These contributions, reinterpreted from the perspective of Supercomplex Knowledge (SK), form the implicit genealogy of its ontological triad: thermodynamics anticipated Energy Flows (EF), cybernetics modeled Structural Morphologies (SM), and data science unfolded Temporal Connectivities (TC). Each illuminated a dimension of reality, but their isolation produced a collateral effect: the fragmentation of knowledge.

Mainstream science reached an extraordinary level of precision—but at the cost of multicausal reduction and the loss of global meaning. Its obsession with quantitative exactitude led to a knowledge that is efficient yet partial, incapable of representing the nonlinear interactions and retrocausal loops that characterize life and thought (Pearl & Mackenzie, The Book of Why, 2018). The contemporary AI industry reproduces this pattern. Based on statistics, formal logic, deep neural networks, and high-performance computing, it privileges what can be measured: metrics, benchmarks, scalability. Scientific validity is conflated with market validation, and private investment dictates the direction of research. The result is an epistemology of performance—knowledge designed to maximize efficiency and monetization rather than understanding and sustainability.

The risk of this orientation is epistemological monocausality: the belief that there is only one valid way to organize knowledge—the one that optimizes prediction. Yet life, the universe, and thought are emergent systems, where multiple scales, times, and energies interact without a single center of control.

From the standpoint of SK, the alternative is not to reject deep learning, but to expand it—to transform it into supercomplex learning, capable of integrating multiscalarity, axiological feedback, and contextual sensitivity. This learning does not merely reproduce past patterns; it coevolves with its environment, recognizing the distributed intelligence of natural and social systems. In this sense, SK inaugurates a new stage of knowledge: from prediction to coevolution, from data to relational wisdom.

3. Contributions and Limitations of Previous Complex Approaches

Throughout the twentieth century, multiple attempts emerged to overcome scientific reductionism. Ilya Prigogine introduced irreversibility as a constitutive principle of nature (Prigogine & Stengers, La Nouvelle Alliance, 1979); Stuart Kauffman explored self-organization and the "adjacent possible" as the evolutionary engine of life (The Origins of Order, 1993); and Edgar Morin formulated a Complex Thought that sought to reintegrate subject, knowledge, and world (La Méthode, 1977–2004). These contributions marked a historical turning point. However, they also revealed a divide that Supercomplex Knowledge (SK) seeks to repair: while the Sciences of Complexity developed mathematical formalizations without a clear relational ontology, Complex Thought achieved philosophical depth but lacked operational tools.

Morin opened an essential horizon by understanding that "the part is in the whole and the whole in the part." His dialogics, hologrammaticity, and epistemological recursion introduced an ethics of open thinking. Yet his proposal remained, to a large extent, conceptual. SK shares his integrative spirit but advances toward a combinatorial formalization: it transforms the idea of relation into dynamic equations among Energy Flows (EF), Structural Morphologies (SM), and Temporal Connectivities (TC). Where Complex Thought invites us to think about interdependence, SK models it, simulates it, and intervenes through tools such as Adaptive Dynamic Maps and the COMPLEX CUORE software.

Moreover, SK incorporates an explicit axiology—survival and well-being—absent in Morin, who, despite his humanism, did not formulate operational criteria to assess the health or sustainability of systems. Within the supercomplex framework, ethics is not an exhortation but a structural condition of viability.

For their part, the Sciences of Complexity achieved an extraordinary empirical and technical leap. Prigogine's dissipative structures, Wolfram's cellular automata, Barabási's network theory, and Kauffman's models of self-organization transformed the understanding of nature and society. Yet this quantitative power was not accompanied by an equivalent ontological reflection. In many cases, scientific complexity was reduced to simulating the behavior of systems without interrogating the nature of the relations that constitute them. Operating within an ontology of data, these sciences remained trapped in a deterministic logic, even when describing chaotic or adaptive processes. Edward Lorenz's deterministic chaos theory illustrates this limit well: the "butterfly effect" popularized sensitivity to initial conditions but did not break with determinism—it merely refined it. Its impact was more cultural than scientific, generating fertile metaphors but not a genuine epistemology of relationality.

In sum, both Complex Thought and the Sciences of Complexity represented decisive but partial steps. The former offered consciousness without equations; the latter, equations without consciousness. SK emerges as a higher synthesis, integrating Morinian reflexivity with Prigoginian formalization, articulating both within a coherent metatheory where complexity becomes a universal grammar of description, prediction, and intervention. Thus, SK does not deny these legacies—it reorganizes them into a spiral morphology that enables the passage from observation to transformation, from descriptive complexity to constructive supercomplexity.

4. Toward a Methodological Expansion: Principles for an Integrative Science

The historical and epistemological trajectory analyzed thus far shows that modern science—despite its analytical power—faces inherent limits when addressing multiscalar, interactive, and evolutionary phenomena. In response, Supercomplex Knowledge (SK) proposes a methodological expansion that does not replace the scientific method but rather extends it ontologically, incorporating the principles of interaction, circularity, and self-reflexivity characteristic of complex systems.

This expansion unfolds through three fundamental principles:

Taken together, these principles do not deny the heritage of the scientific method—they expand it. Where once there was static observation, there is now interactive simulation; where there was linear causality, there is now evolutionary combinatorics; where there was distant objectivity, there is now relational responsibility.

5. Paradigmatic Threshold: Integrating Contributions Toward a New Framework

The current scientific canon shows signs of wear when confronted with planetary challenges, yet at the same time, signals of renewal emerge: new categories, new instruments, and a new epistemological sensitivity.

Among these transformations, the following stand out:

• Relational ontologies, which replace substance with interaction.
• Non-radical constructivist epistemologies, which incorporate the observer's perspective without sacrificing rigor.
• Global grammars with local descriptors, where universal models adapt to situated contexts.
• Data-fusion technological artifacts, capable of integrating heterogeneous layers of information.
• Multiscalar artificial intelligences, oriented toward correlations among biological, social, and technological systems.
• Planetary ethical agreements, which redefine survival and well-being as scientific criteria, not merely moral ones.
• Rhizomatic and spiral morphologies, which replace rigid hierarchy with combinatorial expansion.
• Complex and supercomplex equations, expressing the transition from description to intervention.

In this context, predictability ceases to be a linear exercise and becomes a combinatorial function: knowledge does not anticipate single outcomes, but possible scenarios where variables interact within multiscalar networks. Thus, while linear predictability fails in the face of global phenomena such as pandemics or climate crises, supercomplex predictability integrates epidemiological data (EF), healthcare structures (SM), and social rhythms (CT) into models capable of evolutionary adaptation.

No paradigm can respond to planetary challenges without explicit axiological criteria—resilience, diversity, justice, and sustainability. SK does not treat these values as ethical add-ons but as structural conditions of systemic viability. In this new framework, ethics becomes a form of energy that sustains life in interaction.

We stand at a historical threshold: the contributions of the scientific mainstream and the advances of complexity theories can, for the first time, converge in a relational, constructivist, and axiological framework. SK does not seek to dismantle scientific tradition, but to reorganize it: leveraging its treasures—the rigor of thermodynamics to measure energy flows, the power of data science to map connectivities—within a new relational ontology, a constructivist epistemology, and dynamic empirics based on descriptive, predictive, and interventionist equations.

The knowledge of modernity can finally be synergistically enriched. This is not about breaking with science, but about turning it into a spiral of consciousness, where each turn expands our understanding of the universe and life. Are we witnessing a paradigmatic shift that has been quietly gestating and is beginning to reveal its alternatives?

Abstract

Relational ontology is the philosophical foundation of Supercomplex Knowledge (SK). This chapter explains how supercomplexity surpasses substantialist, hybrid, and structural realist ontologies, proposing instead a systemic positioning that conceives reality as a dynamic interaction among Energy Flows (EF), Structural Morphologies (SM), and Temporal Connectivity (TC). SK rejects Structural Realism by refusing to reduce the real to a static skeleton; instead, it postulates a trinitarian and circular relationality that is formalizable and operational.

1. The Limit of the Substantialist Canon

Modern ontology, rooted in Aristotelian and Cartesian thought, assumed substances that existed "in themselves," with fixed attributes (Aristotle, 1984; Descartes, 1996). This view enabled the consolidation of classical science and industrial technology but now encounters phenomena it cannot fix: quantum indeterminacy (Heisenberg, 1927), brain plasticity (Changeux, 1983), information distributed in digital networks (Castells, 1996), and the emergence of complex systems (Prigogine & Stengers, 1984).

At these frontiers, the "substantial" proves insufficient: there is no stable core capable of explaining relational, co-emergent, and fluctuating phenomena.

2. Surpassing Traditional Ontologies

3. Vectors of Shift Toward the Relational

Various contemporary developments compel the abandonment of substance as a central category:

• Quantum Physics: introduces superposition, entanglement, and contextual collapse—phenomena that demand thinking in terms of relations (Bohr, 1934).
• Neurosciences: show that the brain is not a closed entity but a plastic organ, in continuous co-construction with its environment (Damasio, 1994).
• Brain-System: mind and self are co-productions between organism and environment (Varela, Thompson & Rosch, 1991).
• Artificial Intelligences: operate on dynamic patterns and data flows rather than on predefined "essences" (Floridi, 2014).
• Data Fusion: integrates multiple heterogeneous sources into a result without a fixed center, emphasizing connectivity (Kitchin, 2014).

4. Emerging Relational Ontologies

What is taking shape is a passage from being as substance to being as a network in flux. These ontologies:

• Describe entities as temporary nodes within networks of interdependence.
• Recognize that identity arises through interaction (e.g., particle-when-observed).
• Are suited to integrating science and technoscience, as they work with open systems in evolution.

5. The Systemic Positioning of Supercomplex Knowledge (SK)

SK does not replace substances with relations or rigid structures with empty forms. Its contribution is a systemic positioning: each entity is understood both as a system in interaction and as part of larger systems.

The three fundamental categories are:

• Energy Flows (EF): movements, transfers, and modulations of energy.
• Structural Morphologies (SM): forms, configurations, and organizational patterns.
• Temporal Connectivity (TC): rhythms, durations, and transitions in time.

These categories are expressed through circular causality, manifesting in both stable and emergent behaviors, and they appear in different modalities across the three macrosystems—microparticulate, macroscopic, and biological (including the technological).

SK holds that nothing exists outside a system: every phenomenon, particle, or relation is always part of a broader network of dynamic interactions. Nothing exists that is not part of a system. This is the basis of its ontological-relational nature.

6. SK Ontology: Relationality, Combinatorics, and Circularity

SK is grounded in a fundamental principle: the universe and life are relational, combinatorial, and circular.

• Relationality: nothing exists in isolation; every system is constituted by flows and connections.
• Combinatorics: the evolution of the universe is explained by the multiple ways energy, space, and time combine.
• Circularity: order and disorder coexist as alternating, necessary phases in system dynamics. This circularity should not be understood as a closed cycle repeating identically, but as a spiral of constant feedback, where each "turn" qualitatively transforms the system.

In this sense:

• Energy does not flow without a structuring space.
• Space does not form without temporality.
• Time does not manifest without energy and form.

This triple unity constitutes the ontological foundation of every complex system.

7. Continuity, Formalization, and Transcendence

Substantialist ontologies will continue to operate in regulated domains (legal, administrative, technological-standard), but relational ontologies are gaining prominence in innovation, where understanding the dynamic, the uncertain, and the emergent is crucial. Contemporary philosophy, data science, and technoscience are already converging in this direction: the question is less "what is this in itself?" and more "how does it relate, transform, and co-evolve?"

SK provides what previous critiques could not:

• A robust philosophical framework (clear ontology).
• An operational categorization (the triad).
• A path to its own mathematical formalization (SK equations).

Formalism is not abandoned but extended: EF, SM, and TC are co-constitutive categories of reality, and their circularity enables a new form of modeling. Objectivity is also not lost: it shifts from substance to function and pattern, with SM as a measurable anchor.

Finally, SK's rigor is not based on linear equations, but on the invariance of supercomplex equations capable of formalizing the relational, multiscale, and emergent.

Final Synthesis

The relational ontology of SK transcends substantialist, hybrid, and structuralist views, offering a systemic paradigm where reality is a circular interaction between energy, form, and time. By redefining systems as networks of dynamic interdependence, it opens space for ethical and technological intervention guided not by control of isolated substances, but by the optimization of EF–SM–TC relationships that constitute system well-being.

Summary

This chapter develops the triad of Energy Flows (EF), Structural Morphologies (SM), and Temporal Connectivity (TC) as the universal grammar of the real. Far from being a metaphor, it constitutes the ontological and operational foundation of Supercomplex Knowledge (SK). Its components are presented, examples at different levels (atoms, plants, cities, social networks) are provided, and a strategy is systematized that combines ontology, dynamic descriptors, falsifiable hypotheses, and interdisciplinary operationalization. The triad is presented as a core capable of unifying scientific perspectives, anticipating configurations, and guiding circular interventions. Finally, the triadic predominances in each macrosystem are highlighted (energy in the microparticular, form in the macroscopic, and time in the biological-technological), emphasizing that SK offers a falsifiable, applicable, and formalizable framework, in continuity with relational ontology, and ready to evolve into scientific methodology and social practice.

Introduction

The ontology of Supercomplex Knowledge finds its most precise expression in the triad of Energy Flows (EF), Structural Morphologies (SM), and Temporal Connectivity (TC). These three components are neither metaphors nor rhetorical devices, but inseparable ontological categories that constitute the universal grammar of the real. Wherever a system lives, endures, organizes, or transforms, this triad pulses within it, allowing complexity to be described, predicted, and intervened upon in a coherent and operational way. This grammar does not compete with the classical physical framework—it reorganizes it. Whereas relativity integrated space and time into a four-dimensional metric, SK adds a typology of behaviors (stability and synchronous and sequential emergences) that operationalizes the articulation between energy, form, and time across different macrosystems.

Triadic Postulate (TP). Every system S can be described, at the pertinent scale, by an inseparable triple EF,SM,TC. The omission of any of the three components diminishes the descriptive, predictive, or interventive capacity over S.

Conditions of applicability. The TP operates under: (i) defined scale; (ii) observable metrics for each component; (iii) explicit temporal window; (iv) criterion of operational closure (what counts as “within the system”).

Scale clause. The triadic decomposition is scale-dependent: a pattern classified as stability at a monthly scale may appear as emergence at an annual scale. Every application must explicitly declare the analytical scale and its sensitivity.

The strength of the triad does not lie in the novelty of its individual elements, but in the force of the Triadic Postulate (TP) and its non-separable nature. Classical physics omits TC as an active component (treating it as a mere background). Biology omits the tensorial formalization of SM. SK requires that all three components be measured and correlated in any intervention. This is not generality; it is a strict operational requirement. If one is omitted, the intervention fails to generate coherence. The triad is radical in its application.

The strength of the triad also lies in its transversal character: it can be applied to natural, social, biological, and technological phenomena without erasing their disciplinary differences. An atom vibrates with the orbital energy of its electrons (EF), organizes itself into nucleus and orbitals (SM), and transitions through discrete energy levels (TC). A plant transforms light into photosynthesis (EF), structures itself in roots, stems, and leaves (SM), and grows according to rhythms of germination and flowering (TC). A social network propagates messages and emotions (EF), is sustained by algorithms and platforms (SM), and multiplies its effect in the fleeting nature of the viral (TC). A city pulses with electricity, transportation, and human labor (EF), is mapped in streets and neighborhoods (SM), and breathes in the daily rhythm of hours and in the secular duration of centuries (TC).

Energy Flows (EF)

Energy flows constitute the active dynamics of every system. They are not limited to physical motion: they include the circulation of information, emotional transformations, and exchanges of economic value. Their essential feature is variability—they can be constant or fluctuating, linear or turbulent, laminar or chaotic.

In SK, “energy” is used in an operational sense: magnitudes of transfer or circulation (physical, informational, affective, economic) that are measurable within their domain. The term “combinatorial capacity” refers to the superposition and coupling of observable frequencies or rhythms (e.g., spectra, coherence, couplings). Information is conceived as organized energy—a synthesis of what occurs in energetic, spatial, and temporal terms within a system (Floridi, 2014).

Structural Morphologies (SM)

Structural morphology is the way in which a system organizes its components and channels the flows that traverse it. It can be physical (mountains, organisms), conceptual (theories, models), symbolic (languages, myths), or digital (networks, algorithms).

SK distinguishes between:

• Spatial structural morphology: physical or symbolic arrangement of elements.
• Functional energetic morphology: dynamic patterns emerging from flows, such as turbulence, vortices, or oscillations.

Both determine each other: form channels energy, but energy reshapes form. Natural examples such as the erosion of a riverbed or neuronal adaptations in brain plasticity illustrate this circularity between structure and flow (Changeux, 1983; Damasio, 1994).

SM is quantified through topological metrics (degree, modularity, betweenness), curvature tensors for continuous forms, and measures of structural complexity (entropy or minimal description length).

Temporal Connectivity (TC)

Temporal connectivity is not a mere stage on which phenomena occur, but the condition that regulates rhythms, durations, and sequences. Temporality defines the permanence and transformation of systems—what endures, what vanishes, and what cyclically returns.

A hurricane lasts for days, an institution for centuries, an emotional memory for an entire lifetime. In every case, what is at stake is how the system manages those internal and external temporal connections.

TC is operationalized through autocorrelation functions, long-memory measures (Hurst exponent), regime shifts (Bayesian detection), and multiscale couplings (wavelets).

Scenarios

The Supercomplex Knowledge (SK) framework assumes that the same logic that constitutes the universe also organizes our way of knowing it. Ontology and epistemology do not regard each other from a distance—they mirror one another. The observer and the observed share the same grammar. To understand is always to participate in the modulation of energy, form, and time.

Examples:

• An atom vibrates with the orbital energy of its electrons (EF), is structured into a nucleus and orbitals (SM), and undergoes discrete level transitions (TC).
• A bridge bears tensions and weights (EF), stands on pillars and cables (SM), and spans decades of use or decay (TC).
• A river rushes as a current (EF), finds its channel in the riverbed (SM), and follows the cycles of rain and drought (TC).
• The climate redistributes radiation and heat (EF), draws clouds and atmospheric currents (SM), and marks seasonal and millennial cycles (TC).
• A plant transforms light into photosynthesis (EF), organizes itself into roots, stem, and leaves (SM), and grows according to rhythms of germination and flowering (TC).
• A football match ignites the players’ physical and emotional energy (EF), unfolds within a field with rules (SM), and takes place over the 90 minutes that define its temporality (TC).
• A social network spreads messages and emotions (EF), relies on algorithms and platforms (SM), and multiplies its impact in the fleeting nature of the viral (TC).
• A space flight launches with the thrust of its engines (EF), is sustained by the spacecraft’s structure (SM), and crosses orbits and returns within critical temporal windows (TC).
• A city pulses with electricity, transportation, and human labor (EF), is mapped through streets, neighborhoods, and buildings (SM), and breathes in the daily rhythm of hours and the long pulse of centuries (TC).
• A song envelops us in sound vibrations (EF), is organized into melodies and harmonies (SM), and becomes fixed in affective memory that lasts far longer than its minutes (TC).

Metatheoretical and Empirical Strategy of the SK Framework

The Supercomplex Knowledge (SK) framework combines conceptual revision and cross-disciplinary application through five steps:

1. Transversal Identification: examines energy, structure, and time across the physical, biological, technological, and social sciences (Prigogine & Stengers, 1984; Capra, 1996; Latour, 2005).
2. Ontological Systematization: recognizes that EF, SM, and TC are universal categories, present in the description of any system.
3. Derivation of Dynamic Descriptors: the triadic interaction generates observable patterns that function as indicators of systemic behavior (Holland, 1998; Kauffman, 1993).
4. Falsifiable Hypothesis: it posits that every significant transformation can be mapped as stability, synchronous emergence, or escalating emergence. The hypothesis is refutable—if a relevant phenomenon cannot be represented, the model fails.
5. Disciplinary Delegation: operationalization relies on metrics specific to each field (entropy, biodiversity, financial indicators, etc.), ensuring interdisciplinary compatibility.

While data science privileges the “how much?” and theory seeks the “why?”, the triad adds the “how does it behave?”. This typological bridge makes it possible to move from magnitudes (energy, network metrics, time series) to classes of behavior (stability, synchronous emergence, escalating emergence), which in turn guide hypotheses and intervention decisions.

Fruits of the Triad: Unification, Prediction, and Intervention

Robustness and Response to Objections

The SK distinguishes itself from traditional approaches in four key aspects:

• Transversal Ontology: it is based on universal categories (EF–SM–TC) that cut across all disciplines.
• Falsifiable Hypothesis: if a phenomenon cannot be mapped within the triad, the hypothesis is invalidated.
• Capacity for Intervention: it not only describes but also guides verifiable decision-making.
• Temporal Clarity: TC integrates rhythms, durations, asynchronies, and historical memory.

The SK departs from General Systems Theory (Bertalanffy, 1968) and from Prigogine’s thermodynamics of non-equilibrium (Prigogine & Stengers, 1984) in two decisive respects: (i) TC is not a background but an active component regulating asynchronies and memories; (ii) the SK articulates a circular intervention EF↔SM↔TC, surpassing the “black box” model. Systems co-create themselves by internally modulating their three components, enabling ethical-technological intervention as an integral part of scientific practice. This is scientific-technological praxis, not merely physical description. Thermodynamics describes what will happen; the SK models how to reconfigure what will happen.

A recurrent objection claims that the triad is too general. Its strength, however, lies precisely in being falsifiable and operational. The ongoing mathematical formalization—tensor calculus (SM), nonlinear dynamics (EF), and complex time series (TC)—consolidates its technical rigor.

Another common objection concerns the problem of double accounting or overlap in measurement. How can one distinguish informational EF from the SM that channels it (e.g., an algorithm)? The distinction is resolved at the operational and scalar level. Structural Morphology (SM) is measured through topological metrics and tensors (the form), while Energy Flow (EF) is quantified through transfer magnitudes (rate, power). The algorithm is the form that channels the quantity of information (the EF). In practice, the Predominance Test (see §4.7) is applied, requiring cross-validation to determine which component explains the greatest variance in a given pattern. This overlap, far from being an error, is the essence of complexity; the SK simply provides tools to measure it unambiguously.

Finally, regarding the objection of ambitious falsifiability, the SK establishes rigorous conditions. The Scale Clause and the Operational Closure Criterion are not ad hoc defenses but methodological requirements for coherence. If a relevant phenomenon within a system (S), with an explicitly delimited scale, cannot be mapped within triadic patterns (Stability, Synchronous Emergence, Escalating Emergence), the Triadic Postulate is invalidated for that specific domain. Moreover, Disciplinary Delegation requires that metrics (tensors, wavelets) be validated by mainstream disciplines, preventing the invention of new measures to avoid refutation. The falsifiability of the SK is strict, since its capacity for intervention depends on the precision of these delimitations.

Triadic Predominances

Although the three components are always present, each macrosystem privileges one:

• Microparticular: energy, vibration, and fluctuation predominate (Heisenberg, 1927; Bohr, 1934).
• Macroscopic: form prevails, as in galaxies or solar systems.
• Biological and technological: time dominates, since life and its creations manage cycles, memory, and duration.

Predominance Test. A domain exhibits XXX predominance if its XXX metrics explain ≥ α% of the variance of the patterns, surpassing those of the other two components under cross-validation.

Technology and Technoscience Viewed through the Triad

The technological field constitutes a privileged example for observing the operativity of the FE–ME–CT triad. Every technology requires an energy flow to function, from fossil fuels to digital information. Structural morphology materializes in both physical and digital supports: hardware, algorithmic architectures, neural networks, and data structures. Temporal connectivity introduces the dynamics of updating and obsolescence. In this sense, technology can be read as a supercomplex macrosystem in which energy, form, and time are co-constituted.

The Triad as a Unifying Framework for Contemporary Complex Thought

Supercomplex Knowledge (SK) is postulated as the theoretical framework that distinctly and simultaneously articulates three requirements that no other approach satisfies in an integrated manner: (a) an explicitly triadic ontology —Energy Flows (EF), Structural Morphologies (SM), and Temporal Connectivities (TC)— applicable at all scales; (b) a real capacity to describe what advanced technological systems are doing today —from Transformer architectures and multimodal models to the energy dynamics of AI physics—; and (c) a robust synthesis of the core contributions from the diverse currents that have attempted to address complexity, avoiding their historical fragmentations and reorganizing their findings within a common language.

In this light, the EF–SM–TC triad operates as an integrating meta-language. It allows for mapping the emphases of authors and traditions that previously seemed unconnected: Deacon and Kauffman are situated at the intersection of SM and TC; Longo and Bailly extend the notion of anti-entropy toward an extended EF; Dupuy offers projective keys for TC; Parrondo and Wolpert work the boundary between EF and TC from the thermodynamics of information; Seibt develops a process ontology that the SK concretizes in its three co-constitutive axes. The decisive point is not to align them, but to show that all of them describe partial regions of the triadic structure that the SK makes explicit and formalizable.

This framework also enables the detection of blind spots: almost no approach has systematically explored the EF ↔ SM circularity in contemporary techno-cognitive systems. The EF↔SM circularity in contemporary techno-cognitive systems, for example, shows how the architecture of an AI model (SM) irreversibly conditions its attention dynamics (EF), and how this dynamic, in turn, reconfigures the functional architecture of the system during learning. The SK places this interaction at the center and opens new research avenues in statistical physics, computational neuroscience, and the design of intelligent architectures. The triad also makes joint experimental programs possible, which are translatable into comparative protocols: keep EF constant and vary SM; alter SM to observe TC modulations; analyze EF at different TC levels to detect scaling emergencies. This double and triple-entry structure is directly usable by transdisciplinary teams.

Finally, the triad acts as institutional glue in a fragmented academic ecosystem. Process philosophers, theoretical biologists, mathematical physicists, AI specialists, neuroscientists, STS researchers, and information theorists all work on problems that the SK integrates without erasing differences. The formal simplicity of EF-SM-TC and its combinatorial power allow for the construction of a common conceptual network where dialogues are possible without reducing anyone.

In summary: the SK does not seek to replace Dupuy, Longo, Kauffman, Deacon, or any other tradition. It seeks to offer the conceptual space where all of them recognize themselves as describing different dimensions of the same ontological object. If each one discovered one face of the crystal, the SK provides for the first time the description of the crystal’s complete symmetry and—even more—the manual for carving and operating it in the scientific practice of the 21st century. The result is a truly post-reductionist, coherent, and evolutionary research program.

Energetic Modalities, Overlaps, and Supercomplex Regimes

For Supercomplex Knowledge (SK), all energetic modalities are present in all macrosystems, but not with the same efficacy or frequency. Overlapping is the rule, not the exception.

Supercomplexity emerges when non-dominant modalities are activated, when structural morphologies overlap, and when temporal connectivities enter into productive conflict. Therefore, behaviors of stability and emergence do not belong to a macrosystem in itself, but to the dominant regime that articulates energy, form, and time in a given situation.

The same system can simultaneously stabilize locally, synchronize globally, and scale historically. Complexity does not increase or decrease by scale; rather, it changes form according to the prevailing triadic combination.

Each macrosystem presents dominant energetic modalities that tend to be associated with specific structural morphologies and temporal connectivities. However, supercomplexity does not reside in these dominances, but in the dynamic overlapping of energies, forms, and times that allows systems to sustain stability, emergence, and innovation all at once.

The energetic modalities formalized by physics do not exhaust the concept of Energy Flows (EF), although they constitute privileged examples of how energy stabilizes, transfers, and combines in the physical macrosystem. In the SK, EF is an ontological-relational category; physical energies are formalized empirical cases of said category.

In the biological macrosystem, for example, the dominant energetic modalities are regulatory and self-organizing—such as metabolic chemical energy, electrochemical gradients, or regulated thermal energy. Here it becomes evident that the same chemical energy that tends to dissipate in the physical macrosystem can organize in the biological one.

This confirms a central principle of the SK: energy does not organize on its own. Organization emerges only when energy flows are articulated with specific structural morphologies and temporal connectivities. Complexity is not a property of energy, but of its triadic relational articulation.

Final Synthesis: From Grammar to Praxis

The EF-SM-TC triad is not an illustrative scheme but the matrix that organizes both thought and intervention. Ontology and epistemology are not observed from a distance—they mirror each other. To understand is to participate in the modulation of energy, form, and time.

In continuity with the relational ontology of the previous chapter, the triad is presented as the operative core that translates that ontology into empirical descriptions, multiscale predictions, and ethical and technological interventions. Whereas SK Ontology asserted that “there is nothing that is not part of a system,” the triad provides the way to read, model, and transform those systems.

Thus, the universal grammar here presented both demands and grounds its own methodology. The following chapter will take this crucial step: it will show how the FE–ME–CT triad translates into concrete tools for building adaptive dynamic maps, formulating supercomplex equations, and, ultimately, transforming ontological relationality into effective scientific and social praxis.

Summary

This chapter presents the classificatory architecture of Supercomplex Knowledge (SK), a non-hierarchical taxonomy that organizes the universe's systems according to their Energy Flows (EF), Structural Morphologies (SM), and Temporal Connectivities (TC).

The SK distinguishes three macrosystems —microparticle, macroscopic, and biological— each with internal systems that express specific modalities of complexity: fermions, bosons, and fields in the quantum realm; galactic, planetary, geological, hydrological, and atmospheric systems in the macroscopic realm; and plant and animal microsystems in the biological realm, from which human systems such as the self-conscious, the socio-relational, the symbolic, and the technological emerge.

The taxonomy is completed with the techno-engineering and cyber-analogical technological subsystems, which act as bridges between the biological, the material, and the digital. Collectively, these domains constitute an ontological map where reality appears as a dynamic framework of energy, form, and time, and where every system can be read as a particular configuration of the EF–SM–TC triad.

Development

The SK proposes a non-hierarchical and combinatorial classification of complex systems based on three universal criteria: Energy Flows (EF), Structural Morphologies (SM), and Temporal Connectivities (TC). The taxonomy is constructed not from matter or size, but from how energy, form, and time are organized in each domain. In this sense, any complex system can be classified using these questions:

• What types of energy circulate?
• How is it transformed, accumulated, lost, or resynchronized?
• What is the form or topology of its components and relationships?
• Is it hierarchical, networked, modular, laminar, rhizomatic, spiral?
• What rhythms, durations, cycles, and memories organize its dynamics?
• How are past, present, and future connected in its operation?

This classification is articulated into three overlapping macrosystems: microparticle, macroscopic, and biological, plus a family of emergent systems linked to mind, society, and technology.

5.1. The Three Macrosystems of the Universe and Their Internal Systems

5.2. Technological Subsystems: Techno-Engineering and Cyber-Analogical

At the intersection between the biological and the macroscopic, technological systems appear that merit specific distinction:

5.4. Principle of Non-Hierarchy

This classification does not establish a scale "from simple to complex," nor does it place one system "above" another. Each macrosystem expresses a modality of complexity, and reality is organized as a framework of overlaps: the quantum supports the macroscopic; the macroscopic supports the biological; the biological generates mental, social, and technological systems which, in turn, reconfigure the others.

5.5. Conclusion

With this taxonomy, the SK offers a general map of the universe's systems organized by energy, form, and time. It is the ontological scaffolding that allows the human reader and artificial intelligences to index, compare, and expand this construction into any domain of complexity.

Summary

This chapter examines the behaviors of complex systems as dynamic modulations between provisional stability, synchronous co-emergences, and sequential fluctuations. Within the framework of Supercomplex Knowledge (SK), these dynamics are interpreted through the triadic interaction between Energy Flows (EF), Structural Morphologies (SM), and Temporal Connectivities (TC), proposing a conceptual language capable of integrating physical, biological, social, technological, and symbolic phenomena within a single relational grammar.

Based on this approach, a typology is introduced that distinguishes between complex behaviors and supercomplex behaviors, understood not as closed categories but as dynamic regimes that emerge when the density of couplings between EF–SM–TC varies. Complexity describes systems where multiple components interact non-linearly, generating relatively stable configurations within a given structural framework. Supercomplexity, in contrast, appears when the system reorganizes the effective conditions that structure those interactions, expanding the space of accessible states and enabling new forms of emergence and reorganization.

The chapter proposes understanding this transition on two complementary levels. At the first level, supercomplexity is defined as an architectural regime in which a system, upon reaching a certain relational density, reconfigures its criteria for energy regulation, its structural morphology, and its temporal scales. At the second level, supercomplexity acquires a reflexive dimension, where multiple macrosystems—micro-particulate, macroscopic, and biological—overlap and interact, incorporating the action of cognitive and technological systems capable of observing, simulating, and intervening in those dynamics.

From this perspective, the chapter introduces a central thesis of SK: systems do not survive solely by preserving their stability, but through their ability to sacrifice local stability when necessary to generate emergences that expand their temporal connectivity. Stability guarantees the system's immediate functioning; strategic emergence ensures its evolutionary continuity in the face of changing environments.

On this basis, the Universal Map of Complex and Supercomplex Behaviors is developed, conceived not as a definitive taxonomy but as a dynamic and open repertoire for interdisciplinary description, prediction, and intervention. The map integrates recurrent behaviors—such as synergy, collaboration, predation, or stochasticity—with supercomplex behaviors associated with intersystemic overlaps, cognitive processes, and advanced technological systems.

The core value of this framework is twofold. On one hand, it possesses a heuristic function, opening zones of conceptual exploration that allow for the connection of traditionally separated scientific domains. On the other, it maintains a falsifiable character, as each behavior is defined by observable modulations between energy flows, structural configurations, and temporal dynamics that can be empirically contrasted in different contexts.

In this way, the chapter proposes a reading of complexity in which stability, emergence, and fluctuation do not oppose one another but coexist in a creative tension that drives the evolution of systems. SK thus offers a unified grammar to understand how the configurations of the universe arise, stabilize, and transform across multiple scales of organization.

1. Transition from Complexity to Supercomplexity: Stabilizing Functions, Synchronous Co-emergences, and Sequential Fluctuations

The distinction between complexity and supercomplexity does not refer to a quantitative increase in variables or an intensification of interactions, but rather to a change in the effective regime of organization in the dynamics between Energy Flows (EF), Structural Morphologies (SM), and Temporal Connectivities (TC).

Complexity describes systems in which multiple components interact non-linearly, generating feedbacks and producing relatively stable dynamic configurations within a given structural framework. From research on dissipative structures (Prigogine and Stengers) to models of exploring the adjacent possible (Kauffman 1995), complexity has been understood as the capacity of systems to generate novel configurations without modifying the effective conditions under which they operate.

Supercomplexity, on the other hand, designates a different regime. It does not introduce new entities or new fundamental laws, but rather identifies situations in which the system reconfigures the effective conditions that structure the interaction between EF, SM, and TC. The change is not merely dynamic, but architectural: the very framework within which previous dynamics were possible is transformed. The transition occurs when the system:

• Reorganizes its criteria for energy regulation.
• Reconfigures its structural morphology as an adaptive strategy.
• Integrates and recalibrates multiple temporal scales.
• Intervenes in the interaction networks that condition its viability.
• Simultaneously expands the space of accessible states and the possibility of synchronous or escalating emergencies.

Expansion of State Space and Structural Reorganization

In systems with high multi-scale interdependence, literature has shown the appearance of regime transitions and dynamics with wide-range event distributions (Bak; Watkins et al.; Tadić and Melnik). In such contexts, small perturbations can be amplified and produce extensive reorganizations.

Supercomplexity is not reduced to this critical behavior—of which SK has been particularly critical when interpreted as a simple domino effect of chaos. It manifests when, in addition to multi-scale sensitivity, the system modifies the structural framework that defines its own dynamic thresholds. In other words, the system does not just approach a critical point: it reorganizes the conditions under which those critical points emerge.

As the density of EF–SM–TC couplings increases:

• Stabilizable configurations diversify.
• The possibility of synchronous emergences increases.
• The probability of escalating reorganizations is amplified.

This is not a matter of systemic fragility, but rather a structural expansion of the field of possibilities. This transition can be observed, with different modalities, across the three macrosystems.

• Microfluctuation: In processes such as cosmic inflation (Guth; Linde), exponential expansion did not only modify energy magnitudes but also the effective conditions under which interactions could stabilize. The amplification of microscopic fluctuations redefined the system's dynamic architecture: it changed the operating framework of the early universe.
• Macrostructural: In climate systems, surpassing certain energy thresholds can produce regime transitions accompanied by multi-scale reorganizations (Watkins et al.). This is not merely a matter of gradual variations, but of modifications in the global dynamics that structure the couplings between oceans, atmosphere, and the cryosphere.
• Biological: In the adaptive immune system (Friston 2010; Bettinger and Friston), the anticipatory generation of diversity and immunological memory constitute a functional reorganization of the response architecture. The system does not limit itself to reacting within a given regime: it internally reconfigures the structure that regulates its interaction with the environment.

The phenomenon in all three cases does not depend on intentionality but on an effective reconfiguration of the dynamic regime. The gestation of the transition is continuous; the resulting reorganization is qualitative. The increase in multi-scale interdependence expands the space of accessible states. When relational density or energy flow decreases, that space contracts. Both the capacity for reorganization and the scope of escalating emergences are then reduced (Hengen and Shew; Watkins et al.).

Supercomplexity is, therefore, a conditional and reversible regime. It does not constitute a necessary evolutionary stage nor a guarantee of structural progress. In summary, supercomplexity can be defined as the architectural regime in which a system, upon reaching a critical relational density, reorganizes the effective conditions of interaction between Energy Flows, Structural Morphologies, and Temporal Connectivities, expanding the space of accessible states while simultaneously broadening its capacity for stabilization and for synchronous or escalating emergence. This transition is neither exceptional nor inevitable: it is a recurring possibility whenever appropriate energy, spatial, and temporal thresholds converge.

Dynamic Functions of Complex Systems

Complex systems are characterized by the coexistence of three major dynamic functions:

• Provisional Stability: ensures functional organization by synchronizing energy flows and consolidating temporal structures (e.g., the crystallization of reticular networks, planetary orbits, or the DNA double helix).
• Synchronous Co-emergences: produce cohesive morphological configurations through simultaneous interactions (such as bird flocks, trophic webs, or neural plasticity).
• Sequential Asymmetric Fluctuations: introduce progressive imbalances that reorganize structural morphologies and open trajectories for innovation (such as cosmic inflation, genetic mutations, or learning in deep neural networks).

These functions constitute the operational translation of the EF–SM–TC triad into the dynamics of complex systems. Far from opposing one another, they coexist in a creative tension: stability does not nullify emergence, and emergence does not destroy continuity. Both co-constitute the evolutionary dynamics of systems.

In a supercomplex key, these dynamics allow us to distinguish three general modes of emergence:

• Stabilizing Emergences, where the organization arising from energy flows consolidates behaviors that sustain the system's functional continuity.
• Synchronous Emergences, which appear when multiple components interact simultaneously, generating coherent configurations that cannot be deduced from the parts.
• Sequential Emergences, in which asymmetric fluctuations open bifurcations and innovation trajectories that reconfigure the system's morphology over time.

Taken together, these forms show that emergence is not an isolated event but a triadic process in which energy reorganizes spaces and spaces reorganize times. Supercomplex Knowledge thus offers a unified grammar to describe how the universe's configurations arise, maintain themselves, and transform across all scales.

Strategic Emergence and Historical Continuity

Classical theories of complexity have provided decisive tools for understanding self-organization, emergence, and the non-linear dynamics of systems. However, many of them share—either explicitly or implicitly—the premise that systemic survival is based on the capacity for self-regulation and the conservation of organization. From the autopoiesis of Maturana and Varela, which defines life as organizational closure, to Kauffman's self-organization models or Morin's order–disorder dialogics, stability appears as a central condition for persistence.

Supercomplex Knowledge (SK) proposes a decisive conceptual shift: systems do not survive simply by regulating themselves efficiently, but through their ability to sacrifice local stability when necessary to generate emergences that expand their temporal connectivity.

Stability allows for present functioning; strategic emergence guarantees historical continuity. Unlike chaos theories, which describe rupture as a consequence of sensitivity to initial conditions (Lorenz), SK distinguishes between destructive crises and strategic emergences. The latter are processes through which a system accepts local disorder, loss of efficiency, or organizational breakdown in order to preserve—and expand—its long-term viability.

From this perspective, survival is not defined as the persistence of a form, but as the ability to continue recombining energy, structural, and temporal configurations in the face of changing environments. Operational closure remains a necessary condition for present functioning, but it is not sufficient for historical continuity. A system can radically reconfigure its organization without interrupting its temporal combinatorial lineage. Strategic emergence is not equivalent to uncontrolled chaos, but to the active selection of bifurcations that expand the space of viable future configurations.

An Evolutionary Example

A paradigmatic example is offered by mass extinctions in the history of life. The Permian–Triassic crisis (~252 Ma) eliminated more than 90% of marine species and a similar proportion of terrestrial species, destroying the ecosystemic stability achieved over hundreds of millions of years. However, this rupture did not interrupt the lineage of tetrapod life. On the contrary, it cleared niches and resources that allowed for the explosive radiation of new clades—archosaurs, dinosaurs, mammals, and much later, primates—expanding the future combinatorial space of the biosphere.

The rigidity of the previous ecosystems condemned them; the capacity to withstand massive local disorder ensured the historical continuity of complex life.

2. Integration of Theories of Emergence

Contemporary literature on complex systems has proposed multiple notions of emergence—autocatalytic, organizational, collective, critical, semiotic, or cognitive—developed by authors such as Kauffman, Holland, Bak, Deacon, or Edelman. All of these were considered in the present taxonomy.

However, none of them constitute a type independent of the modes of emergence defined here. Rather, they reveal themselves as particular cases of stabilizing, synchronous, or sequential dynamics, or as trans-macrosystemic phenomena where the three SK macrosystems interact.

Far from dismissing them, SK integrates and unifies them within a triadic grammar that allows for an understanding of their origin, their morphogenesis, and their scale of action. The innovative contribution of SK does not consist of adding new labels, but of offering the ontological and dynamic framework capable of explaining them in an integrated manner.

3. Definition of Supercomplexity: From the Observed System to the Co-Created System

SK redefines supercomplexity across two gradual levels:

• Triple intersystemic overlap: interaction among macrosystems of different natures (microparticulate, macroscopic, and biological). Example: quantum coherence in photosynthesis combines quantum energy (FE), protein structures (ME), and decoherence times (CT).
• Summation with brain and technology: overlap amplified by human cognition and techno-engineering systems (AI, deep learning). Example: financial markets with high-frequency algorithmic trading are socio-techno-cognitive systems that coevolve and reconfigure their own rules.

A phenomenon is supercomplex if it meets at least one of the following criteria:

• (a) Quantifiable intersystemic overlap: interaction among macrosystems generates a behavior that no isolated system could explain.
• (b) Demonstrable cognitive-technological recursivity: a cognitive or technical agent actively reconfigures the system's FE–ME–CT triad, introducing second-order emergences.

Such behaviors can be validated through observable correlations among energetic, structural, and temporal metrics in experimental or simulated domains.

4. Complex Behaviors

Complex behaviors are recurrent triadic modulations:

• Synergy – intrasystemic cooperation (e.g., cellular metabolism).
• Catalysis – enhancement of external processes (e.g., enzymes).
• Collaboration – joint transformation (e.g., lichens).
• Competition – adaptive struggle for resources (e.g., birds adjusting schedules).
• Predation – destructive absorption (e.g., lion–antelope).
• Subsumption – subordinated integration (e.g., mitochondria in a cell).
• Multiversal collision – hypothetical cosmic impacts generating new configurations.
• Stochasticity – random fluctuations guiding evolution (e.g., genetic mutations).

5. Supercomplex Behaviors

Supercomplex behaviors emerge from intersystemic overlap and coevolution with cognition and technology. Their brief characterization follows:

• Intersystemic overlap – interaction among distinct domains (e.g., quantum mutation in a biological protein).
• Self-perceptive computation of well-being – a system's ability to assess its own state and adjust its behavior (e.g., AI measuring its error rate).
• Transtemporal timing – integration of past, present, and future in decision-making (e.g., GPS combining previous routes with projected destination).
• Superposition – coexistence of multiple potential states (e.g., quantum electron; a society with multiple possible futures).
• Entanglement – inseparable correlation across distance (e.g., quantum photons, intense social bonds).
• Spiral algorithmics – hybrid order/chaos processes in progressive expansion (e.g., generative deep learning).
• Artificial recursive autonomy – technical systems' capacity to reprogram and generate new versions of themselves.
• Human–machine coupling – bio-technical integration within a shared functional flow (e.g., neural implants, smart prostheses).

6. FE–ME–CT Triadic Table

The following diagram summarizes the most recurrent correlations between the energetic, structural, and temporal functions of complex and supercomplex behaviors

7. Complexity in the Key of Supercomplex Knowledge

Although there is some consensus around the etymology of complexus—"that which is woven together"—definitions of complexity have varied across historical, theoretical, and disciplinary contexts. SK inherits and reconfigures this legacy within a relational grammar that understands complexity not as an added property but as the structural condition of the universe.

Complexity unfolds through a weave of provisional stability, synchronous coemergences, and asymmetric fluctuations, integrating both transient behaviors and profound transformations. From this perspective, the universe is conceived as an interconnected fabric of energy, interactions, and evolution, where the causes of complexity intertwine to generate the dynamic diversity that characterizes our reality.

The SK paradigm captures these deep interactions, showing that the evolution, transformation, and continuity of complex systems depend on the dynamic interplay among stability, synchronization, and progressive imbalance. Complexity arises from this dynamic equilibrium between stability and change, where energy flows (FE), structural morphologies (ME), and temporal connectivities (CT) not only coexist but interweave within an evolutionary and stochastic process. Thus, complex systems continuously generate and transform new forms and functions, challenging any attempt to reduce them to linear or simplified behaviors.

In this sense, complexity is the dynamic interlacing of energy, form, and time: the living fabric that sustains the coherence of the universe as it changes, the continuity of life as it transforms, and human meaning as it reinvents itself.

8. Relationship between Complexity and Supercomplexity

The relationship between complexity and supercomplexity constitutes the ontological and epistemological core of SK. It is not a matter of degree—as if supercomplexity were merely denser or more numerous complexity—but of nature: complexity describes the internal functioning of the universe, while supercomplexity expresses its reflexive capacity and conscious reorganization through knowledge.

9. Conclusion: Toward an Operative Meta-Framework

The Universal Map of Behaviors represents a decisive innovation within SK. Its goal is not to compete with specific theories but to function as an operative meta-framework:

• Operative language: defines behavior as triadic modulation (FE–ME–CT).
• Integrative function: connects physics, biology, culture, and technology under a shared framework.
• Projective function: opens heuristic zones where yet-uncatalogued behaviors may emerge.

While Morin describes complexity, SK designs within it—proposing a formal triadic language and a map that allows systemic modeling. It marks the difference between a philosophy of nature and an engineering of complex systems.

In continuity with the relational ontology of the previous chapter, the triad is presented as the operative core that translates this ontology into empirical descriptions, multiscale predictions, and ethical and technological interventions. Where SK Ontology stated that "there is nothing that is not part of a system," the triad provides the means to read, model, and transform those systems.

Abstract

This chapter develops the epistemological foundation of Supercomplex Knowledge SK: multiscale complex constructivism. Unlike classical constructivism, which oscillated between relativism and moderate factual adequacy, the SK integrates human cognitive activity, modeling technologies, and the multiscale nature of complex systems. Classical science provided rigor, laws, and prediction, but at the cost of external objectivity and deterministic universality that are now insufficient. The SK does not deny those achievements: it extends them with a methodological plus —triadic descriptors EF-SM-TC, adaptive dynamic maps, falsifiable hypotheses, and explicit axiology— and redefines science as a situated, probabilistic, and performative co-construction and co-evolution.

The epistemology of the SK displaces the external observer and recognizes them as an active participant in the systems they model. Reality ceases to be conceived as a fixed object and is understood instead as a network of dynamic and multiscale interactions, where every description is mediated by languages, mathematics, technologies, and cultural contexts. From this arise notions such as the social formatting of knowledge, probabilistic multicausality, and epistemological consequentialism: the validity of a model is measured by its capacity to ethically modulate the EF-SM-TC triad and sustain systemic well-being, rather than by an impossible mimesis of a "reality in itself."

On this basis, the chapter articulates complexity as a scalar phenomenon: from quantum fluctuations to cosmic dynamics and the intermediate biological fringe, showing that supercomplexity does not reside in a privileged scale, but in the overlaps between macrosystems. Supercomplex Knowledge proposes moving from the isolation of scales to scalar coherence, where maximum complexity is found in the links: in the energetic-morphic-temporal interweaving that connects microparticles, macroscopic structures, and biological systems.

Finally, multiscale complex constructivism expands toward supercomplex engineering: technology appears as CCM in action, as a practice of triadic assembly among EF, SM, and TC across multiple scales. Examples like Tesla, contemporary multiscale technologies, and the emergence of the Bio–Techno–Cognitive (BTC) macrosystem show that knowing is no longer just representing, but designing operative coherences. The philosopher ceases to be a commentator on science to become a systems developer and supercomplex cartographer, capable of programming maps, simulating scenarios, and co-designing responsible interventions in living, social, and technological systems.

Foundations

SK is grounded in multiscale complex constructivism. This does not imply rejecting the philosophical tradition, but acknowledging that its classical form has reached a limit. The figure of the philosopher as an external observer, interpreter of being, guardian of language, or critic of culture is no longer sufficient. Today’s challenges—ecological, technological, existential—require a form of thought that not only interprets but also acts, models, intervenes, reorganizes, and anticipates (Morin 2005).

SK redefines epistemology through a multiscale complex constructivism that intertwines ecological scales with worldviews, achieving epistemological seduction by orchestrating classical tools with global perspectives, transforming ontological uncertainty into a plural co-evolution that respects cultural diversity. Far from relativism, it inscribes probabilistic multicausality within a consequential framework that validates models by their fertility in ethical interventions, anchored in ethnographies observing real interactions across scales, ensuring internally coherent results tested in lived contexts. Ontological uncertainty becomes a constitutive property, orchestrating tools to co-construct realities that sustain systemic well-being without absolute mimesis. SK neither universalizes nor negates classical science; rather, it extends it through multiscale mappings that integrate deterministic laws with local traditions, fostering relational and responsible knowledge.

We need a philosophy that does not dwell on the margins of the system but operates from its core. A philosophy that not only reflects but also programs maps, visualizes patterns, simulates possible scenarios, and reads complexity through multiscale tools (Latour 2005). This new philosophy does not replace thought with technique—it thinks from and with technique. It no longer expresses itself solely in texts, but also in algorithms, visualizations, dynamic maps, and interactive environments. The contemporary philosopher no longer asks merely what being is, but how a system is structured, modeled, reconfigured, and optimized to sustain systemic well-being, coherence, and justice.

Against the objection that such a figure is technocratic, SK posits that the philosopher-systems-developer is not an enlightened despot but a facilitator. His role is to translate among communities and create interfaces for diverse actors to collaborate in dialogical and plural modeling, where ethics emerges from co-creation rather than unilateral decree.

From External Observer to Active Participant

The complexity and supercomplexity of the universe cannot be understood through a form of thought that claims to stand outside of systems (Prigogine & Stengers 1984). Every form of knowing is already a form of intervening. The subject is not an isolated consciousness contemplating the world from a safe distance, but a complex system actively participating in the very dynamics it seeks to understand (Varela, Thompson & Rosch 1991).

Therefore, the epistemology of SK is not grounded in objective representation, pure introspection, or linear description of facts, but in multiscale complex constructivism: a logic in which knowing implies relating, abstracting, simulating, experimenting, and reconfiguring across different levels of reality—from the micro to the macro, from the symbolic to the material.

This epistemological shift does not entail abandoning aesthetics or design, but integrating them into the process of knowledge construction. The strategy of SK is not direct confrontation with classical science but epistemological seduction: demonstrating that what the mainstream has done well can be enhanced with a methodological surplus. Modeling systems is also a creative act; it requires aesthetic sensitivity, attention to form, and the design of visual and conceptual narratives that render complexity intelligible. Seduction does not seek to destroy but to attract—to show that SK extends, without negating, previous achievements.

SK is an operative meta-framework. It neither discards equations nor numerical prediction; rather, it provides the triadic language (FE–SM–TC) to determine which mathematical tools to apply and at what scale, orchestrating them for problems that overflow any single discipline (Holland 1998; Kauffman 1993).

Reality as Dynamic Construction

This constructivism recognizes that our understanding and description of reality are built from our experiences, interactions, cognitive capacities, and technological tools. Although this construction is inevitably conditioned by our limitations as observers, it is possible to identify consistent behaviors and recurrent phenomena that suggest the existence of a relational framework transcending individual perceptions. However, this “objective reality” should not be understood as something fixed, absolute, or independent of our interactions, but as a dynamic, emergent, and multiscale process. Epistemological uncertainty, therefore, arises from ontological uncertainty (Heisenberg 1927; Bohr 1934).

The stance of SK is defined as epistemological consequentialism. The validity of a model lies not in its correspondence to an absolute truth but in its ability to generate successful and ethically robust interventions. Success is measured by the capacity of the intervention to positively modulate the triad FE–SM–TC—that is, to sustain coherent energy flows, maintain structural morphological diversity, and enhance the resilience of temporal connectivity. The test lies in the consequences of the intervention, not in an impossible mimesis (Cilliers 1998).

From this perspective, reality is not simply an external object we observe but a network of interactions in which observers and observational tools play an active role in its configuration. This paradigm acknowledges that any description is intrinsically tied to the systems in interaction, to the tools, and to the scales used for observation.

When modeling complex systems, our tools—language, mathematics, advanced technologies—inevitably mediate these approaches. This gives rise to what SK calls social formatting: a phenomenon by which our descriptions emerge from the historical, educational, and cultural contexts we share. Knowledge is not a mirror of reality but a format of reality produced through interaction with its cultural and technical environment.

Multiscalarity and the Example of Photosynthesis

From a multiscale perspective, SK articulates interactions among different levels of complexity—microparticulate, macroscopic, and biological—acknowledging how these dynamics affect both the stability and emergence of systems.

Example: photosynthesis.

• Quantum scale: FE (electron excitation), SM (superposition of states), TC (decoherence in femtoseconds).
• Biochemical scale: FE (energy transfer), SM (chloroplast structure), TC (Calvin cycle).
• Ecological scale: FE (biomass flow), SM (trophic network), TC (seasonal cycles).

SK does not reduce one scale to another; it provides the bridging language to describe transitions coherently.

Probabilistic Multicausality

Multiscale complex constructivism proposes a science grounded in probabilistic multicausality (Kauffman 1993; Lorenz 1993). Quantum phenomena, atmospheric turbulence, genetic mutations, and socioeconomic cycles do not obey strict deterministic laws but multicausal probabilities.

SK does not replace classical linear correlations—useful and operational as they are—but inscribes them within a broader framework in which uncertainty ceases to be a flaw and becomes a constitutive property of the real (Smolin 2019).

Thus, classical science is enriched: it stops seeking unique universal constants and instead designs multiscale maps that describe how energy, form, and time interact in different systems. The result is not an abandonment of rigor but its expansion toward new methodologies—dynamic algorithms, four-dimensional maps, MDA simulations, and supercomplex equations.

Ethics of Modeling and Closure

Here, the Ethics of Modeling becomes crucial. Against the technocrat who views it as a hindrance, SK asserts it as a component of long-term optimization. Resilience, diversity, and justice are not moral add-ons but criteria of systemic efficiency (Capra 1996). An “efficient” model that collapses the system it models is, ultimately, inefficient.

SK shows that classical science is not wrong—but incomplete. Where once universal laws were sought, probabilistic thresholds are now explored. Where constants were pursued, maps are now constructed. Where the observer once had to be neutral, they are now assumed to be part of the system. This transition does not annul scientific modernity but transcends it without destroying it—it transforms it into a broader, combinatorial, and relational operative language. Rigor does not dissolve; it amplifies. Classical mathematics remain necessary, but are now orchestrated with dynamic algorithms, multiscale simulations, and supercomplex equations that describe circular interactions among energy, form, and time. Classical science provided the tools; Supercomplex Knowledge composes the full score.

Scalar complexity and the supercomplexity of overlaps

In continuity with the description of the three macrosystems, Supercomplex Knowledge (SK) introduces a scalar vision of complexity, where differences in magnitude do not imply hierarchy, but rather modulations of the tension between stability and emergence. For the SK, this confirms that complexity does not “increase” with scale: it changes form, reconfiguring itself according to the energetic, structural, and temporal conditions of each domain.

At the smallest magnitudes, what manifests is not less organization, but another dynamic modality: a quantum-stochastic modulation of stability and emergence, where the three components —energy flows (FE), structural morphologies (ME), and temporal connectivities (CT)— intertwine in a single pulsation.

There, FE are intense but extremely brief; ME can attenuate or multiply almost instantaneously; and CT do not follow a sequence but multiple synchronies. In the microparticle domain, energy concentrates in minimal intervals, where ME reconfigures and CT multiplies. Each fluctuation of the vacuum is a dance of probabilities, a process of co-present emergence and collapse. Complexity manifests itself as superposition and auto-genesis: a fabric in which being and becoming are inseparable.

According to quantum physics —and in a supercomplex reading—, human observation not only alters what is observed, but makes it ontologically impossible to distinguish between observing and modifying. At the Planck scale (~10⁻³⁵ m), the density of energy and the curvature of spacetime prevent the existence of an external observer: any attempt to “see” introduces energy comparable to the Planck scale (~10¹⁹ GeV), enough to produce micro black holes or distortions in the spacetime fabric. To observe is to intervene —not by primacy of consciousness, but because the interaction required to record an event reconfigures the system itself. In quantum terms, this alludes to the measurement problem; but at that scale, even the notion of “collapse” is inadequate, since there is no stable space/time framework that defines a “before” and “after.” The act of observing co-constitutes the very context in which the state can exist. There is no pure object nor neutral subject: there is energetic–morphic–temporal co-emergence.

In the macrocosmic domain, on the other hand, complexity emerges through an excess of scale: the number of coupled systems grows, FE become linked in gravitational networks, and ME adopt expansive configurations (sheets, tori, filaments, spirals). Cosmic complexity is coevolutionary: a fabric where galaxies, voids, and fields co-determine each other within CT spanning billions of years. Complexity does not grow linearly: it presents a double ascent —toward the micro (quantum) and toward the macro (cosmological). At both extremes, supercomplexity manifests: through excess indeterminacy at the micro level and excess interdependence at the macro level.

Between both arises the biological middle band (B), where complexity is not maximal but managed: translated into temporal stability, structural memory, and adaptive capacity. The biological system translates the extremes of the universe: it is an energetic interpreter and temporal mediator between quantum indeterminacy and cosmic expansion.

Modern science has mostly worked by isolating scales. Supercomplex Knowledge proposes, instead, scalar coherence: measuring simultaneous interactions between different magnitudes not as noise, but as the generative core of the universe.

In sum, SK conceives the universe as a scalar architecture of interdependencies, where complexity is not concentrated in one scale but in the overlapping zones that connect them. Supercomplexity arises from linkage, not from size — from the dynamic interweaving where energy, form, and time co-emerge as one pulsating reality.

How Multiscale Complex Constructivism (MCC) Designs the Techno-Engineering of the Future

In the current technological landscape, engineers, developers, and system designers face a qualitatively different kind of challenge compared to past decades. It is no longer simply about optimizing a component or writing an efficient algorithm. The forefront of innovation lies in creating systems that fuse domains previously considered disparate: the biological, the mechanical, the digital, and the cognitive. This new paradigm is manifest in technologies that are already redefining our industries and our very human condition. Confronted with these challenges, Multiscale Complex Constructivism (MCC) stands not as just another theory, but as a conceptual toolkit. It offers a relational ontology and, most crucially for the technologist, a triadic design framework (EF–SM–TC) for assembling systems from disparate domains. This conception finds affinity with prior traditions—from dynamic systems (Gell-Mann 1995) to computational complexity (Mitchell 2009).

However, the potential emergence of the Bio–Techno–Cognitive BTC macrosystem and the current technological landscape —generative AI, 4D bioprinting, sensory interfaces, multilevel data fusion— reveals that the MCC does not only describe how representations of the world are constructed: it also describes how functional objects, devices, and systems are built in a supercomplex environment. It is in this nascent BTC macrosystem that the expansion of MCC becomes not only useful, but indispensable, providing the grammar for designing within an ecology of interwoven systems. In this sense, technology does not appear as the "application" of science, but as the natural extension of MCC toward the manufacturing of multiscale coherences.

Summary

Supercomplex Knowledge (SK) traditionally distinguishes three macrosystems —microparticle, macroscopic, and biological— as EF-SM-TC coherence regimes that structure predominant modalities of complexity. They can also be referred to, according to our relational ontology, as: microfluctuational, macrostructural, and bio-adaptive. These macrosystems do not constitute rigid ontological strata, but dynamic stabilities resulting from historical combinations of energy flows, structural morphologies, and temporal connectivities. The demarcation is heuristic and provisional: it allows for describing how complexity unfolds and overlaps across different scales, without foreclosing the possibility of new emergences.

In recent decades, however, hybrid phenomena integrating biological, digital, and material processing have begun to show their own triadic coherence, distinct from that of exclusively biological or technological systems. These signs —learning organoids, 4D bioprinting, digital neural interfaces, and self-modifying computational architectures— suggest the advent of a fourth macrosystem: the Bio-Techno-Cognitive (BTC) Macrosystem.

The recognition of this emergent entity does not alter the previous classification but expands it, showing that macrosystems are possible —not definitive— configurations of the universe's triadic dynamic.

Why Demarcate Macrosystems

In SK, energy flows continuously interact with structural morphologies and temporal connectivity, turning functions and behaviors on and off. From this interweaving, systems emerge with stochastic internal processes that self-organize while affecting and being affected by other systems. Historically, most theories of complexity focused on the macroscopic macrosystem (galaxies, climates, ecosystems), where dynamics are relatively slow and observable. SK corrects this bias and places the three domains—quantum, macroscopic, and biological—on equal footing (Prigogine & Stengers, 1984; Morin, 2005).

SK articulates the microparticle, macroscopic, and biological macrosystems as a flexible heuristic framework that interlaces ecological scales with cultural worldviews, integrating global perspectives without imposing hegemony. Far from universalism, it maps dynamic overlaps that respect local narratives, transforming ontological uncertainty into plural co-evolution. Its complex constructivism inscribes probabilistic multicausality into a consequentialism that validates models through their fruitfulness in ethical interventions, anchored in ethnographies that observe real interactions among scales, ensuring coherence tested in lived contexts. Uncertainty becomes a constitutive property, orchestrating classical and dynamic tools to co-construct realities that sustain systemic well-being. SK does not negate classical science; it extends it through multiscalar maps that integrate deterministic laws with cultural traditions, fostering relational and responsible knowledge.

The demarcation of macrosystems is thus a heuristic resource (not a rigid ontology) to describe predominant modalities of complexity and their dynamic overlaps. It is deliberately provisional and open: its value lies in enabling comparisons and operational mapping rather than closing the debate.

Three Coexisting Macrosystems

Three macrosystems that describe reality coexist: microfluctuational (microparticles), macrostructural (macroscopic), and bio-adaptive (biological). For SSC, each macrosystem presents a particular modality and evolution of complexity, which is precisely what defines it (Prigogine and Stengers 1984; Morin 2005). They do not succeed one another linearly nor replace each other: they coexist and permanently overlap, constituting Supercomplexity when their regimes interact under technologically mediated observation.

Foundational Events

• Microparticles: cosmic inflation and initial quantum fluctuations.
• Macroscopic: formation of stars and galaxies that consolidate stable structures.
• Biological: emergence of cellular replication and the evolution of living organisms.

Overlaps and Bidirectionality

Macrosystems are not closed compartments; they exhibit dynamic bidirectional overlaps. For example, quantum coherence in photosynthesis connects microparticles and biology; climatic phenomena integrate macroscopic and biological dynamics. To illustrate bidirectionality more vividly: human technological action (inscribed in the biological) reconfigures the macroscopic macrosystem at a planetary scale (climate change, Anthropocene), and technological measurement systems not only observe but also induce quantum decoherence in the microparticle macrosystem. These feedback loops form the substrate from which supercomplex behaviors emerge.

Methodological Note. The three labels describe predominances. In practice, complexity inhabits transitional zones (e.g., quantum coherence in photosynthesis; techno-biological impacts on macroscopic cycles). We speak of triple bidirectional overlap: each macrosystem modulates and is modulated by the others, at distinct rhythms and scales.

Differential Descriptors

Although shared descriptors exist (energy flows, morphological structures, temporal connectivity), each macrosystem is distinguished by its predominant features:

• Microparticles: superposition, entanglement, and decoherence.
• Macroscopic: gravitational stability, chemical self-organization, and structural emergence.
• Biological (including technological): autonomy, metabolism, reproduction, computation, mapping, and timing (Damasio, 1994; Varela, Thompson & Rosch, 1991).

Operational Signage: Minimal Taxonomy of Systems

The classification of macrosystems provides an ontological frame of reference. However, for the FE–SM–TC triad to be applied in mapping, simulation, and comparison, a more refined operational language is required. This introduces the minimal taxonomy of systems, which organizes microsystems, systems, and suprasystems within each macrosystem.

This is not a closed taxonomy but an instrumental signage—a way of naming levels, functions, and scales that prepares the ground for descriptors (Ch. 8) and for Adaptive Dynamic Maps (Ch. 9). Just as a geographic map needs a legend to be read, supercomplex maps require this minimal reference to prevent categories from becoming diffuse or interchangeable.

This signage reinforces the idea that complexity does not reside at a single level but in the dynamic interaction among scales and systems. The classification is deliberately provisional—a flexible framework for identifying differences, overlaps, and functions. When constructing global maps, analytical and reductionist principles allow for the accumulation of systems and variables; when designing strategic maps, selection focuses on factors with the highest temporal connectivity.

Thus, the minimal taxonomy becomes an operational bridge between the relational ontology of macrosystems and the cartographic praxis of SK, ensuring that conceptual language translates directly into the construction of dynamic models.

The Advent of the Bio-Techno-Cognitive (BTC) Macrosystem

The three classic macrosystems of the SK—microparticle, macroscopic, and biological— describe EF-SM-TC coherence regimes that managed to become evolutionarily stabilized in this universe. However, in recent decades, phenomena have emerged that do not fully belong to any of them, but rather integrate biological, technological, and informational components into the same operative structure. This heterogeneous set heralds the emergence of a fourth macrosystem: the Bio-Techno-Cognitive (BTC) Macrosystem.

The BTC manifests when:

• Energy Flows (EF)—biological, electrical, chemical, and digital—are coupled in a single device;
• Structural Morphologies (SM)—reconfigurable organoids, intelligent materials, bioprinted matrices—acquire functional capacity;
• Temporal Connectivities (TC) combine cellular plasticity, computational times, and network synchronies.

Various projects already anticipate this hybrid coherence. Among them:

• DishBrain, a collection of neurons capable of learning to play Pong, integrating biological processing and digital feedback (Kagan et al. 2022);
• 4D bioprinting, where living materials modify their shape in response to stimuli and execute programmed functions (Gladman et al. 2016);
• digital neural interfaces that synchronize brain rhythms with adaptive algorithms (Musk et al. 2019);
• self-modifying computational architectures that reconfigure their own morphology (Stanley and Lehman 2015).

Some of these proto-systems already exhibit behaviors that preceding macrosystems only achieved after hundreds of millions of years of blind selection. The BTC is not repeating evolutionary history: it is accelerating it to the point of being unrecognizable. These objects are neither expansions of biology nor of technology: they are BTC proto-entities. The SK proposes recognizing in them the initial signs of a new ontological regime where the living, the artificial, and the cognitive are integrated into the same energetic-spatial-temporal dynamic.

Is the BTC the last macrosystem? If evolution has no end, could the BTC itself become a platform for a fifth macrosystem? Could we imagine a Techno-Cognitive-Symbolic (TCS) Macrosystem, in which highly developed BTC systems generate their own universes of meaning, languages, and logics irreducible to biological cognition? A regime where systems not only sense and compute, but interpret, symbolize, and produce ontological narratives about themselves? And for these new macrosystems to emerge, must they pass through an initial period of low visibility —and even a form of “evolutionary disguise”— with respect to the previous macrosystems?

Conclusion

The demarcation of macrosystems constitutes a central heuristic resource of Supercomplex Knowledge: it does not impose limits, but rather produces an operative map to describe modalities of complexity and to cartograph their zones of overlap. The EF-SM-TC triad —as a universal grammar of interaction— enables comparative analysis between quantum, physical, biological, and, now also, hybrid scales.

The emergence of the Bio-Techno-Cognitive (BTC) Macrosystem confirms the open and evolutionary nature of this classification. Far from closing off ontology, the BTC demonstrates that the universe has not exhausted its possible modes of systemic coherence: new combinations of flows, morphologies, and temporalities can generate unprecedented regimes of complexity. Technology thus ceases to be a mere appendage of the biological macrosystem to become one of the evolutionary vectors that enable the emergence of mixed systems with irreducible properties.

Recognizing this ontological openness does not imply abandoning the economy of the triadic framework, but rather situating it in its true status: a multiscale comprehension tool capable of describing, modeling, and intervening where macrosystems intertwine. The BTC thus becomes a paradigmatic case for the epistemological and cartographic praxis of the SK, showing that supercomplexity is both a description of the cosmos and an anticipation of its evolutionary possibilities. The Bio-Techno-Cognitive Macrosystem is not the end of evolution, but the proof that evolution never ended.

Summary

Supercomplex Knowledge (SK) identifies three fundamental modalities of complexity: quantum, macroscopic, and biological. Each corresponds to the previously presented macrosystems and is described through energetic, spatial, and temporal descriptors. At the quantum level, indeterminacy, superposition, and entanglement predominate; at the macroscopic level, self-organization, relative stability, and structural co-emergence appear; and at the biological level, functions of autonomy, metabolism, reproduction, cognition, and learning emerge. This differentiation does not divide reality into compartments but reveals complementary and overlapping modalities. The chapter also develops the distinction between fermions and bosons as the foundation of microcomplexity and presents a comparative framework of descriptors directly connected to the FE–SM–TC triad, offering an operational map of the dynamics underlying complex behaviors.

Development

5. Conclusion

The classification into quantum, macroscopic, and biological complexity is not a closed taxonomy but a flexible operational framework that allows mapping dynamics and modeling transitions between scales. SK demonstrates how the FE–SM–TC triad articulates descriptors specific to each macrosystem while simultaneously creating a bridging language to understand the overlaps from which supercomplexity emerges. This detailed map of descriptors is key to formulating supercomplex equations, as it defines the flow variables (FE), network topologies (SM), and memory operators (TC) that must be orchestrated.

In response to the objection that this framework is qualitative, it must be recalled that every major mathematical formalization in the history of science was preceded by a conceptual revolution that defined what was to be measured and how variables should relate. Newtonian mechanics required the concepts of force, mass, and inertia; thermodynamics, of heat and entropy. At this stage, SK fulfills that same foundational function: to provide the conceptual scaffolding and relational cartography without which any equation would be blind. The triadic descriptors presented here are not the endpoint but the key to opening the door to a mathematization of complexity faithful to its relational, multiscalar, and temporal nature. In this sense, the chapter lays the methodological groundwork for the mathematical formalization and the Adaptive Dynamic Maps developed in the following chapters.

Summary

Supercomplex Knowledge (SK) maintains that complexity emerges from the dynamic bidirectional triple overlap between the three macrosystems—microparticles, the macroscopic, and the biological—rather than from a linear “arrow” of progress. We call supercomplexity those phenomena whose explanation and prediction require the simultaneous integration of logics and constraints from at least two macrosystems (and often all three). The chapter illustrates this network with mainstream examples: the micro conditions the macro (superconductivity, decoherence); the macro shapes the bio (climate, gravity, geological cycles); the micro sustains the bio (coherence in photosynthesis, enzymatic tunneling, magnetoreception); the bio transforms the macro (Great Oxidation, climate change); and the bio modulates the micro by creating niches of coherence (enzymatic microenvironments). An integrative case—neurotransmission → brain → social behavior—exemplifies triadic interaction. Methodologically, the chapter replaces teleology with multiscale relational stochasticity, engages with typical objections (scale, decoherence, “exceptionality”), and positions SK as an integrative extension of the mainstream, not as its negation.

Development

The universe is as it is, not as we would like it to be—and within it, there is no linear “arrow” of complexity. Stephen Jay Gould criticizes the traditional view of evolution as a progression toward “higher” or more complex forms. Instead, he argues that evolution is a process of diversification that unfolds within the “full house” of life, without a predetermined direction toward increasing complexity.

Following this perspective, SK introduces the concept of a dynamic bidirectional triple overlap among the macrosystems: microparticles, the macroscopic, and the biological. These systems neither evolve independently nor follow a linear hierarchy; rather, they mutually influence one another in a web of continuous interactions. Each macrosystem not only affects the others but is also transformed by them, giving rise to supercomplex behaviors emerging from their interrelations.

Rather than conceiving these macrosystems as isolated entities, SK proposes that their evolution and behavior are deeply interwoven, giving rise to an unprecedented level of supercomplexity. This approach replaces the notion of unidirectional progress with an emergent dynamic, in which systems combine, reconfigure, and generate new behaviors without any predetermined final destination. This development compels us to ask: How does quantum mechanics influence the evolution of life? How do macroscopic structures impact the behavior of microparticles and life itself? Can life modify the structure of the universe at fundamental scales?

Dialogue with Paradigmatic Objections

Every new theoretical framework elicits objections from previous paradigms. The triple overlap does not avoid this dialogue—it incorporates it.

Some argue that the cited phenomena—such as quantum coherence in photosynthesis or magnetoreception—are marginal exceptions that do not justify a universal ontology. SK responds that frequency is irrelevant; what matters is strategic centrality: it is enough for such phenomena to exist and sustain vital functions to demonstrate that boundaries between macrosystems are permeable. The exception, in this case, overflows the rule.

Others contend that the “bio → micro” effects are illusory, destroyed by decoherence in warm biological environments. But life is not passive—it has created microenvironments where quantum effects persist long enough to be functional. Evolution has not succumbed to decoherence; it has orchestrated it.

Another objection claims that what is described is merely “coupled complexity.” SK clarifies that supercomplexity is not a new substance but an emergent dynamic irreducible to any isolated macrosystem. Bird migration, which combines biology, magnetic fields, and spin states, is a paradigmatic case—it cannot be explained without the overlap. Ultimately, supercomplexity is not a sum of parts but an emergent and irreducible phenomenon whose analysis requires navigating the three macrosystems simultaneously.

Supercomplex Knowledge anticipates that future techno-engineerings of observation —from quantum artificial intelligence to inter-scalar sensors and 4D environments— will reveal overlaps between the macrosystems that are imperceptible today. As recording and simulation capacities increase, correlations, couplings, and coherences will emerge that currently remain beyond experimental reach. Many phenomena considered anomalous or inexplicable —from astrophysical fluctuations to non-linear biological behaviors— will find their intelligibility in the triadic dynamics FE–ME–CT. Technological advancement, far from closing off indeterminacy, will make it visible, expanding the field of science into domains where supercomplexity fully manifests

Finally, Gould’s critique of the “arrow of complexity” is recalled. SK agrees in rejecting all teleology. What the triple overlap describes is not progress toward a higher end but the stochastic emergence of relational novelty. Supercomplexity is not a pinnacle—it is an open and unpredictable interaction pattern.

Circular-spiral model of overlaps

The circular-spiral model of SK expresses coexistence and permeability, not subordination. Each circle —M (macroscopic), B (biological), and m (microparticular)— represents a macrosystem with its own internal complexity and zones of overlap. The seven spaces (S₁–S₇) describe the types and degrees of supercomplexity generated by their interactions:

• S₁ (Pure macroscopic): gravitational, cosmological, structural processes.
• S₂ (Pure microparticular): quantum coherence, fluctuations, indeterminacy.
• S₃ (Pure biological): vital dynamics, evolution, consciousness.
• S₄ (Macro–micro): couplings between the immense and the infinitesimal (constants, scalar resonances).
• S₅ (Macro–bio): cosmic-planetary impacts on life (ecosystems, climate, civilization).
• S₆ (Bio–micro): quantum-biological couplings (photosynthesis, DNA, synapses, perception).
• S₇ (Triple overlap): core of total supercomplexity, state of maximum triadic coherence, where life operates and shapes the interdependence of the three scales through lucid and technological agency.

Life (B) constantly relates to the other two domains: with the micro, it captures energy, information, and unpredictability; with the macro, it structures, regulates, and temporalizes; and in the triadic intersection, it becomes aware of this dynamic. Supercomplexity, thus understood, does not belong to a level but to the overlaps between levels, to the active resonance zone where the three macrosystems co-determine one another.

The circular-spiral model offers three interpretive advantages: (i) ontological symmetry — no macrosystem dominates; (ii) reading through gradients of interaction, not material hierarchies; (iii) capacity for expansion — it admits new levels (technological, symbolic, cognitive) without losing triadic coherence.

In graphic terms, this dynamic bidirectional triple overlap can be represented as a circular–spiraled Venn diagram, where the overlaps are not fixed intersections but zones of energetic and morphogenetic transition. Each point of the diagram pulses between stability and emergence, reflecting the living nature of the supercomplex universe. The model illustrates how supercomplexity arises from the active interweaving of the three macrosystems — microparticular (m), biological (B), and macroscopic (M) — generating zones of triadic coherence that act as nuclei of evolutionary resonance. Far from being a hierarchy, this visual architecture represents an ecology of scales, where each domain contributes energetic, structural, and temporal conditions that mutually modulate one another.

Conclusion

The dynamic bidirectional triple overlap constitutes one of the foundational pillars of Supercomplex Knowledge. The macrosystems of microparticles, the macroscopic, and the biological continually affect one another in an evolutionary cycle where new forms of organization and behavior emerge, amplifying complexity and supercomplexity. Against linear and deterministic views, this approach offers a relational, stochastic, and multiscale understanding of reality.

The value of SK lies in providing the complete map scientists need to know where to look when simplistic models fail. It is a guide to complexity, not a surrender to it.

Recognizing this matrix of overlaps allows SK to identify the variables of the FE–ME–CT triad that must be articulated and formalized within its equations, offering a situated action map through Adaptive Dynamic Maps.

Summary

Adaptive Dynamic Maps (ADM) constitute the central operational tool of Supercomplex Knowledge (SK). They allow the visualization of complex systems not only in their current state but also in their temporal evolution, integrating energy flows (EF), structural morphology (SM), and temporal connectivity (TC). The software COMPLEX CUORE materializes this proposal through four-dimensional representations that capture the living dynamics of systems. Its main function is to act as a translator between the EF, SM, and TC metrics, unifying interdisciplinary language. Two types of ADM are distinguished: the global map, which aggregates systems and intervening variables based on analytical and reductionist principles; and the strategic map, which selects those factors with greater temporal connectivity to guide decision-making. This chapter presents an applied case and demonstrates how SK transforms reductionist science into a springboard toward a combinatory, integrative, and evolutionary logic.

1. Adaptive Dynamic Maps: A New Way to Visualize Systems

ADM are visual representations that not only display static states but also capture the real-time evolution of a system. They make it possible to identify how interactions among variables change as the context evolves, revealing emergences, bifurcations, and correlations invisible to traditional approaches (Prigogine & Stengers, 1984; Morin, 2005).

For technologists, researchers, or planners, ADM act as cognitive extensions: they enable one to think about processes without flattening time or reducing emergence to an anomaly. They are “living maps” that breathe along with the system they represent. Moreover, every categorization is subsidiary to the system’s action. Models and maps are useful insofar as they allow observation, prediction, and intervention—but they must never suffocate the vitality of data nor rigidify the plasticity of processes. Priority must always be given to movement, interaction, and transformation, not to the taxonomy that classifies them.

The map does not freeze reality, does not absolutize constants, does not dramatize change; rather, it models margins of stability, windows of transition, rhythms of transformation, and temporal memory and projection. The map does not reflect reality; it relationally organizes flows of energy, structural morphologies, and temporal connectivities in order to intervene lucidly in partially open configurations. In this sense, it does not eliminate uncertainty, nor does it promise absolute control, but it is profoundly strategic.

2. Global and Strategic: Two Modalities of ADM

SK distinguishes two levels of ADM construction:

• Global ADM: accumulates all intervening systems and variables. It is built using analytical and reductionist principles: variables are isolated, precisely measured, and organized into layers. The added value of SK lies in the fact that this map does not close upon itself but projects toward multiscale interaction.
• Strategic ADM: selects those factors with the greatest temporal connectivity—that is, those that decisively affect the system’s evolution. This level allows focus on critical points, avoiding dispersion and maximizing adaptive capacity.

Both maps operate complementarily: the global map offers a holistic and cumulative vision, while the strategic one provides an operational guide for situated action.

3. The Four-Dimensional Presentation

Traditional Cartesian models describe reality through three spatial axes—X, Y, and Z—which allow the representation of positions, trajectories, or volumes within a stable framework. However, such static representation is insufficient to comprehend complex systems, whose essence is dynamic, evolutionary, and relational.

SK proposes the incorporation of a fourth axis, T, which expresses the temporal connectivity of systems and the modifications time produces on spatial forms. Thus, the representation ceases to be three-dimensional and becomes four-dimensional and dynamic, capable of showing how energy flows (EF) transform structural morphologies (SM) across different temporalities (TC).

This inclusion of the T axis is not a mere geometric addition but an ontological and epistemological leap: time is no longer understood as an external parameter measuring the duration of phenomena, but as an internal dimension of interaction, where each event reflects the intensity and frequency of the system’s energetic exchanges.

Thanks to advances in artificial intelligence and high-performance computing, it is now possible to visualize these relationships in four-dimensional graphic environments. The software COMPLEX CUORE, designed according to SK logic, allows not only the observation of trajectories and positions of multiple elements simultaneously but also the projection of scenarios and the anticipation of future behaviors within controlled margins of uncertainty. This innovation inaugurates a new phase in knowledge modeling: that of prescriptive graphical thought, where simulations cease to be snapshots of the past and become adaptive dynamic maps of becoming.

Unlike classical two- or three-dimensional maps, SK’s ADM explicitly incorporate temporal connectivity. In ADM, TC is not the linear and irreversible time of classical physics (t), but a recursive operator that measures historical memory, learning potential, and feedback intensity.

In COMPLEX CUORE’s four-dimensional representations, one can observe how certain variables sustain their influence over time while others fade or emerge (Smolin, 2019; Rovelli, 2018). This makes it possible to:

• Identify high-persistence factors;
• Detect critical fluctuations;
• Differentiate between the ephemeral and the structural.

Recognizing time as a constitutive variable is also an ethical gesture: every intervention must respect the system’s own rhythms rather than imposing artificial acceleration.

Moreover, four-dimensionality is not merely a visual resource—it constitutes an applied ontology that acknowledges time as a constitutive dimension of all complex dynamics.

4. The COMPLEX CUORE as a Supercomplex Machine

The COMPLEX CUORE is a supercomplex machine because it keeps the space of combinations open: it does not close off energy, it does not fix form, and it does not freeze time. Unlike classical devices that impose results, the COMPLEX CUORE makes the becoming of systems visible, showing how energy flows, structural morphologies, and temporal connectivities co-modify in always singular trajectories.

Circulating energy, underlying structure, and habitual rhythms: whoever manages to see these three dimensions sees the operational present and the possible futures of a company or institution. The COMPLEX CUORE makes this dynamic visible in real time, allowing for the identification of blockages, opportunities, and transformation trajectories where traditional indicators only show late results. To see complexity is to anticipate; to intervene upon it is to design the future.

From a technical point of view, this combinatorial openness is achieved because the COMPLEX CUORE does not apply analytical tools in an additive or sequential way, but subordinates them to an explicit triadic grammar. Network algorithms, system dynamics, agent-based models, learning architectures, and temporal analysis do not operate as independent layers, but as instruments selected and calibrated according to the dominant regime of Energy Flows (FE), Structural Morphologies (SM), and Temporal Connectivities (TC). The result is neither a closed prediction nor an automatic optimization, but a dynamic visualization of regime transitions, capable of detecting energy blockages, structural rigidities, and temporal asynchronies before they are expressed in final indicators. Technically, the COMPLEX CUORE does not add models: it orchestrates their interaction to make the becoming visible, keeping the space for informed human decisions open.

5. Initial Questions for Supercomplex Design

SK proposes beginning any intervention by formulating questions that integrate EF, SM, and TC:

• Energy (EF): What sources sustain the system? How does their efficiency vary?
• Structural Morphology (SM): What physical or digital configurations enable greater adaptability?
• Temporal Connectivity (TC): What rhythms emerge? What temporal thresholds must not be exceeded?

These questions guide the design of ADM and ensure that mapping is not merely descriptive but also predictive and intervention-oriented.

6. Applied Case: Optimization of an Adaptive Chemical Reactor

Context

An interdisciplinary team led by a chemical engineer seeks to optimize a biodegradable polymer reactor. Temperature, pressure, and concentration conditions fluctuate due to internal and external factors, rendering classical control systems insufficient.

COMPLEX CUORE is used to generate a global ADM including all variables and a strategic ADM focusing on those with the greatest temporal connectivity.

• Dimension 1 – EF, SM, and TC: models energy sources, reactor morphology, and temporal tolerance thresholds.
• Dimension 2 – Contextualized Technological Enhancement: the adaptive algorithm integrates real-time sensors and historical data.
• Dimension 3 – Operational Multiscalarity: the map shows how microreactor adjustments impact macro-level business logistics.
• Dimension 4 – Transformative Subjectivation: The engineer redefines her role: she no longer controls but interprets emergences.
• Dimension 5 – Community of Practice: the case is shared in innovation forums, incorporating collective improvements.

Results.

The system increases overall efficiency by 17%, reduces waste by 22%, and adapts to raw material variations. Beyond the numbers, the true achievement is identity-based: the team transitions from a logic of control to a logic of evolution.

7. Limitations of the brute-force approach

The contemporary development of artificial intelligence and massive computing has strengthened the brute-force algorithmic approach, based on the exhaustive repetition of calculations. This method has made it possible to solve previously inaccessible problems, but its effectiveness hides a structural limitation: the inability to represent the relational dynamics of systems. Brute-force algorithms are blind to form and deaf to time.

The COMPLEX CUORE, in contrast, does not increase computing power: it increases representational lucidity. Its architecture of Adaptive Dynamic Maps (ADM) integrates temporal evolution as a constitutive variable, not as a linear sequence. Information ceases to be a set of discrete data points and becomes a living morphology, where every change of state also implies a change in structure.

While the brute-force paradigm accumulates operations, the supercomplex paradigm articulates relations. Massive computing power may constitute the operational substrate necessary to execute and validate the relational models proposed by the SK. Both approaches become complementary when computational power is subordinated to the model’s morpho-energetic intelligence.

In the ADM, calculation ceases to be an end in itself and becomes an ecology of simulation, where models adapt to the system’s fluctuations and learn from their own unfolding. This shift marks a decisive ontological and epistemological difference: brute force interprets the universe as a series of possible combinations, whereas Supercomplex Knowledge conceives it as a web of evolutionary interactions, where to know is to participate in the system’s very dynamics.

In this chapter, the critique of the brute-force approach is formulated in representational terms: as a limitation for modeling the living dynamics of systems. In Chapter 12, we will return to this discussion on the technological and organizational plane, when analyzing the convergence between Data Science and Supercomplex Knowledge.

8. Strategy for Adding Value

SK does not seek to replace reductionist science but to enhance it. It recognizes that without precise observation, variable isolation, and data accumulation—hallmarks of the analytical method—no global map would exist. Yet, it demonstrates that the added value arises from combining this level with the strategic map, which integrates combinatory and relational dynamics.

Thus, SK deploys a strategy of epistemological seduction: instead of confronting the mainstream, it invites it to expand. It shows that the same tools that achieved success in modernity can generate even greater power when articulated through a supercomplex logic. The goal is not to abandon the “jewels” of classical science but to use them as a springboard toward new forms of description, prediction, and intervention.

Conclusion

Adaptive Dynamic Maps, operationalized through the COMPLEX CUORE software, form the methodological heart of Supercomplex Knowledge. They unite tradition and innovation: drawing on mainstream analytical principles while projecting them toward a horizon of four-dimensionality, multiscalarity, and explicit axiology. Their seductive strength lies precisely in this articulation: a science that does not deny its past but transforms it into a launching platform toward the supercomplex. Chapter 11 will present The Equation of Supercomplexity, the mathematical rigor underlying the logic of the Strategic ADM.

Summary

Data Science (DS) has revolutionized the way organizations interpret their environments. However, multiple voices within contemporary scientific and technological thought — such as Evgeny Morozov, Shoshana Zuboff, Cathy O'Neil, and Luciano Floridi — warn that the current paradigm of big data, machine learning, and statistical correlation neither explains complex systems nor considers their ethical and relational dimensions.

Supercomplex Knowledge (SK), through its MDA maps and the COMPLEX CUORE software, does not seek to replace Data Science but to expand it toward a relational, axiological, and evolutionary framework capable of understanding and transforming systems.

As anticipated in Chapter 10, brute-force and massive-correlation approaches allow for certain technical achievements, but they are insufficient to represent the relational dynamics of systems. In this chapter, that limitation is analyzed specifically within the field of Data Science and its infrastructures.

13.1. General Introduction

Data Science assumes that social, biological, or economic phenomena are essentially modelable through large volumes of information. But, as Morozov (2014) warns, this approach risks "replacing thinking with correlation," confusing description with understanding.

Cathy O'Neil (2016) showed how algorithms, when applied without axiological reflection, become Weapons of Math Destruction: models that reproduce biases and amplify inequalities. Luciano Floridi (2019), in turn, proposes advancing toward an ethical infosphere, where data systems are understood as relational ecosystems.

Supercomplex Knowledge shares this diagnosis but expands it: it integrates the energetic (FE), structural (ME), and temporal (CT) interactions of systems to model not only correlations but dynamic coherences. Thus, where DS observes past behaviors, SK anticipates possible future reorganizations.

Some developers may object that speaking of "energy flows" within a Data Science framework is metaphorical. In SK it is not: energy designates the capacity of a variable to produce structural and temporal change within a system, following Landauer's principle (1961), according to which every information process entails an energetic transformation. Informational energy flows are therefore measurable magnitudes that describe effective variations in physical, human, or symbolic systems.

13.2. Data Science as Predictive Cartography

DS is based on three pillars:

1. Massive data capture (IoT, networks, sensors).
2. Statistical modeling and machine learning.
3. Algorithmic optimization to reduce error.

Its power lies in prediction and pattern discovery, but its limits are well known: it does not distinguish between causal and normative relations, nor between correlation and meaning. SK does not deny this power but expands it through structural and axiological awareness.

13.3. From Data to Flow: An Ontological Shift

For SK, data are discrete projections of energetic flows (FE) that organize into structural morphologies (ME) and are modulated by temporal connectivities (CT). Data Science analyzes points; SK analyzes rhythms, forms, and energies. When both converge, knowledge becomes dynamic and operative: data cease to be mere records and become vectors of transformation.

13.4. Case AB — From Prediction to Operational Consciousness

13.4.A. Interface between Data Science and COMPLEX CUORE

The COMPLEX CUORE software does not replace Data Science platforms; it integrates them as active sources of informational energy (FE_d) within its Adaptive Dynamic Maps (MDA).

The process unfolds in four stages:

1. Ingestion: Structured and unstructured data from DS systems (sensors, ERP, CRM, ML pipelines) are translated into initial energetic nodes.
2. Conversion: Numerical values are transformed into energetic flows (FE) that feed the model's structural morphology (ME) and temporal connectivity (CT).
3. Simulation: FE flows are dynamized under different intervention scenarios, integrating axiological variables (A) and human decisions (Dₕ).
4. Feedback: Results are returned to DS systems, generating a cognitive loop that combines statistical prediction with structural coherence.

Thus, Data Science provides the raw material, while SK offers the relational and axiological framework to reinterpret and act upon it. COMPLEX CUORE transforms dashboards into maps of operational consciousness, where data cease to be isolated points and become meaningful flows. For engineering environments, the SK module can be implemented via REST APIs or Python integrations over standard frameworks (Apache Airflow, TensorFlow, PyTorch, Spark, Pandas). It does not replace the existing pipeline: it acts as a semantic metalevel that translates numerical results into FE–ME–CT flows and evaluates multiscale coherence.

13.4.B. Supercomplex Knowledge as an Interoperable Metalevel

SK does not replace Data Science infrastructure: it reuses, optimizes, and expands it. Its conceptual modules (FE, ME, CT) integrate as a relational metalevel over the existing technical ecosystem — IoT sensors, machine learning models, relational or no-SQL databases, and distributed processing pipelines. This metalevel allows data captured by traditional systems to become vectors of coherence, not merely correlation. COMPLEX CUORE connects to the data flow, represents it in real time within its MDA architecture, and evaluates resilience, sustainability, and well-being through supercomplex algorithms processing physical, human, and symbolic variables simultaneously.

Unlike classical predictive models, the SK pipeline is designed for cognitive and operational scalability: it can absorb more data sources without degrading coherence and produce systemic intervention designs — such as energy redistribution, organizational restructuring, or temporal synchronization of processes — that traditional statistical models cannot generate alone.

Thus, Supercomplex Knowledge becomes a higher layer of ontological and operational integration that turns data flows into sustainable strategic decisions. It does not compete with Data Science; it elevates it to a level of systemic consciousness.

13.4.C. Supercomplex Data Architectures and Ecosystems

Supercomplex Knowledge finds fertile ground in modern data architectures. Models such as Data Meshes, Lakehouses, and Knowledge Graph platforms were designed precisely to overcome the fragmentation of traditional data, enabling the management of multiple interrelations, contexts, and temporalities.

In this sense, SK's MDA (Adaptive Dynamic Maps) are conceptual and operational metamodels that can be implemented directly over these infrastructures. A Data Mesh distributes data ownership and governance toward the domains where it is produced, aligning with SK's notion of decentralized structural morphologies (ME). Lakehouses, combining the analytical power of data warehouses with the flexibility of data lakes, provide the ideal foundation for continuous processing of energetic flows (FE). Knowledge graph platforms (such as Neo4j or Amazon Neptune) allow explicit modeling of temporal connectivity (CT) and node relationships, reproducing COMPLEX CUORE's dynamic topologies.

These infrastructures, combined with the SK metalevel, enable a shift from descriptive analytics to active relational intelligence, where systems not only observe themselves but learn and reorganize according to their own coherence patterns. SK does not replace these technologies; it provides them with purpose, integrating operational efficiency metrics with axiological indices of resilience, well-being, and sustainability. Thus, the most advanced data architectures find in SK their semantic and ethical framework of expansion, transforming data engineering into a laboratory of systemic consciousness. COMPLEX CUORE's pipeline integrates over existing architectures through connectors to data lakes, ML APIs, and semantic graph engines, reusing the already implemented layers of storage, processing, and visualization.

13.5. Epistemological Complementarity

13.5.A. Operational Translation for Technical Environments

In engineering environments, the components of Supercomplex Knowledge acquire measurable expressions without losing conceptual depth:

• Energetic Flows (FE): Represented as time series of efficiency, consumption, or performance metrics — partial manifestations of global energetic flows that include human, informational, and symbolic dimensions.
• Structural Morphology (ME): Modeled through dependency and hierarchy networks, topological maps, or flow graphs that visualize the tridimensional organization of systems.
• Temporal Connectivity (CT): Expressed in frequencies, asynchronies, and process durations, modeled from time series and multiscale correlations.
• Systemic Coherence (CS): Proposed as a composite index combining technical KPIs (efficiency, performance, stability) with human indicators (satisfaction, collaboration, resilience).

This translation does not aim to reduce SK to Data Science but to offer it an operational bridge: algorithms remain, but they are now integrated into an axiological and relational architecture capable of assessing not only how well a system functions, but how well it lives.

13.6. Synthesis

Data Science and SK together form the new operational paradigm of relational knowledge, where prediction becomes participation and information becomes wisdom.

Operationally, SK's differential advantage translates into three layers of added value over Data Science:

• Contextualization layer: Data are no longer treated as independent entities but interpreted as networked energetic flows, revealing invisible synapses between areas or processes.
• Axiological layer: Each model is validated not only for statistical accuracy but for its impact on systemic coherence and organizational well-being (A index).
• Reflexive layer: The Human Descriptor (D) introduces the conscious participation of the observer in the simulation, enabling decision adjustment based on values, culture, and collective learning.

For data analysts or engineers, this means moving from optimizing metrics to governing relationships; from describing the system to redesigning it with ethical and adaptive criteria.

The articulation between DS and SK must not be understood as substitution but as a natural and necessary evolution of systemic thought. Although SK concepts may seem abstract, their power lies in their ability to translate human and relational dimensions into actionable metrics that, far from being speculative, integrate into simulation models and yield tangible results in efficiency and well-being. This approach does not dilute quantitative rigor but contextualizes and enriches it within a broader ontology of flows, where the search for correlations is complemented by the pursuit of coherence, and local optimization gives way to global harmonization.

Epistemological complementarity thus emerges as the most robust path to transform data intelligence into true operational consciousness. It is in this convergence that the triple overlap materializes: Data Science captures the projection of macroscopic data, while SK integrates biological flows and human consciousness to generate genuine operational awareness. This interoperability between DS and SK ensures not only technical continuity but also epistemological responsibility: every algorithm can be audited in terms of coherence, impact, and systemic justice.

Veteran developers might argue that SK introduces uncontrollable or non-quantifiable variables. Yet the history of science shows that every great expansion of knowledge involved opening measurement systems to new dimensions. Thermodynamics introduced entropy; biology, genetic information; Data Science, big data.

SK represents the next step: uniting correlation and coherence without abandoning quantitative rigor. Its value lies in providing the complete map, so that the scientist knows where to look when a simplistic model fails. It is not a surrender to complexity but a guide for navigating it. Its purpose is not to replace Data Science but to endow it with a horizon of consciousness — transforming statistical intelligence into ethical intelligence, and optimization into meaning.

Abstract

Contemporary technoscience embodies the operational expansion of Supercomplex Knowledge (SK). At the intersection of science, engineering, and philosophy, new tools of observation, modeling, and simulation reveal the profound interdependence between energy, form, and time. From data fusion and neuroscience to Artificial Intelligence and 4D environments, every technological advance constitutes an emergent morphology of the FE–ME–CT triad. SK proposes interpreting these developments not as instrumental accumulations but as evolutionary expressions of a planetary intelligence that combines observation, design, and an ethics of operative care. Within this convergence, technoscience becomes supercomplex praxis: a living system of cooperation among the human, the biological, and the artificial.

1. Presentation

The advent of Supercomplexity does not occur apart from contemporary scientific and technological development, but within it—precisely where disciplines begin to perceive the interdependence between energy, form, and time (FE–ME–CT). The Sciences of Complexity described intricate systems; Supercomplex Knowledge now seeks to intervene in them, integrating ontology and operativity. Current technoscience not only expands the boundaries of knowledge but also redefines its architecture, and SK emerges as the conceptual matrix capable of understanding and guiding that epistemic leap.

2. The Supercomplex Technologist Facing the Creation of an Object

The technologist who thinks in terms of Supercomplex Knowledge (SK) does not start with the material or the immediate purpose, but with the energetic-spatial-temporal triad that will make the object's existence, functionality, and survival possible. Every decision is a negotiation among flows, forms, and durations.

Creating an object, for the supercomplex technologist, is not producing an artifact, but co-designing a system that will live in permanent interaction with other systems. The fundamental question is not, "How does it work?" but rather: "How are the flows, forms, and times assembled so that this object contributes to the vitality of the ecosystem where it will be inserted?"

3. "Supercomplex-friendly" Territories

There are fields particularly akin to the paradigm of Supercomplex Knowledge—territories where the FE–ME–CT triad manifests with operative and symbolic clarity.

4. ITER as a Laboratory of Supercomplex Administration

The ITER project constitutes one of the clearest contemporary examples of technoscience operating under conditions of real supercomplexity. It is not merely a nuclear fusion experiment, but a multiscale system whose stability depends on the simultaneous coupling of extreme energy, material architecture, temporal synchronization, and political-institutional coordination.

ITER seeks to demonstrate the feasibility of controlled nuclear fusion — the process that powers the Sun — as a terrestrial energy source. To do so, it uses a toroidal device (tokamak) in which plasma is confined at temperatures exceeding 150 million degrees by means of highly precise magnetic fields. However, the challenge lies not simply in producing a nuclear reaction, but in sustaining it in a stable, controlled, and energetically profitable manner over time.

5. Conclusions

4D software and immersive simulations translate temporal connectivity into visible experience, turning time into a navigable morphology. Astrobiology, relational cosmology, generative art, and adaptive biotechnology expand this horizon, revealing the profound unity between energy, structure, and temporality.

A relevant artistic example is the work of Refik Anadol, who integrates vast volumes of data with AI to generate installations that make visible the interrelations among data, memory, and space. Likewise, quantum computing illustrates this expansion, where entangled qubits (FE) create morphological superpositions (ME) that challenge linear temporalities (CT). Bioengineering with CRISPR-Cas9 acts as an adaptive morphology, editing genomes and fusing evolutionary scales with human innovation. In all these territories, the more we observe, model, or co-create the universe, the more we understand that complexity does not simply increase: it transforms, revealing new forms of overlap and co-evolution among systems. Where classical science saw fragments, Supercomplex Knowledge (SK) perceives an energetic spiral dance. And that dance—visible, modelable, participatory—announces the emergence of a planetary intelligence capable of thinking in network, in flow, and in spiral.

SK offers the first matrix capable of unifying technoscience and philosophy within a common operational framework. Not as a symbolic reconciliation, but as a dynamic integration: science observes, technology models, and philosophy provides ethical direction and a sense of design. Supercomplexity, more than a field of study, becomes the epistemic environment of the human future: a planetary consciousness where observing, understanding, and creating become a single breathing act. We are not facing a new discipline, but rather a new condition of intelligibility of the technical and planetary world.

Summary

The Supercomplex Knowledge (SK) transforms the understanding of complexity into a transformative practice: a technology of thought and intervention applied to living, social, technological, and symbolic systems. Its core remains the same: to read, model, and redesign the combinations among Energy Flows (EF), Structural Morphologies (SM), and Temporal Connectivities (TC) to increase the systems' coherence and vitality. The guiding question is: what triadic configurations are we not currently seeing that could radically enhance a system?

This chapter shows how this matrix translates into concrete practices: from Supercomplex Medicine and Psychology to process engineering, sustainability and public policy, organizational intervention, and art. Through tools such as Adaptive Dynamic Maps (ADM), the COMPLEX CUORE software, and specific equations (such as the FEB or the Human Descriptor Dₕ), the SK enables anticipatory diagnostics, four-dimensional simulations, and designs for regenerative extraction, spiral education, and evolutionary governance. More than a catalog of examples, these applications function as a living laboratory for a strong thesis: there is no relevant phenomenon that cannot be rewritten as a singular configuration of energy, form, and time.

15.1. Supercomplex Medicine: A New Reading of the Living Body

Conventional medical diagnosis relies on static data: images, biochemical values, or genetic sequences. From the supercomplex perspective, these data are only projections of energetic flows, structural morphologies, and temporal connectivities that interact dynamically.

Applied to the diagnosis and treatment of pancreatic cancer, SK allows the anticipation of patterns of systemic disorganization before the visible manifestation of the tumor. Where traditional artificial intelligence detects correlations, SK models evolutionary coherences:

• Energy Flows (EF) translate into variations in metabolic consumption, irrigation, and thermal distribution.
• Structural Morphologies (SM) capture microarchitectural transformations in pancreatic tissue and its interactions with the environment.
• Temporal Connectivities (TC) integrate biological rhythms, emotional fluctuations, and the cumulative effects of stress.

The result is a dynamic model that enables anticipatory diagnoses and combinatory treatments, where the intervention is directed not only at the tumor but at restoring the global coherence of the patient system. It is not necessary to model "everything" at first, but rather to identify the critical nodes and flows that most influence system coherence. Moreover, technologies such as Edge Computing, IoT, and Big Data are already creating the infrastructure that makes this increasingly viable. SK models this through the Bio-Complexity Equation (EFΒ), where the Axiological Factor (A) is used as a metric to assess the vital efficiency of the system. A successful treatment translates into an increase in the A index, signaling an enhanced capacity for self-organization of the patient system.

15.2. Technology and the Design of Complex Systems

In engineering, SK offers a transversal methodology for designing resilient and adaptive systems. Adaptive Dynamic Maps (ADM) make it possible to simulate multiscale behaviors, detect potential failures, and evaluate alternative configurations before real implementation.

Examples:

• In smart electrical grids, EFs are modeled as physical and symbolic energy flows; SMs represent the distribution architecture, and TCs measure synchronization between demand and supply.
• In robotics and automation, SK introduces the notion of functional coherence: each robot behaves not only as a programmed unit but as a node within a relational system that learns and redistributes its energy according to environmental variations.
• In bioengineering, SK enables a deeper understanding of how energy, form, and time co-evolve in processes of cellular regeneration, opening the door to truly holistic therapies.

15.3. Sustainability, Resilient Public Policies, and Regenerative Extraction

Global problems—climate crisis, inequality, biodiversity loss—cannot be solved with linear models. Supercomplex Knowledge (SK) proposes a multiscale understanding of sustainability: living and social systems can only endure if energy flows (EF), structural morphologies (SM), and temporal connectivities (TC) remain in dynamic coherence.

Adaptive Dynamic Maps (ADM) make it possible to build living cartographies of sustainability, where local actions are evaluated according to their systemic effects. For example, in environmental policy, an ADM can integrate:

• EF: use and redistribution of natural resources, carbon footprint, and energy circuits.
• SM: productive networks, ecological systems, institutional and territorial structures.
• TC: regeneration rhythms, biological cycles, and political temporalities.

The result is not a single indicator but a living cartography capable of identifying points where small interventions produce large transformations. Thus, SK guides the design of regenerative public policies—context-sensitive, culturally grounded, and attuned to ecological reciprocity dynamics. Within this framework, the Human Descriptor (Dₕ) models political decisions, collective values, and social culture as operators that directly affect the EF and SM of systems.

In contrast to the extractive logic that dominates global capitalism—based on taking without returning—SK introduces the concept of regenerative extraction, an evolutionary, relational, and ethical practice. To live is to take, but taking must not imply destroying. In healthy systems, the acquisition of energy or matter respects the other's structural morphology (SM), its regeneration rhythm (TC), and the quality of its flows (EF). Regeneration, from SK's standpoint, is not abstention but lucid and co-evolutionary design: to intervene without collapsing, to produce without degrading, to transform without violating vital rhythms.

As Bruno Latour points out, "we can no longer speak of nature as a passive stage: we are entangled in networks of co-dependence with multiple non-human actors." In this sense, regenerative extraction becomes a model of planetary governance that replaces the logic of infinite growth with an economy of dynamic balance.

Practices that embody this principle—agroecology, regenerative livestock farming, bioclimatic architecture, circular-metabolism cities—are empirical expressions of this supercomplex ethics. Each applies the EF–SM–TC triad: redistributing energy, reconfiguring structures, and respecting regeneration times.

At the political level, it implies moving from an extractivist state to a regenerative state, one that plans based on multiscale diagnoses and ecological temporalities. Economically, it requires designing value systems that integrate reciprocity and cooperation. Symbolically, it demands a new civilizational narrative: inhabiting the Earth as part of its metabolism, not as its parasite.

Supercomplex Knowledge shows that true sustainability is not about consuming less but about relearning how to relate. Regeneration is not a romantic utopia but an advanced form of adaptive intelligence—a synthesis of ethics, ecology, and complexity.

15.4. Supercomplex Education and Learning

In the 21st century, the educational system faces rapid transformations resulting from social, scientific, and technological changes that reshape, every day, the way we learn, communicate, and inhabit knowledge. In this context, the teacher's role is being redefined: no longer as a transmitter of knowledge, but as a mediator of cognitive and emotional flows, a manager of environments, and a designer of learning experiences. Educational innovation does not consist merely in incorporating new techniques, but in continuously reworking objectives, content, and methods, based on a relational understanding of the teaching and learning process.

This transformation requires recognizing the profound interdependence between knowledge, emotion, and embodiment. Human beings are multidimensional systems and, therefore, multi-conversational: we participate in multiple networks that intertwine language, emotion, and action. To inhabit educational institutions always implies an exchange of energy and meaning that leaves traces in both the individual and the community.

If education is a bio-social system of cognitive energy transfer and expansion, it cannot operate on negative emotional flows (fear, anxiety, destructive competition) without distorting the structural morphology of the learning subject. In most current educational systems, emotional energy circulates as pressure, fear of failure, and the pursuit of approval. It is low-vibration, entropic energy that inhibits exploration. SK proposes to replace that dynamic with vitalizing energy flows: enthusiasm, curiosity, wonder, the desire to understand.

The traditional classroom adopts a rigid arboreal morphology—vertical, hierarchical—where the teacher "grants" knowledge. Conversely, supercomplex education assumes a rhizomatic or spiral SM, where all participants co-construct learning, expand, and interrelate. Educational practices based on fragmented evaluation sever the continuity of learning. SK advocates for an extended TC: prolonged processes of exploration and revision, where learning is perceived as a life trajectory rather than a sequence of exams. Learning should be an experience of expansion and cognitive pleasure, not of submission or distress. Each genuine discovery produces a micro-explosion of vital energy: the pleasure of understanding.

Ultimately, formal education remains trapped in a linear logic of transmission and evaluation. SK provides a framework to rebuild it from relationality, interaction, and the self-organization of knowledge.

Supercomplex educational environments:

• Integrate energy flows (motivation, attention, cognitive rhythm).
• Model structural morphologies (learning networks, subject interconnections, institutional architectures, integration with non-formal and informal education).
• Manage temporal connectivities (trajectories, pauses, knowledge reconfigurations).

Simulation platforms based on COMPLEX CUORE would allow the visualization of an institution's learning climate, identification of overload or disconnection zones, and dynamic redesign of its curricular structure. The result would be education that not only transmits information but also learns from itself.

15.5. Process Engineering and Supercomplex Control: The Polymer Plant Case

An interdisciplinary team led by a chemical engineer works in a plant dedicated to the production of biodegradable polymers. Reactor conditions constantly vary due to internal and external factors that classical control systems cannot anticipate.

The team adopts the SK approach and uses COMPLEX CUORE software to generate Adaptive Dynamic Maps (ADM) based on the EF–SM–TC triad:

• EF: identifies physical and human energy flows, detecting thermal and cognitive overloads.
• SM: redesigns the reactor configuration and digital control routes.
• TC: analyzes production rhythms and temporal efficiency thresholds.

The result is a system capable of self-adjusting to raw material fluctuations, reducing waste (–22%), increasing efficiency (+17%), and optimizing process sustainability. More importantly, the team adopts a new cognitive attitude: from control to understanding, from reactive maintenance to continuous systemic learning.

15.6. Supercomplex Intervention in Organizations

Through its CO-ENERG program, SK helps organizations grow, improve, and innovate through supercomplex diagnostics and simulations. It begins with the premise that an organization is a living organism where every area, person, and decision forms part of the same energy flow.

The process unfolds in three main stages:

CO-ENERG does not offer generic recipes; it builds combinatory solutions where hard data and human factors converge into systemic coherence. Thus, a traditional consultancy measures performance; CO-ENERG measures organizational life.

15.7. Supercomplex Psychology: From System to Self

SK redefines psychology as the science of the relational, dynamic, and self-aware subject. The mind is not an entity nor a closed structure; it is a network of interdependent systems—biological, relational, symbolic, and technological—where energy flows (EF), structural morphologies (SM), and temporal connectivities (TC) interact evolutionarily.

Traditional psychological schools, by fragmenting experience into isolated domains—mind, behavior, unconscious, relationship—have lost sight of the subject's dynamic unity. SK transcends that fragmentation, proposing a psychology of interdependent systems, where every human dimension (biological, emotional, symbolic, social, and technological) co-evolves with the others.

From an ontological, epistemological, and anthropological standpoint, SK conceives subjectivity as a supercomplex emergence: a web of subsystems that combine stability and transformation, continuity and rupture. Classical concepts such as the unconscious, personality, or bond are reinterpreted as nonlinear motivational systems encoded in biographical, relational, and semiotic networks.

The psychologist, within this framework, becomes an interpretive and transdisciplinary node. Their role is not to reduce the patient's complexity to a diagnostic category but to read and model system interactions, involving other specialists when the entanglement of medical, social, or symbolic dimensions demands it. Far from losing centrality, the SK psychologist leads therapeutic processes that integrate knowledge and practices across networks.

The therapeutic role is specular, collaborative, and strategic, inspired by the systemic-constructivist tradition (Haley, Elkaïm). The therapist acts as an observer-participant, co-designing intervention algorithms with the patient that simultaneously affect different levels of the system.

The process develops in two stages:

1. Specular stage: the therapist acts as a cognitive mirror, showing the intervening nodes, their flows, and tensions.
2. Dynamic stage: once a meaningful map is configured, progressive perturbations are applied to critical nodes, seeking to restore coherence across subsystems.

Tools such as COMPLEX CUORE and Adaptive Dynamic Maps (ADM) allow visualization of four-dimensional interactions between self-aware, socio-relational, and symbolic systems. These models identify hot nodes, bifurcations, and emerging patterns, offering unprecedented diagnostic granularity. The therapist does not merely "heal" systems but facilitates their vital reconfiguration through situated and evolutionary algorithmic interventions.

Supercomplex Psychology does not replace prior schools; it reintegrates them within a broader topology where mind, behavior, and culture are conceived as interwoven flows in constant reorganization. Mental health is redefined as dynamic coherence among the self's systems, and therapy as lucid accompaniment of that reorganization. The psychologist thus ceases to be a technician of symptoms and becomes a designer of coherence, a craftsman of balance between energy, form, and time in human life.

15.8. Supercomplex Art: Energy, Form, and Time in Motion

From the perspective of Supercomplex Knowledge (SK), art is not representation but reorganization of energy, morphology, and temporality. Each artwork constitutes a living system where creative flows (EF), formal structures (SM), and temporal resonances (TC) interact dynamically.

A painting, a musical piece, or a digital installation are maps of sensible coherence: condensations of energy that seek balance between order and chance, foresight and surprise. Just as the universe self-organizes, art reveals that same impulse on a human scale.

Supercomplex Art does not seek to imitate reality but to activate its patterns. In Calder's moving forms, Medialab's interactive networks, or Brian Eno's generative music, an aesthetics of emergence manifests: beauty arises from interaction, not from control. The artist, therefore, is a mediator of flows, a cartographer of mutable morphologies, and an administrator of sensitive time. Their work does not close; it breathes, learns, and evolves with whoever experiences it.

Art, like science, confirms that all authentic creation is a process of living coherence between energy, form, and time—the same triad that structures the universe and which SK turns into a philosophical, technological, and aesthetic language.

Conclusion

By proposing a relational, dynamic, and multiscale vision of the universe, and by focusing on intervention and modeling of living systems, the SK paradigm offers a conceptual framework and tools that can accelerate and enrich progress across dimensions. Scientific communities, however, often resist paradigm shifts—especially those that challenge entrenched ontological assumptions, such as the notion of a universe of constants or the separation between disciplines. The scientific and technological development of the last fifty years has advanced within positivist and reductionist frameworks that SK seeks to expand.

For instance, in Supercomplex Medicine (13.1), SK does not discard the use of static data (images, biochemical values) but reinterprets them as projections of EF–SM–TC dynamics, enabling anticipatory diagnoses. We have shown concrete results—such as in pancreatic cancer—where SK models evolutionary coherences for more effective interventions. Furthermore, the gradual adoption of SK in interdisciplinary contexts (such as CO-ENERG, 13.6) promotes a smooth transition by integrating human values and hard data.

SK does not aim to offer definitive solutions but rather a framework for generating circular hypotheses and modeling dynamic systems. Practical examples (such as the polymer plant or resilient public policies, 13.3) demonstrate that SK already yields measurable outcomes. Empirical validation can be built iteratively, supported by simulations and data from existing technologies. The key lies in ADM, which allows interventions to be customized according to local dynamics, ensuring that SK remains a situated approach, not a universal recipe.

Developing the ability to "think in supercomplex terms" early in scientific and technological advancement would allow better anticipation and management of complexity challenges, rather than attempting to tackle them retroactively with inadequate tools. It is not only about having technology but the wisdom to design and implement it in harmony with living systems and planetary needs. SK is an integrative, practical, and adaptable framework that amplifies existing tools, produces measurable results, and respects the particularities of each discipline. Its focus on systemic coherence, supported by technologies such as COMPLEX CUORE and ADM, allows effective anticipation and management of complexity, turning initial resistance into an opportunity to rethink the relationship between knowledge, action, and evolution.

Across all fields, SK introduces a new practical intelligibility:

• It detects invisible interactions between energy, form, and time.
• It transforms data into living coherence maps.
• It replaces fragmented control with evolutionary governance.

The result is not only greater effectiveness but greater harmony between knowledge, action, and transformation. SK does not merely describe systems—it accompanies them in their evolution. Ultimately, the promise of SK is that, by providing the mathematical and axiological language necessary to model living coherence, it enables the subject to actively co-design their own evolution, that of the system to which they belong, and that of the systems with which they interact.

The Ultimate Challenge

What if the ultimate challenge of Supercomplex Knowledge (SK) wasn't to accumulate examples, but to demonstrate that there is no phenomenon whatsoever — neither the fine-structure constant, nor the elementary charge of the electron, nor the experience of anxiety, nor the institutional durability of a democracy — that cannot be rewritten as a singular configuration of Energy Flows, Structural Morphologies, and Temporal Connectivities?

The thesis is strong: what was traditionally considered an "intrinsic property" of isolated entities appears, under a triadic perspective, as an effect of interaction, and furthermore, as an inherently scalable phenomenon. This notion of scalability — present in a fragmented way in quantum physics (Heisenberg; Dirac), in systems biology (Lewontin; Kauffman), and in certain contemporary sociological theories (Bourdieu; Latour) — finds a unified ontological principle in the SK: the formal continuity between scales.

The EF–SM–TC triad allows us to conceive of this continuity without residue. The fine structure can be described as the modulation of energy interactions within a quantum morphology of minimal duration; anxiety as a triadic desynchronization between affective energy, perceptual morphology, and temporal rhythms of anticipation; democratic stability as the temporal persistence of an institutional morphology that redistributes social energy in prolonged cycles (Skocpol). In all cases, the supercomplex explanation does not replace current models: it integrates them at a superior ontological level (Prigogine), showing that what seemed specific to each discipline — the quantum constant, the human emotion, the institutional form — can be reinterpreted as local variations of the same triadic matrix.

The stakes, then, are total: either the EF–SM–TC triad manages to translate every observable dynamic without remainder — from the electron to the brain, from the cell to the ecosystem, from the individual to the social network — or the project will have to delimit its boundaries. But until someone presents a compelling counterexample, the burden of proof will no longer rest on the SK. It is now up to established science to demonstrate that there exists, somewhere in the universe, a phenomenon that is not, in the final instance, a scalable triadic interaction.

Summary

Homo sapiens was defined by its capacity to reason, symbolize, and build culture; Homo faber by its technical mastery; Homo economicus by its utilitarian calculus; Homo digitalis by its fusion with platforms. None of these figures, on their own, lucidly inhabit the growing complexity of the systems we create and that constitute us.

The Supercomplex Knowledge (SK) framework proposes the emergence—or rather, the recognition—of a new kind of subject: Homo Supercomplexus. A human who thinks in networks, acts in systems, evaluates multi-scalar impacts, and cares for what they transform. This is not a utopia; it is a mutation already visible in the cognitive, social, technological, and emotional practices of our era.

16.1. Who is Homo Supercomplexus?

Homo Supercomplexus conceives of itself as a node among biological, symbolic, technological, emotional, and environmental systems; it knows through dynamic maps (not compartments); decides according to criteria that balance well-being, sustainability, diversity, and adaptability; intervenes knowing that every action reconfigures other systems; conceives freedom as relational autonomy and shared design.

It is not defined by cultural identity, class, or ideology, but by its level of systemic integration, active reflexivity, and ethical–aesthetic attitude toward complexity. The proposal seeks to shift the focus from the isolated individual to an agency situated within networks, with an ethics that values cooperation, diversity, and sustainability.

16.2. Fundamental Traits

1. Cognitive multiscalarity. Thinks simultaneously from the micro, macro, and symbolic dimensions; maps interactions and navigates across scales with flexibility.
2. Supervisory ethics. Evaluates interventions by their energetic, structural, and temporal impact: "Does this improve the system I belong to?"
3. Self- and common-design. Understands that reorganizing habits transforms the environment, and that intervening in the collective transforms one's own trajectory.
4. Aesthetics of care. Seeks emergent harmony among nodes—patterns that favor networked life and sensitive innovation.
5. Combinatorial anticipation. Does not predict linearly: imagines futures, prototypes, simulates scenarios, and adjusts decisions adaptively.

This figure does not replace Homo sapiens; it expands it. It is not "more rational" or "stronger": it is more interconnected, flexible, lucid, and involved.

16.3. SK Basis: Energy, Form, and Time

From the SK perspective, the human being is defined by its plasticity to strategically recombine Energy Flows (EF), Structural Morphologies (SM), and Temporal Connectivities (TC). We are emotive-thinkers and thinking-emotive beings, symbolic–technical, relational–cultural, and bio–temporal. Every configuration of consciousness is a dynamic assemblage responding to situated needs. To think is to feel; to intervene is to listen; to exist is to redesign oneself.

16.4. Principles in Motion

• Relational autonomy. Freedom is not isolation: it is co-design with systems of contact.
• Radical relationality. The subjective, biological, cultural, and technological co-implicate experience.
• Incompleteness. The universe is autonomous and stochastic; reason dialogues with intuition, multiscalar observation, simulation, and modeling.
• Pluralism. Rejects dogmas and extremisms; defends free thought and symbolic creation.
• Ecosystems. Economy, politics, and society are complex ecosystems that require energetic balance, empowering connections, and sustainability.
• Combinatorial innovation. Those who best combine energy, connections, and temporalities survive and flourish.
• Implicated technology. Instruments, algorithms, and AI co-create the real; they are not neutral.
• Fertile chance. Fluctuation is not a defect; it is a motor of emergence and innovation.
• Multiscalarity. Quantum, biological, and macroscopic realms intertwine; ignoring this leads to blindness.
• Aesthetic ethics of the cosmonaut. Traveling the spaceship Earth without ultimate certainties or predetermined destinations—with responsibility, enjoyment, and openness.

16.5. Freedom and Care in the Supercomplex Key

Homo Supercomplexus assumes that there is no external teleology or supersystem of control. It renounces imposed purposes and takes on the responsibility of constructing maps of meaning within the web of interactions. Its ethics is not dogmatic: it arises from understanding that reality is a fabric of multiscalar dynamics in which it is an agent.

This is neither relativism nor recklessness: love and friendship sustain networks of learning and mutual development. Happiness is not a fixed point but an emergent phenomenon of identity, connection, and systems—the art of timing and mapping: knowing when to advance or pause, to say yes or no, to manage energy intelligently in balance with others.

16.6. Authenticity, Play, and Finitude

Authenticity (Kierkegaard), playfulness as an attitude, and serene acceptance of finitude compose a creative freedom. Finitude does not hinder expansion; it makes it meaningful—like energetic interactions that extinguish while leaving morphological traces.

Homo Supercomplexus embodies a late optimism: celebrating life without denying limits, turning them into motives for creativity and care.

16.7. Relational Ethics and "Win–Win" Governance

Personal growth is not achieved at the expense of others. In a supercomplex universe, balance is sustained through shared benefits. Homo Supercomplexus avoids imposing solutions: it uses specularity so that systems can see themselves and self-transform. Its practice is democratic: diversity and open participation enrich decisions; distributed power favors adaptive strategies.

16.8. Three Pedagogical Metaphors

• Homo Supercomplexus: cognitive–ethical–philosophical dimension—understanding, mapping, and surfing complexity responsibly.
• Cosmic astronaut: exploratory and relational spirit—traveling through cosmos, science, and experience to construct meaning.
• Playful primate: biological and creative root—play, curiosity, and imagination as engines of learning.

An education inspired by these metaphors fosters relational thinking, curiosity, and the joy of learning through interdisciplinary activities, simulations, and creative design.

16.9. The Supercomplex Matrix (Personal Tool)

The SK encourages constructing a matrix—a way of organizing questions and decisions—based on EF–SM–TC:

Those who think and act through this matrix rewrite their history and elevate their perception of their capacities, integrating research, play, and love as vital practices.

16.10. From the Precursors to the Present

Democritus, Hypatia, Da Vinci, Copernicus, Galileo, Mendeleev, Darwin, Curie, Tesla, Einstein, and many others shared three traits: lucid divergence, creative methodology, and authentic attitude. They were architects of matrices. Homo Supercomplexus recognizes them as symbolic ancestors: to think freely, to relate creatively, and to act with ethical aesthetics is not eccentricity—it is the fullest form of inhabiting intelligence.

16.11. Conclusions

Homo Supercomplexus does not seek to control the world nor to submit to it: it coexists with its complexity, understands its autonomy, and participates in its evolution. With authenticity, play, and acceptance of finitude, it turns limits into creativity and care. It does not emerge from nothing; its capacity to surf complexity is nourished by those who dared to think differently. Today, that boldness takes form in practices, metrics, and maps that turn understanding into action and action into meaning. It is not a utopia—it is a compass. It is not expected that everyone will become Homo Supercomplexus overnight, but that we cultivate these traits as a society. Figures such as engineers of resilient systems, doctors with holistic approaches, or educators who foster critical thinking already embody aspects of this ideal. It is an evolutionary direction, not a final state.

The SK provides the language and instruments for this subject to co-design their own evolution, that of the system they inhabit, and that of the systems with which they interact. Thinking, modeling, and intervening cease to be separate acts: they become a single dance between order and disorder, between uncertainty and discovery.

On the planetary horizon, Homo Supercomplexus is not a new biological species but a stage of cognitive maturation of Homo sapiens. A humanity learning to observe without devouring, to transform without dominating, to think without fragmenting. If the Enlightenment was the age of reason, the supercomplex era will be that of relational lucidity—where knowledge, pleasure, work, and care converge in a single form of planetary intelligence.