Consciousness has been the bone of contention for philosophers throughout centuries. Indian philosophy largely adopted lived experience as the starting point for its explorations of consciousness. For this reason, from the very beginning, experience was an integral way of grasping consciousness, whose validity as a tool was considered self-evident. Thus, in Indian philosophy, the question was not to move from the brain to mind but to understand experience of an individual and how such an experience is determined through mental structures (...) (and secondarily, the preoccupation with the brain and its relation to the mind). In contrast, cognitive science (the study of mind and cognition through 1 interdisciplinary methods, with emphasis on computational methods) found its debates soaked in discussion which primarily involved the brain and mind. Experience was not considered a primary source of information and its validity had to be established to consider it a source of information of mind. With the rise of physicalism and realization that mental states are correlative to brain states, the body was virtually neglected from involvement in understanding the mind and the attempts to reduce mind to the brain were rampant. The inability to explain subjective experience of an individual through neuroscientific findings alone has urged philosophers to explore other ways of understanding the ontology of mind. Over the last few years, embodied cognition and enactive approach have brought back the body as a central participant in this debate, providing fertile grounds to explain the relation of brain, body and mind. This paper proposes that we understand the brain as a complex system from which the mind emerges. This emergence is marked by the development of novel property of self-consciousness in human beings. The mind is a process which is embedded throughout the body and thus, the body acts as an actualizing medium for the individual. Thus, the brain is a necessary condition for the mind to be while the mind is embedded throughout the body. The brain and mind are in reciprocal causal relationship with one another, as is the body and environment with one another. In this paper, embodied cognition is understood through principles of Merleau Ponty's idea of embodiment, than through Andy Clark and Francis Varela's alone. (shrink)

The term “Complex Systems Biology” was introduced a few years ago [Kaneko, 2006] and, although not yet of widespread use, it seems particularly well suited to indicate an approach to biology which is well rooted in complex systems science. Although broad generalizations are always dangerous, it is safe to state that mainstream biology has been largely dominated by a gene-centric view in the last decades, due to the success of molecular biology. So the one gene - (...) one trait approch, which has often proved to be effective, has been extended to cover even complex traits. This simplifying view has been appropriately criticized, and the movement called systems biology has taken off. Systems biology [Noble, 2006] emphasizes the presence of several feedback loops in biological systems, which severely limit the range of validity of explanations based upon linear causal chains (e.g. gene → behaviour). Mathematical modelling is one the favourite tools of systems biologists to analyze the possible effects of competing negative and positive feedback loops which can be observed at several levels (from molecules to organelles, cells, tissues, organs, organisms, ecosystems). Systems biology is by now a well-established field, as it can be inferred by the rapid growth in number of conferences and journals devoted to it, as well as by the existence of several grants and funded projects.Systems biology is mainly focused upon the description of specific biological items, like for example specific organisms, or specific organs in a class of animals, or specific genetic-metabolic circuits. It therefore leaves open the issue of the search for general principles of biological organization, which apply to all living beings or to at least to broad classes. We know indeed that there are some principles of this kind, biological evolution being the most famous one. The theory of cellular organization also qualifies as a general principle. But the main focus of biological research has been that of studying specific cases, with some reluctance to accept (and perhaps a limited interest for) broad generalizations. This may however change, and this is indeed the challenge of complex systems biology: looking for general principles in biological systems, in the spirit of complex systems science which searches for similar features and behaviours in various kinds of systems. The hope to find such general principles appears well founded, and I will show in Section 2 that there are indeed data which provide support to this claim. Besides data, there are also general ideas and models concerning the way in which biological systems work. The strategy, in this case, is that of introducing simplified models of biological organisms or processes, and to look for their generic properties: this term, borrowed from statistical physics, is used for those properties which are shared by a wide class of systems. In order to model these properties, the most effective approach has been so far that of using ensembles of systems, where each member can be different from another one, and to look for those properties which are widespread. This approach was introduced many years ago [Kauffman, 1971] in modelling gene regulatory networks. At that time one had very few information about the way in which the expression of a given gene affects that of other genes, apart from the fact that this influence is real and can be studied in few selected cases (like e.g. the lactose metabolism in E. coli). Today, after many years of triumphs of molecular biology, much more has been discovered, however the possibility of describing a complete map of gene-gene interactions in a moderately complex organism is still out of reach. Therefore the goal of fully describing a network of interacting genes in a real organism could not be (and still cannot be) achieved. But a different approach has proven very fruitful, that of asking what are the typical properties of such a set of interacting genes. Making some plausible hypotheses and introducing some simplifying assumptions, Kauffman was able to address some important problems. In particular, he drew attention to the fact that a dynamical system of interacting genes displays selforganizing properties which explain some key aspects of life, most notably the existence of a limited number of cellular types in every multicellular organism (these numbers are of the order of a few hundreds, while the number of theoretically possible types, absent interactions, would be much much larger than the number of protons in the universe). In section 3 I will describe the ensemble based approach in the context of gene regulatory networks, and I will show that it can describe some important experimental data. Finally, in section 4 I will discuss some methodological aspects. (shrink)

Abstract The study of complex systems is central to understanding natural phenomena that exhibit apparent randomness and disorder. Giorgio Parisi’s Nobel-winning contributions, particularly in spin glasses and replica symmetry breaking, provide profound insights into how disordered systems maintain hidden order and statistical predictability. This paper examines Parisi’s theoretical contributions and explores their conceptual alignment with Angelito Malicse’s 4 universal laws: the law of karma/system integrity, the universal law of balance in nature, the universal feedback loop mechanism, and (...) the universal interconnected nodes. Through detailed examples from physics, neuroscience, and economics, this paper demonstrates that Parisi’s work can be interpreted as a scientific manifestation of these universal laws. -/- . (shrink)

This thesis presents the first comprehensive empirical investigation of ontological instability in complex systems, introducing a novel theoretical framework called Quantitative Ontological Dynamics (QOD) that bridges philosophical ontology with empirical measurement. Through systematic analysis of quantum mechanical systems, biological phase transitions, economic market dynamics, and other complex phenomena, we demonstrate that ontological categories are not fixed but exhibit measurable fluctuations that can be quantified, predicted, and analyzed using rigorous mathematical methods. -/- Our research reveals that traditional (...) fixed ontological positions fail to account for the dynamic nature of reality as observed in complex systems. We introduce five fundamental metrics for measuring ontological instability: fluctuation intensity, correlation dynamics, phase transition indicators, ontological uncertainty, and giant component dynamics. These metrics, combined into a composite Ontological Instability Index (OII), provide unprecedented insight into how the fundamental nature of systems changes over time. -/- The empirical evidence presented demonstrates that ontological instability is not merely a philosophical curiosity but a measurable phenomenon with practical implications for understanding and predicting system behavior. Our analysis of financial market crises, biological phase transitions, and quantum measurement processes reveals consistent patterns of ontological fluctuation that challenge conventional approaches to system analysis and prediction. -/- This work establishes ontological instability as a legitimate subject of scientific inquiry and provides the theoretical and methodological foundation for a new field of quantitative ontology. The implications extend beyond academic philosophy to practical applications in risk assessment, system design, and crisis prediction across multiple domains. (shrink)

Crossing the road within the traffic system is an example of an action human agents perform successfully day-to-day in complex systems. How do they perform such successful actions given that the behaviour of complex systems is often difficult to predict? The contemporary literature contains two contrasting approaches to the epistemology of complex systems: an analytic and a post-modern approach. We argue that neither approach adequately accounts for how successful action is possible in complex (...) systems. Agents regularly perform successful actions without obeying (explicit or implicit) algorithmic rules (as the analytic approach suggests) and without an existential leap to action (as the post-modern approach suggests). We offer an alternative: A common-sense pragmatist epistemology, one that focuses on the kind of actions making up most agents’ successful moment-to-moment actions in complex systems. Successful actions obtain when agents apply ceteris paribus rules-of-thumb during predictive and decisional practices while achieving some desired goal. (shrink)

This paper explores some of the factors that make complex systems complex. We first examine the history of complex systems. It was Aristotle’s insight that how elements are joined together helps determine the properties of the resulting whole. We find (a) that scientific reductionism does not provide a sufficient explanation; (b) that to understand complex systems, one must identify and trace energy flows; and (c) that disproportionate causality, including global tipping points, are all (...) around us. Disproportionate causality results from the wide availability of energy stores. We discuss three categories of emergent phenomena—static, dynamic, and adaptive—and recommend retiring the term emergent, except perhaps as a synonym for creative. Finally, we find that virtually all communication is stigmergic. (shrink)

This paper introduces the Architecture of Complex Systems (ACS) as an ontological framework for understanding systems as coherent architectures rather than as collections of behaviors or dynamic processes. ACS addresses a foundational question: under what conditions does a system exist as a unified entity at all? The framework is grounded in the primacy of relations over observation and dynamics. Architectural truth is defined as relational and is shown to depend on coherence, closure, and invariants within a bounded (...) relational structure. On this basis, the paper establishes a fundamental boundary: no amount of observational data or knowledge of system dynamics suffices to reconstruct system ontology. Dynamics may describe state transitions and trajectories, but they do not uniquely determine architectural structure. A minimal relational algebra is introduced as a working language for architectural reasoning. This algebra is used to articulate admissibility, coherence, and architectural existence without reference to domain-specific implementations or empirical realizations. Architecture is treated as ontologically prior to both dynamics and observation. The contribution of this work is intentionally foundational. It fixes the ontological core of ACS and situates it within a broader research program concerned with discrete existence and the limits of observation. Domain-specific interpretations and applications are left as subsequent extensions built upon this explicitly defined foundation. (shrink)

This study constructs a generative model of subjective experience based on neuroscience and complex systems research. It explores the origins of the sense of authenticity in both subjective and dream experiences, while embedding two core dilemmas of consciousness philosophy—the hard problem and the first-person perspective problem—within the evolutionary mechanisms of complex systems. Grounded in structural coherence, and drawing from systems science frameworks, we develop systems-based explanatory pathways for these issues. This research deepens understanding of (...) classical problems in consciousness philosophy and offers novel perspectives for consciousness studies through a complex systems lens. (shrink)

In the last 20 years, a stream of research emerged under the label of „complex problem solving“ (CPS). This research was intended to describe the way people deal with complex, dynamic, and intransparent situations. Complex computer-simulated scenarios were as stimulus material in psychological experiments. This line of research lead to subtle insights into the way how people deal with complexity and uncertainty. Besides these knowledge-rich, realistic, intransparent, complex, dynamic scenarios with many variables, a second line of (...) research used more simple, knowledge-lean scenarios with a low number of variables („minimal complex systems“, MCS) that have been proposed recently in problem-solving research for the purpose of educational assessment. In both cases, the idea behind the use of microworlds is to increase validity of problem solving tasks by presenting interactive environments that can be explored and controlled by participants while pursuing certain action goals. The main argument presented here is: both types of systems - CPS and MCS – can only be dealt with successfully if causal dependencies between input and output variables are identified and used for system control. System knowledge is necessary for control and intervention. But CPS and MCS differ in their way of how causal dependencies are identified and how the mental model is constructed; therefore, they cannot be compared directly to each other with respect to the cognitive processes that are necessary for solving the tasks. Knowledge-poor MCS tasks address only a small fraction of the cognitive processes and structures needed for knowledge-rich CPS situations. (shrink)

This paper investigates the mechanism of downward causation in emergent systems, proposing the concept of an effective potential as its mathematical formalization from the perspective of lower-level dynamics. In connection with nonlinear dynamical equations, we demonstrate that feedback mechanisms in complex systems are often erroneously reduced to phenomenological models. This reveals a methodological pitfall in physical research: phenomenological models grounded in reductionism may fail to uniquely map onto the intrinsic dynamics of the system. Consequently, we advocate establishing (...) a new paradigm that transcends reductionism and seeks a more fundamental understanding of complex systems. Using cosmological-scale dark matter phenomena as an example, we highlight the interdisciplinary significance of this approach. (shrink)

This paper critically examines the argument for discreteness as a fundamental property of reality, a perspective advanced in fields such as quantum mechanics, computational models, and digital philosophy. Proponents of this view assert that the universe operates as a system of finite, quantized units, reducible to discrete particles, bits, or mathematical constructs. While compelling in specific contexts, discreteness is not an intrinsic feature of reality but an emergent phenomenon arising under particular conditions and shaped by the limitations of observational tools (...) and mathematical abstractions. Using the philosophical frameworks of Dynamic Materialism and Adaptive Realism, and the scientific lens of Neodynamics, this paper explores the limitations of the discreteness argument. I critique its dependence on idealized models, its misinterpretation of quantum and relativistic theories, and its tendency to overlook the emergent nature of discrete phenomena within continuous systems, highlighting how tools and methods of observation, constrained by resolution and scale, often impose artificial boundaries that create the illusion of fundamental discreteness. I present continuity as a more foundational framework, emphasizing its role in supporting emergent coherence, nested scales of interaction, and feedback-driven adaptation. By integrating stochastic and continuous dynamics into our understanding of complex systems, I advocate for a shift away from reductionist models toward a relational and adaptive approach that better reflects the interconnected nature of reality. This paper aims to reframe the discreteness vs. continuity debate, offering a nuanced synthesis that bridges philosophical and scientific perspectives while pointing to future directions for exploration and integration. (shrink)

In the philosophy of science, increasing attention has been given to the methodological novelties associated with the study of complex systems. However, there is little agreement on exactly what complex systems are. Although many characterizations of complex systems are available, they tend to be either impressionistic or overly formal. Formal definitions rely primarily on ideas from the study of computational complexity, but the relation between these formal ideas and the messy world of empirical phenomena (...) is unclear. Here, I give a definition of complex systems that draws on algorithmic complexity theory, but also provides a way of interpreting the formal idea in an empirical setting. I then use the definition to show that two canonical forms of scientific idealization are empirically inadequate when applied to complex systems. The inadequacy of these forms of idealization is shown to be the primary reason that complex systems require novel methods. Moreover, this demand for novel methods helps explain the rise of complex systems science as an autonomous discipline. (shrink)

This paper discusses the epistemic status of biology from the standpoint of the systemic approach to living systems based on the notion of biological autonomy. This approach aims to provide an understanding of the distinctive character of biological systems and this paper analyses its theoretical and epistemological dimensions. The paper argues that, considered from this perspective, biological systems are examples of emergent phenomena, that the biological domain exhibits special features with respect to other domains, and that biology (...) as a discipline employs some core concepts, such as teleology, function, regulation among others, that are irreducible to those employed in physics and chemistry. It addresses the claim made by Jacques Monod that biology as a science is marginal. It argues that biology is general insofar as it constitutes a paradigmatic example of complexity science, both in terms of how it defines the theoretical object of study and of the epistemology and heuristics employed. As such, biology may provide lessons that can be applied more widely to develop an epistemology of complex systems. (shrink)

Let us start by some general definitions of the concept of complexity. We take a complex system to be one composed by a large number of parts, and whose properties are not fully explained by an understanding of its components parts. Studies of complex systems recognized the importance of “wholeness”, defined as problems of organization (and of regulation), phenomena non resolvable into local events, dynamics interactions in the difference of behaviour of parts when isolated or in higher (...) configuration, etc., in short, systems of various orders (or levels) not understandable by investigation of their respective parts in isolation. In a complex system it is essential to distinguish between ‘global’ and ‘local’ properties. Theoretical physicists in the last two decades have discovered that the collective behaviour of a macro-system, i.e. a system composed of many objects, does not change qualitatively when the behaviour of single components are modified slightly. Conversely, it has been also found that the behaviour of single components does change when the overall behaviour of the system is modified. There are many universal classes which describe the collective behaviour of the system, and each class has its own characteristics; the universal classes do not change when we perturb the system. The most interesting and rewarding work consists in finding these universal classes and in spelling out their properties. This conception has been followed in studies done in the last twenty years on second order phase transitions. The objective, which has been mostly achieved, was to classify all possible types of phase transitions in different universality classes and to compute the parameters that control the behaviour of the system near the transition (or critical or bifurcation) point as a function of the universality class. This point of view is not very different from the one expressed by Thom in the introduction of Structural Stability and Morphogenesis (1975). It differs from Thom’s program because there is no a priori idea of the mathematical framework which should be used. Indeed Thom considers only a restricted class of models (ordinary differential equations in low dimensional spaces) while we do not have any prejudice regarding which models should be accepted. One of the most interesting and surprising results obtained by studying complex systems is the possibility of classifying the configurations of the system taxonomically. It is well-known that a well founded taxonomy is possible only if the objects we want to classify have some unique properties, i.e. species may be introduced in an objective way only if it is impossible to go continuously from one specie to another; in a more mathematical language, we say that objects must have the property of ultrametricity. More precisely, it was discovered that there are conditions under which a class of complex systems may only exist in configurations that have the ultrametricity property and consequently they can be classified in a hierarchical way. Indeed, it has been found that only this ultrametricity property is shared by the near-optimal solutions of many optimization problems of complex functions, i.e. corrugated landscapes in Kauffman’s language. These results are derived from the study of spin glass model, but they have wider implications. It is possible that the kind of structures that arise in these cases is present in many other apparently unrelated problems. Before to go on with our considerations, we have to pick in mind two main complementary ideas about complexity. (i) According to the prevalent and usual point of view, the essence of complex systems lies in the emergence of complex structures from the non-linear interaction of many simple elements that obey simple rules. Typically, these rules consist of 0–1 alternatives selected in response to the input received, as in many prototypes like cellular automata, Boolean networks, spin systems, etc. Quite intricate patterns and structures can occur in such systems. However, what can be also said is that these are toy systems, and the systems occurring in reality rather consist of elements that individually are quite complex themselves. (ii) So, this bring a new aspect that seems essential and indispensable to the emergence and functioning of complex systems, namely the coordination of individual agents or elements that themselves are complex at their own scale of operation. This coordination dramatically reduces the degree of freedom of those participating agents. Even the constituents of molecules, i.e. the atoms, are rather complicated conglomerations of subatomic particles, perhaps ultimately excitations of patterns of superstrings. Genes, the elementary biochemical coding units, are very complex macromolecular strings, as are the metabolic units, the proteins. Neurons, the basic elements of cognitive networks, themselves are cells. In those mentioned and in other complex systems, it is an important feature that the potential complexity of the behaviour of the individual agents gets dramatically simplified through the global interactions within the system. The individual degrees of freedom are drastically reduced, or, in a more formal terminology, the factual space of the system is much smaller than the product of the state space of the individual elements. That is one key aspect. The other one is that on this basis, that is utilizing the coordination between the activities of its members, the system then becomes able to develop and express a coherent structure at a higher level, that is, an emergent behaviour (and emergent properties) that transcends what each element is individually capable of. (shrink)

For some post-structuralist complexity theorists, there are no epistemic meta-perspectives from where to judge between different epistemic perspectives toward complex systems. In this paper, I argue that these theorists face a dilemma because they argue against meta-perspectives from just such a meta-perspective. In fact, when we understand two or more different perspectives, we seem to unavoidably adopt a meta-perspective to analyse, compare, and judge between those perspectives. I further argue that meta-perspectives can be evaluated and judged from meta-meta-perspectives, (...) and so on. This suggests an epistemic hierarchy. Perspectives, meta-perspectives, meta-meta-perspectives, etc. can be ranked according to the degree to which they confer understanding. I also explore what scope my thesis might have outside the philosophy of complexity by applying it to the sociology of science. (shrink)

Large sets of elements interacting locally and producing specific architectures reliably form a category that transcends the usual dividing line between biological and engineered systems. We propose to call them morphogenetically architected complex systems (MACS). While taking the emergence of properties seriously, the notion of MACS enables at the same time the design (or “meta-design”) of operational means that allow controlling and even, paradoxically, programming this emergence. To demonstrate our claim, we first show that among all the (...) self-organized systems studied in the field of Artificial Life, the specificity of MACS essentially lies in the close relation between their emergent properties and functional properties. Second, we argue that to be a MACS a system does not need to display more than weak emergent properties. Third, since the notion of weak emergence is based on the possibility of simulation, whether computational or mechanistic via machines, we see MACS as good candidates to help design artificial self-architected systems (such as robotic swarms) but also harness and redesign living ones (such as synthetic bacterial films). (shrink)

Museums lose their conceptual complexity and polysemy under conditions of war, forced confrontation, and struggles for survival, which may lead to a loss of diversity in the long run. Parametric General Systems analysis allows us to consider a museum as a system and to explore substratum, structural, and conceptual types of simplicity and complexity. Such qualitative analysis makes it possible to move the discussion from the ideological and value sphere to the field of rational and science-based justification. This justification, (...) in turn, illustrates why it is important to maintain complex interpretations in Ukrainian museums during the current war. (shrink)

Reinforcement Learning (RL) has emerged as a powerful technique for optimizing decision-making in dynamic, uncertain, and complex environments. The ability of RL algorithms to adapt and learn from interactions with the environment enables them to solve challenging problems in fields such as robotics, autonomous systems, finance, and healthcare. In dynamic environments, where conditions change in real-time, RL must continually update its policy to maximize cumulative rewards. This paper explores the application of RL in dynamic environments, with a focus (...) on its ability to optimize real-time decision-making for complex systems. We discuss the challenges associated with these environments, such as non-stationarity, partial observability, and the trade-off between exploration and exploitation. Furthermore, we review recent advancements in RL techniques, including deep reinforcement learning (DRL), multi-agent RL, and model-based RL, and how these methods are addressing the complexity of real-time decision-making. Finally, we present a roadmap for future research, highlighting open questions and potential applications of RL in various industries. (shrink)

This work presents an innovative mathematical model to quantify "implicit purpose" in complex systems. The proposed formula allows for the detection of emergent trends not explicitly programmed, providing a descriptive and adaptable tool for analyzing dynamic systems. Validated through simulations in three distinct domains (energy management, urban traffic, and hospital optimization), the formula demonstrates robustness and effectiveness in identifying coherent behaviors in complex systems. This approach opens new possibilities for retrospective analysis of complex (...) class='Hi'>systems and their practical application across various fields. (shrink)

Truly random numbers are typically regarded as mathematical abstractions, but they can be interpreted through cognitive, systemic, and complex phenomena. This paper explores three frameworks for generating or conceptualizing truly random numbers: (1) cognitive uncertainty via fractalian quines with working memory overload, (2) Byzantine fault-tolerant systems, and (3) high-frequency black-swan dynamics in complex systems. Each framework is accompanied by illustrative examples to clarify mechanisms that produce effective randomness.

This paper proposes a formal framework for moving the discussion of free will from the metaphysical plane to scientific verifiability. We introduce the concept of free will as an emergent property manifested in complex "agent-environment" systems when they reach a critical region in three-dimensional parameter space. This space is defined by axes of: behavioral determinism ($D$), environmental entropy ($H$), and agent informational complexity ($I_A$). We postulate that a free will act ($F_{sv}$) corresponds to a behavioral pattern satisfying the (...) irreducibility criterion—it cannot be adequately modeled as either a deterministic or purely stochastic process, while demonstrating consistency with the agent's internal value system. We propose specific mathematical estimates for model parameters, formulate testable hypotheses, and outline experimental protocols for their verification in cognitive science, neuroscience, and artificial intelligence research. (shrink)

Today a change is imperative in approaching global problems: what is needed is not arm-twisting and power politics, but searching for ways of co-evolution in the complex social and geopolitical systems of the world. The modern theory of self-organization of complex systems provides us with an understanding of the possible forms of coexistence of heterogeneous social and geopolitical structures at different stages of development regarding the different paths of their sustainable co-evolutionary development. The theory argues that (...) the evolutionary channel to the observed increasing complexity is extremely narrow and only certain discrete spectra of relatively stable self-maintained structures are feasible in complex systems. There exists a restricted set of ways of assembling a complex evolutionary whole from diverse parts. The law of nonlinear synthesis of complex structures reads: the integration of structures in more complex ones occurs due to the establishment of a common tempo of their evolution. On the basis of the theory, we can see not only desirable but also attainable futures. (shrink)

Across complex adaptive systems; biological, economic, technological, and ecological; a recurring constraint emerges in the rate at which systems can update without losing coherence. This paper introduces the Curvature Constraint, a derived principle identifying a bounded rate of recursive systemic adaptation typically centered around a 2% annualized curvature band. Rather than an arbitrary metric, this range arises from the structural interplay between entropy leakage, feedback response, memory retention, and symbolic assimilation. Using the Spectrum of Possibility and Recursive (...) Choice (SPARC) framework, I formalize curvature as the rate of directional change in a system’s coherence over time. I demonstrate that exceeding this band destabilizes feedback loops, while slower rates result in stagnation or loss of adaptivity. Drawing on empirical observations from growth economics, cognitive science, urban infrastructure, and evolutionary biology, I show that this attractor is not a policy convenience but a structural necessity. The Curvature Constraint serves as both a descriptive insight and a prescriptive boundary for forecasting, governance, and technological development, providing a minimal universal constraint on sustainable systemic transformation. (shrink)

This paper introduces the Principle of Predictable Intervention (PPI), an original, interdisciplinary universal constraint proposed by the author, governing intelligent action within complex systems. Unlike traditional epistemological or reductionist approaches, PPI does not seek to define systems by their intrinsic essence, but by the boundary conditions under which interventions produce reliably predictable outcomes. The central claim is straightforward: no system can sustain interventions at layers where outcomes become unpredictable relative to inputs. This principle provides a unified framework (...) for analyzing decisions across diverse fields including artificial intelligence, medicine, governance, and philosophy of mind. The implications are profound: genuine creativity, autonomy, and free consciousness, as commonly understood, may not be physically instantiable without violating PPI. (shrink)

This paper critiques the ontological overreach of three core mathematical constructs—probability, calculus, and provability—arguing that they misrepresent the structure of complex systems when treated as foundational truths rather than as epistemic scaffolds. In fields such as artificial intelligence, climate modeling, and molecular biology, these tools have been elevated beyond their descriptive function, warping system behavior through abstraction-first assumptions. Probability introduces fictive randomness, calculus imposes continuity where none exists, and provability reinforces closure within inherently open systems. Through the (...) CODES (Chirality of Dynamic Emergent Systems) framework, we demonstrate how each of these constructs encodes hidden assumptions about time, motion, and truth that collapse under resonance-based modeling. We propose a coherence-driven alternative based on phase alignment scoring, recursive resonance fields, and structured emergence—one that treats mathematical form not as ontology, but as a harmonic compression of deeper physical and cognitive dynamics. (shrink)

Modern institutions increasingly exhibit epistemic drift: delayed recognition of emerging risks, divergence between official representations and ground‑level conditions, and widening testimonial fractures across social strata. Existing frameworks—polarization, misinformation, administrative failure, complexity governance—identify important symptoms but do not specify the upstream structural conditions under which such failures become synchronized and self‑reinforcing. This paper develops Vertical Distance Theory (VDT), a structural account of how epistemic drift emerges when hierarchical separation jointly degrades reciprocal visibility, feedback fidelity, and consequence coupling. When these V–F–C impairments (...) co‑occur, systems lose contact with the lived realities they are meant to register, producing stratified epistemic pathologies: abstraction and risk‑blindness among insulated elites, stability‑seeking proceduralism in middle strata, and high‑resolution experiential knowledge without institutional traction downstream. VDT identifies empirical signatures of vertical distance—including exposure gradients, institutional responsiveness lag, representational divergence, and cross‑stratum testimonial fracture—and argues that the mechanism generalizes to organizational, computational, and hybrid systems. A brief link to Truth Stabilization Theory (TST) clarifies why reducing vertical distance is necessary but insufficient for sustained truth‑tracking, highlighting the epistemic capacities required for drift resistance. (shrink)

Human experience is commonly treated either as a byproduct of biological processes or as a separate metaphysical phenomenon. Both approaches encounter persistent explanatory limitations when addressing recurrent mismatches between subjective experience, structured reports, and physical measurement, particularly in domains studied across consciousness research, cognitive science, and measurement theory, where the observing system itself is constrained and direct instrumental access is structurally limited or epistemically unavailable. -/- This paper introduces the Interface Threshold Hypothesis, a systems-level extension of Emergent Necessity Theory (...) (ENT), proposing that human experience functions as a constrained informational interface to an underlying reality whose full structure is not directly accessible through biological perception alone. Building on ENTs formal framework of coherence thresholds, resilience ratios, and hysteresis dynamics, the hypothesis treats experience not as an originator of reality nor as an epiphenomenon, but as a bandwidth limited readout subject to thresholded failure modes. The hypothesis introduces a formally defined interface operator, an interface stress functional, and operational criteria for identifying structured non-random similarity across reports such as independently generated experiential accounts studied in consciousness research, symbolic representations analyzed in Cognitive science, and observer mediated records examined in systems and measurement theory without relying on anecdotal validation or ontological inflation. -/- This paper specifies explicit scope and falsification conditions, and makes no ontological claims beyond those supported by the formal framework. (shrink)

In this article, I explore the consequences of two commonsensical premises in semantics and epistemology: (1) natural language is a complex system rooted in the communal life of human beings within a given environment; and (2) linguistic knowledge is essentially dependent on natural language. These premises lead me to emphasize the process-socio-environmental character of linguistic meaning and knowledge, from which I proceed to analyse a number of long-standing philosophical problems, attempting to throw new light upon them on these grounds. (...) In particular, I criticize the use of expressions such as ‘absolute truth’, ‘absolute existence’ and ‘the thing in itself’, arguing that they lead to what I call ‘the ultralinguistic paradox’ (a fatal antinomy). In the same way, I review a number of mainstream topics in philosophical semantics, epistemology and metaphysics, reformulating them in terms much more naturalistic – and less mysterious – than usual. (shrink)

We propose a theoretical framework that conceptualizes grief as coherence degradation in complex systems experiencing dependency nullification. Building from first principles of systems theory, we derive grief as a measurable system response following the disruption of dependencies that have become functionally integrated into operational architecture. This framework addresses persistent fragmentation in grief research by providing mathematical foundations that generate specific, testable hypotheses about grief mechanisms and individual differences. We propose three core hypotheses: dependency integration depth predicts grief (...) intensity, metacognitive vulnerability patterns determine complication risk, and threshold dynamics distinguish adaptive from maladaptive responses. Available empirical evidence provides preliminary support for these predictions, though systematic validation through studies designed specifically to test coherence degradation mechanisms remains necessary. The framework offers potential foundations for precision assessment and targeted intervention while revealing connections between individual grief experiences and universal principles governing complex system adaptation. (shrink)

Collected Papers, Vol. XVII – A Multi-Disciplinary Approach to Uncertainty, Indeterminacy, and Complex Systems is a substantial and wide-ranging volume, bringing together 74 articles across nearly 1,000 pages. The contributions, originally published in a variety of international scientific journals, reflect the breadth and maturity of contemporary research in neutrosophy and its related domains. The volume explores a rich spectrum of interdisciplinary fields, including logic, philosophy, physics, mathematics, statistics, information fusion, artificial intelligence, decision theory, complex systems, and (...) robotics, offering both foundational developments and applied methodologies for modeling uncertainty, indeterminacy, and contradiction. All papers are authored or co-authored by Florentin Smarandache, either individually or in collaboration with 69 distinguished co-authors from 18 countries (Ahmed Abdelhafeez, Mohamed Abdel Basset, Hamiden Abd El-Wahed Khalifa, Ijaz Ahmad, Muhammad Ahsan, Alhanouf Alburaikana, Ahmed M. Ali, Anum Ali, Mubashir Ali, Wajid Ali, Sulaiman Al Amro, Rafif Alhabib, Saleh I. Alzahrani, Farrukh Arslan, Muhammad Arshad, Büşra Aydoğan, Robert N. Boyd, Victor Christianto, Ajoy Kanti Das, Rakhal Das, Suman Das, Rama Debbarma, Srila Dey, Dinh Van Dzung, Alaa Elmor, Takaaki Fujita, Daniela Gifu, Mohammad Hamidi, Hoang Anh Tuan, A. Kalavathi, Khuram Ali Khan, S. Krishnaprakash, Duraisamy Kumar, Luong Thi Hong Lan, Le Nhu Dieu Huon, Maikel Yelandi Leyva-Vázquez, Priyanka Majumder, Ahmed A. Metwaly, Mona Mohamed, Noreen Mushtaq, Ion Nălbitoru, Nguyen Long Giang, Nguyen Thi Hien, Nguyen Thi Lan Nhi, Nguyen Thien Luong, Nguyen Tho Thong, Nguyen Thu Huong, Murat Olgun, Gabrijela Popovic, Michael Angelo B. Promenti, Afshan Qayyum, Atiqe Ur Rahman, Shazia Rana, Muhammad Saeed, Alaa Salem, A. Saranya, Alexandra Şandru, Ovidiu Ilie Şandru, Tanzeela Shaheen, Dragisa Stanujkic, Maja Stanujkic, Mohadeseh Taghinezhad, John Frederick D. Tapia, Mehmet Ünver, Vo Si Nam, Michael Voskoglou, Muhammad Waqas, Muhammad Yasir, Badria Almaz Ali Yousif), underscoring the global reach and collaborative nature of neutrosophic research, and revealing a coherent yet diverse intellectual landscape, ranging from theoretical frameworks—such as neutrosophic logic, hypersoft sets, superhyperstructures, and revolutionary topologies—to practical applications in healthcare analytics, artificial intelligence, sustainability, engineering, and decision-making under uncertainty. Together, the works collected in this seventeenth volume provide both a comprehensive reference and a forward-looking perspective on the evolving role of neutrosophic and multi-valued approaches in addressing complex real-world problems. (shrink)

The fast convergence of predictive artificial intelligence and neurobiological mapping has brought back Hard Determinism, leading to the common assumption that since biological systems are lawful, human agency is an illusion and the future is a static "block universe". This paper argues that this conclusion—what we call Causal Nihilism—is actually a category error in system modeling. -/- We show that even if we strictly assume a causally closed, deterministic manifold, the long-term future of a complex system remains Informationally (...) Inaccessible. By integrating Chaos Theory with Landauer’s Principle of thermodynamic information, we demonstrate that the computational cost of predicting a complex agent quickly exceeds what any physically embedded observer can feasibly handle [Cover & Thomas, 2006]. We propose the "Unpredictability Horizon" as a clear boundary where outside prediction breaks down. We therefore move past the binary definition of Free Will into a scalar metric we call Agency Depth (DA), suggesting that growing this internal complexity is the main functional scope of action for living agents. -/- We conclude that because the future is computationally irreducible for any complex agent, the agent’s internal processing becomes the Salient Cause of the outcome, upholding the dignity of agency as a necessary thermodynamic process. (shrink)

Abstract -/- The development of complex organ systems from DNA instructions exemplifies a universal principle governing nature: the law of balance. This principle asserts that local interactions governed by rules of proportion, feedback, and dynamic equilibrium generate emergent order across scales, from molecular biology to cosmic structures. This paper explores how embryonic development illustrates self-organization guided by balance, situates this principle in broader natural systems such as ecosystems and galaxies, and argues for its universality as a framework (...) for understanding not only biological life but also societal and environmental stability. -/- . (shrink)

In this paper the central ideas and history of the theory of complex systems are described. It is shown how this theory lends itself to different interpretations and, correspondingly, to different political conclusions.

Many of the diverse attempts to solve the Hard Problem of Consciousness rely on emergentism. Those who adopt a strong version of emergentism usually argue that consciousness is ontologically irreducible to its underlying physical processes. In contrast, those who adopt a weak version of emergentism usually argue that consciousness is explanatorily irreducible to its underlying physical processes — whether due to epistemic or pragmatic limitations. In this context, the central argument of this paper is that Daniel Dennett’s account of consciousness (...) can be understood as adopting a weak form of emergentism. His Multiple Drafts Model aligns neatly with a nuanced form of weak emergence, grounded both in circumstantial constraints and in the limits of our understanding. Although Dennett spoke rarely and cautiously about emergence, and despite his commitment to qualia-eliminativism (as a consequence of his Heterophenomenology), his account of consciousness nonetheless satisfies the central hallmark of weak emergentism: consciousness is, indeed, a phenomenon irreducible to its subphenomena — but only in practice, not in principle. (shrink)

This collection of scientific papers from the KMOCS-2025 conference represents a comprehensive exploration of contemporary approaches in mathematical modeling, optimization, and artificial intelligence across multiple engineering and technological domains. The proceedings are organized into three thematic sections that collectively demonstrate the interconnected nature of modern computational science. The first section focuses on perspective directions in mathematical modeling, featuring research on multiphysics modeling in aerospace structural design, vibration resistance of reinforced cylindrical shells, stability analysis of hollow shells under thermal loads, heat (...) and moisture regenerators, sloshing dynamics in partitioned tanks, and crack propagation in energy machinery structural elements. Papers also address mathematical models for predicting thermal fields, processing big data in distributed information systems, and modeling control systems using ECAD tools. The second section presents models and optimization methods, covering topics such as computational thermodynamic-kinetic simulation of vacuum steel refining, optimization of combined heating systems, investment portfolio distribution, routing optimization with spatial and weight constraints, and discrete Fourier transform applications in digital image processing. Special attention is given to hybrid evolutionary algorithms for discrete optimization problems under uncertainty, predictive analysis for electricity consumption regulation, and resource allocation in project management. The third section explores artificial intelligence models and methods, including YOLO neural networks for plant growth monitoring, fuzzy logic applications in blockchain network monitoring, explainable decision support systems integrating human expertise with computational insights, reinforcement learning for UAV coverage path planning, and physics-informed neural networks for solving multidimensional dynamic problems. The section also covers predictive analytics for stochastic processes, ontological approaches in intelligent hardware-software systems, semantic segmentation of digital images, gesture recognition in real-time, and post-quantum encryption algorithms. (shrink)

Scientists depend on complex computational systems that are often ineliminably opaque, to the detriment of our ability to give scientific explanations and detect artifacts. Some philosophers have s...

Comprehensive when it was published in 1990, this guide brought together information on the broad spectrum of education and research opportunities then available in the sciences of complexity. Prepared under the direction physicist-author Fritjof Capra and pioneering cyberneticist Heinz Von Foerster, the guide's purpose was to make these kinds of investigations more accessible by providing information on programs, institutions, organizations, and literature where one can learn about their principles, methods, and applications. The guide was intended to help interested students and (...) educators locate the various academic fields, departments, institutes, and programs that offer education in systems thinking and complex systems research. The guide also served professionals who are already involved in the systems sciences as a directory to general activity in what are otherwise widely separate fields. By browsing the guide one can get a snapshot of where education and research in systems science was happening at the time in both North America and Europe. (shrink)

The digitalization of human society continues at a relentless rate. However, to develop modern information technologies, the increasing complexity of the real-world must be modeled, suggesting the general need to reconsider how to carry out conceptual modeling. This research proposes that the often-overlooked notion of ‘‘system’’ should be a separate, and core, conceptual modeling construct and argues for incorporating it and related concepts, such as emergence, into existing approaches to conceptual modeling. The work conducts a synthesis of the ontology of (...) systems and general systems theory. These modeling foundations are then used to propose a CESM+ template for conducing systems-grounded conceptual modeling. Several new conceptual modeling notations are introduced. The systemist modeling is then applied to a case study on the development of a citizen science platform. The case demonstrates the potential contributions of the systemist approach and identifies specific implications of explicit modeling with systems for theory and practice. The paper provides recommendations for how to incorporate systems into existing projects and suggests fruitful opportunities for future conceptual modeling research. (shrink)

This paper examines the complexity of the interconnection of varna and caste systems in Indian society. It reflects the complexities of the traditions of different caste groups and the system that regulates their relationship with each other. It will also reflect on the modes where they turn slightly flexible and become harshly rigid. The concept of community no longer exists. However, it is a stronger claim but is made while stressing the importance of the social Purushārtha Sādhana. Both terms (...) (caste and varna) are used interchangeably. However, a question regarding their meaning would need an entire book to be written. Both words are too ambiguous, especially for non-Indians or non-Hindus, though everyone, including Hindus and Indians, may find it challenging to distinguish between them. (shrink)

This paper proposes a foundational operating system for consciousness by modeling consciousness as a measurable, structured energetic field rather than a purely subjective or emergent phenomenon. It introduces a formal three-layer ontology of consciousness—Ordered Energy (OE), Entropic Energy (EE), and Relational Energy (RE)—and defines quantitative indices for consciousness measurement and coherence, including Vibrational Consciousness Energy (VCE), Consciousness Resonance Index (CRI), and Conscious Field Index (CFI). -/- Building on this ontology, the paper presents the Consciousness Operating System (COS) as a multi-layered (...) architectural framework integrating consciousness metrics, information flow, and system-level coordination. The work further situates consciousness within a civilization-scale model through the Consciousness–Kardashev Scale (CK0–CK5), providing a bridge between philosophy of mind, ontology, complex systems theory, and applied scientific research. -/- This document serves as a foundational reference and prior-art declaration for consciousness-based science, artificial intelligence alignment, and civilization-scale system design. (shrink)

This manifesto introduces Systemic Physics as a new theoretical domain grounded in the principles of emergence, synergy, and ontological continuity. It serves as the most comprehensive formulation of the Systemic Continuum Paradigm (SCP) to date—an approach that redefines reality not as a collection of fundamental particles, but as a multi-layered field of threshold-bound systems whose properties arise only through structured interactions. By rejecting the traditional dichotomy between "natural" and "artificial" systems, the SCP proposes that all organized ensembles—ranging from (...) galaxies to neural networks—follow the same systemic logic: properties such as gravity, intelligence, resilience, and expansion emerge only when internal synergy (ISB) crosses a Systemic Threshold (ST), at which point one dynamic monopolizes the General Systemic Balance (GSB) of that scale. This paper consolidates the ontological axioms, scale-relative mechanisms, and falsifiability criteria of the SCP; it reinterprets dark matter and dark energy as phase transitions within gravitational synergy, redefines forces as monopolies of emergence, and outlines a cross-domain roadmap for scientific application. Designed for physicists, philosophers, and complexity theorists, it positions emergence not as a metaphor, but as the structural law of reality. It is both a theoretical scaffold and a call to action: to build a science that measures thresholds instead of assuming absolutes, and to approach the universe not as a closed equation, but as a living architecture of unfolding systemic resonance. (shrink)

Functional decomposition is an important goal in the life sciences, and is central to mechanistic explanation and explanatory reduction. A growing literature in philosophy of science, however, has challenged decomposition-based notions of explanation. ‘Holists’ posit that complex systems exhibit context-sensitivity, dynamic interaction, and network dependence, and that these properties undermine decomposition. They then infer from the failure of decomposition to the failure of mechanistic explanation and reduction. I argue that complexity, so construed, is only incompatible with one notion (...) of decomposition, which I call ‘atomism’, and not with decomposition writ large. Atomism posits that function ascriptions must be made to parts with minimal reference to the surrounding system. Complexity does indeed falsify atomism, but I contend that there is a weaker, ‘contextualist’ notion of decomposition that is fully compatible with the properties that holists cite. Contextualism suggests that the function of parts can shift with external context, and that interactions with other parts might help determine their context-appropriate functions. This still admits of functional decomposition within a given context. I will give examples based on the notion of oscillatory multiplexing in systems neuroscience. If contextualism is feasible, then holist inferences are faulty—one cannot infer from the presence of complexity to the failure of decomposition, mechanism, and reductionism. (shrink)

Achieving Coherence offers a new way to understand and work with the complexity that defines our time. In a world shaped by uncertainty and deep interconnection, the SPARC framework, short for Spectrum of Possibility and Recursive Choice, provides a practical model for bridging insight and action across fields. The book explores how systems sustain stability and adapt under changing conditions through the principles of coherence, constraint satisfaction, and recursive feedback. By examining how dimensional shifts, layered constraints, and resilience emerge (...) from dynamic interaction, SPARC reveals how structure and flexibility coexist within evolving environments. -/- Its ideas apply across a wide range of contexts, from strengthening critical infrastructure and managing ecological systems to improving artificial intelligence and coordinating multi-agent networks. By showing how local choices ripple through larger systems and how adaptation depends on the balance between order and variation, Achieving Coherence offers a clear and forward-looking approach to navigating complexity and designing systems that can grow, learn, and endure. (shrink)

In this paper, a framework incorporating flexibility as a characteristic is proposed for designing complex, resilient socio-ecological systems. In an interconnected complex system, flexibility allows prompt deployment of resources where they are needed and is crucial for both innovation and robustness. A comparative analysis of flexible manufacturing systems, economics, evolutionary biology, and supply chain management is conducted to identify the most important characteristics of flexibility. Evolutionary biology emphasises overlapping functions and multi-functionality, which allow a system with (...) structurally different elements to perform the same function, enhancing resilience. In economics, marginal cost and marginal expected profit are factors that are considered to be important in incorporating flexibility while making changes to the system. In flexible manufacturing systems, the size of choice sets is important in creating flexibility, as initial actions preserve more options for future actions that will enhance resilience. Given the dynamic nature of flexibility, identifying the characteristics that can lead to flexibility will introduce a crucial dimension to designing resilient and sustainable socio-ecological systems with a long-term perspective in mind. (shrink)

(This version was submitted to Behavioral and Brain Science. A revised version was published by Biological Theory) Estimates of autism’s incidence increased 5-10 fold in ten years, an increase which cannot be genetic. Though many mutations are associated with autism, no mutation seems directly to cause autism. We need to find the direct cause. Complexity science provides a new paradigm - confirmed in biology by extensive hard data. Both the body and the personality are complex dynamic systems which (...) spontaneously self-organize from simple dynamic systems. Autism may therefore be caused by the failure of a simple dynamic system. We know that infants who cannot track their mother’s face often become autistic, that eye-contact initiates intersubjectivity which is blocked in autism, and that the infant-mother pair seems designed to promote eye-contact, as does the eye’s appearance. This author earlier proposed that failure of eye-contact might directly cause autism and that early non-maternal childcare, including television/video, would therefore be statistically linked to autism. Waldman et al. (2008; 2006) recently proved that autism is strongly linked to precipitation (indoor activity) and to the introduction of cable. The most plausible explanation? Early exposure to television/video is linked to autism. Furthermore a normal developmental cascade (blocked in autism) has been deciphered: (a) Infant-mother eye-contact triggers increased maternal attention. (b) Early maternal attention permanently increases not only baseline vasopressin but also that oxytocin release which is triggered by subsequent maternal attention. (c) Vasopressin and oxytocin promote face recognition, gazing-at-the-eyes, emotion recognition, and social bonding. The eye-contact hypothesis suggests a clinical study addressing prevention: recruit prospective parents who agree to curtail television/video/computer/wi-fi in their families; measure autism’s incidence in their children. (shrink)

Evolving systems in both the life and physical sciences are often thought to be directional. The processes that drive the evolution of systems are diverse, ranging from natural selection to thermodynamics. However, in many treatments of these processes, the different kinds of directionality and types of ends that evolving systems trend towards are often either poorly specified or implicitly assumed. This paper aims to provide a classification of ends and directional processes that can be used to identify (...) and characterize directionality in evolving systems. Specifically, we propose that directional evolution can be either terminal or perpetual, with perpetual further divided into targeted or open-ended. Additionally, we caution against conflating organization, order, and complexity, as each tracks different properties of a directional system. (shrink)

Since its formulation in the twentieth century, Systems Theory has lacked a truly unifying paradigm. One fundamental reason for this fragmentation is the persistent anthropocentric bias that separates what we label “natural” from what we deem “artificial.” This paper argues that such a distinction is not an ontological truth but rather a cultural construct that has shaped our understanding of reality. By dismantling this false opposition, we can envision a “Systemic Continuum,” where biological, technological, and social systems are (...) studied within a single theoretical framework. This approach could revolutionize our understanding of Artificial Intelligence, technological evolution, and the emergence of complex structures, paving the way for a more robust integration of the diverse strands within Systems Theory. (shrink)

Complexity typically manifests as nonlinear relationships between the whole and parts of a system. Chaos emerges through nonlinear evolutionary processes, while emergence arises when the system develops characteristics absent in its components. By analyzing relationships between set-theoretic elements, this paper explores fundamental system theory issues. Based on the premise of a vicious cycle between collective and individual existence, the study derives core relationships governing system evolution, clarifies consistency in system transformation, and identifies the origins of complexity in chaotic and emergent (...) phenomena. (shrink)

Complexity typically manifests as nonlinear relationships between the whole and parts of a system. Chaos emerges through nonlinear evolutionary processes, while emergence arises when the system develops characteristics absent in its components. By analyzing relationships between set-theoretic elements, this paper explores fundamental system theory issues. Based on the premise of a vicious cycle between collective and individual existence, the study derives core relationships governing system evolution, clarifies consistency in system transformation, and identifies the origins of complexity in chaotic and emergent (...) phenomena. (shrink)

Complexity typically manifests as nonlinear relationships between the whole and parts of a system. Chaos emerges through nonlinear evolutionary processes, while emergence arises when the system develops characteristics absent in its components. By analyzing relationships between set-theoretic elements, this paper explores fundamental system theory issues. Based on the premise of a vicious cycle between collective and individual existence, the study derives core relationships governing system evolution, clarifies consistency in system transformation, and identifies the origins of complexity in chaotic and emergent (...) phenomena. (shrink)