Unified and causal complex-dynamic origin of standard (special and general) relativistic and quantum effects revealed previously at the lowest levels of world interaction dynamics is explicitly generalised to all higher levels of unreduced interaction processes, thus additionally confirming the causally complete character of complex-dynamical, naturally quantised relativity, which does not contain any artificially added, abstract postulates. We demonstrate some elementary applications of this generalised quantum relativity at higher levels of complex brain and social interaction dynamics.

An extended analysis compared to observations shows that modern “globalised” world civilisation has passed through the invisible “complexity threshold”, after which usual “spontaneous”, empirically driven kind of development (“invisible hand” etc.) cannot continue any more without major destructive tendencies. A much deeper, non-simplified understanding of real interaction complexity is necessary in order to cope with such globalised world development problems. Here we introduce the universal definition, fundamental origin, and dynamic equations for a major related quantity of (systemic) (...) risk characterising real complex system development tendencies at any level of dynamics. Practically important conclusions are derived, opening further detailed applications in economy, finance and development practice. (shrink)

This paper develops a unified model of semantic dynamics that links word-level meaning formation to sentence-level coherence. On the word level, each semantic point is represented by a fourfold structure {p, ip, −p, −ip} that captures signifier, signified, and their antonymic counterparts. Operators O act on this structure as mixtures of identity and complex rotations; a higher-order attractor Aw does not modify words directly but gradually reweights the operator’s internal coefficients, suppressing antonymic detours and amplifying the passage from signifier to (...) signified. Taking value as a universal attractor, the process converges toward fixed forms (a stable operator and a stable sentence vector), providing a teleological—not merely statistical—account of how meaning stabilizes. On the sentence level, discourse states are modeled as pairs (O,M). Their trajec- tories under As fall into three classes: convergence into a fixed point (meaningful), practical invariance (trash by stagnation), and outward drift (trash by divergence). Early detection becomes possible by inspecting short-run displacements of O and M: negligible change signals stagnation, monotonic decrease indicates convergence, and growth reveals divergence. These diagnostics support practical minimization strategies (compression, filtering/downweighting, recycling, and retroactive evaluation). By integrating micro-level rotations of meaning with macro-level stabilization of discourse, the framework synthesizes semiotics with dynamical-systems ideas and clarifies how semantic attractors can guide language toward coherence. The approach aligns with the view of formative operators (e.g., Microvita) as teleological organizers of meaning, without reducing them to empirical statistics or mere stylistic constraints. (shrink)

Navigating Complexity explores the defining questions of our time, how human beings make sense of themselves and their choices in a world that never stands still. The book introduces the SPARC framework, short for the Spectrum of Possibility and Recursive Choice, a model that connects philosophy, cognitive science, and systems theory to rethink how we understand behavior, autonomy, and identity. At its heart, SPARC offers a view of the mind as an adaptive process rather than a fixed thing. Through (...) the interplay of possibility and recursion, it shows how beliefs, emotions, and habits take shape within patterns of feedback and social exchange. Choice, in this view, is not a single act but an evolving negotiation between what we imagine and what the world allows. -/- Drawing from neuroscience, the philosophy of mind, and cybernetic principles, Navigating Complexity moves beyond theory into practice. It offers ways to recognize the forces that influence our thinking, to reduce noise and fragmentation, and to cultivate coherence amid uncertainty. By tracing how feedback, emotion, and context shape decision-making, the book reveals a deeper truth about adaptation. To navigate change is not simply to survive it, but to participate in shaping what comes next. (shrink)

Expert video game players exhibit unique behaviors compared to their less experienced counterparts. Such behaviours may also influence physiological aspects such as blinks and eyelid movements. In this study, we used the Eye Aspect Ratio (EAR) signal from a webcam to investigate the complex dynamics of eyelid movements among players with different levels of expertise in Tetris. We measured complex dynamics using recurrence quantification analysis (RQA) based measures (Determinism, Laminarity, Average Diagonal Line, and Trapping Time). Our results show that expert (...) Tetris players display more complex patterns in their eyelid behaviour, but also that some of the measures obtained using RQA correlate directly with player actions (keys pressed) and events in Tetris (numbers of lines cleared). This study provides the first example of a direct connection between RQA measures extracted from the EAR signal and behavior displayed in a game. Our results also demonstrate the potential of using RQA measures extracted from the EAR in analysing human behavior during other screen-presented tasks. (shrink)

This work presents a post-positivist research framework to explain any surprising fact in the evolutionary path of a complex, dynamic and contingent social phenomenon. Primarily, it reconciles the ontological and epistemological assumptions of Critical Realism with the principles of American Pragmatism. Then, the research approach is presented: theoretical propositions about a social structure are translated into a set of grammar rules that acknowledges a pattern of sequences of events of either individual action or social interaction between actors within a (...) real social system. The result is a discrete mathematical model for a concrete category of social process based on these rules. Finally, data-grounded refinement of the theory is possible by the comparison between cases belonging to the same category, but differing about some contingent pattern of sequences of event outcomes. Consequently, their grammars differ in some pairs of context-sensitive rules that can explain this surprising fact, such that the derivation of this alternative historical trajectory of event outcomes becomes an extension to the early category of social process. In this sense, the proposed framework suggests there is a hierarchy of classes of grammars for middle-range explanations based upon the ontological assumption of the generative nature of social reality. (shrink)

We review the recently proposed universal concept of dynamic complexity and its new mathematics based on the unreduced interaction problem solution. We then consider its progress-bringing applications at various levels of complex world dynamics, including complex-dynamical nanometal physics and living condensed matter, unreduced nanobiosystem dynamics and the integral medicine concept, causally complete management of complex economical and social dynamics, and the ensuing concept of truly sustainable world governance.

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)

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 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)

This paper proposes an architectural approach to the analysis of complex self-regulating systems aimed at resolving the gap between the registration of empirical effects and the understanding of causal mechanisms underlying system stability, transitions, and breakdown. -/- The model introduces distinguishability as a fundamental structural condition of system existence and dynamics. System regimes, phase transitions, phase windows, adaptive stability, tension-based fixation, and disorganization are interpreted as different modes of preserving, processing, or losing distinguishability over time. -/- The proposed framework operates (...) at an architectural level, allowing the integration of observable regimes into a unified causal structure without reducing system complexity or relying on discipline-specific parameters. This enables consistent differentiation between adaptive and disorganized dynamics, reversible and irreversible transitions, and stable regimes and phase windows. -/- The model is applicable across physical, biological, cognitive, social, and artificial systems, including machine learning and neural network dynamics. Architectural relations identified in the paper provide a foundation for subsequent formalization within specific scientific domains while preserving causal rigor and conceptual consistency. -/- The work is presented as a preprint and constitutes a conceptual and methodological basis for further theoretical development, empirical testing, and interdisciplinary application. (shrink)

Instead of postulated fixed structures and abstract principles of usual positivistic science, the unreduced diversity of living world reality is consistently derived as dynamically emerging results of unreduced interaction process development, starting from its simplest configuration of two coupled homogeneous protofields. The dynamically multivalued, or complex and intrinsically chaotic, nature of these real interaction results extends dramatically the artificially reduced, dynamically single-valued projection of standard theory and solves its stagnating old and accumulating new problems, “mysteries” and “paradoxes” within the unified (...) and causally complete picture of intrinsically evolving, dynamically complex reality. The permanently unfolding complexity progress thus revealed is fundamentally unlimited and does not need to stop at the directly observed diversity of usual matter complexity. The exciting, but rigorously substantiated and objectively inevitable prospects of further civilisation complexity development towards its superior levels of genuine sustainability and global Noosphere are outlined, with practically important conclusions for today’s critical problem solution. (shrink)

This summary of the original paradigm of the universal science of complexity starts with the discovered exact origin of the stagnating "end" of conventional, unitary science paradigm and development traditionally presented by its own estimates as the only and the best possible kind of scientific knowledge. Using a transparent generalisation of the exact mathematical formalism of arbitrary interaction process, we show that unitary science approach and description, including its imitations of complexity and chaoticity, correspond to artificial and ultimately (...) strong reduction of the natural plurality of unreduced interaction results called realisations to a single, "average" or "exact", realisation. This severe reduction of the natural world dynamics in unitary science underlies all its unsolvable "mysteries" and "paradoxes", persisting "difficult problems", and finally the modern "end" of the progress of just that, actually very special kind of knowledge, whose irreducible limits lead to the modern deep crisis of the global civilisation development. We show then how the rigorously substantiated restoration of the full richness of real-world dynamics within the intrinsically unified knowledge of the universal science of complexity provides not only the causally complete solution to the old and new problems of unitary science but opens practically unlimited possibilities for the new progress of science and civilisation as a result of this crucial extension, which we call complexity revolution. The unreduced analysis of the universal complexity science shows that at the current critical bifurcation point of development, we have only two incompatible possibilities and emerging tendencies, either the dangerously growing degradation within the dominating unitary science and thinking limits (irrespective of purely empirical technology power becoming even dangerous here) or the new golden age of scientific discoveries and civilisation progress with the qualitatively extended approach and results of unreduced complexity science and the new thinking it implies. (shrink)

While practical efforts in the field of artificial intelligence grow exponentially, the truly scientific and mathematically exact understanding of the underlying phenomena of intelligence and consciousness is still missing in the conventional science framework. The inevitably dominating empirical, trial-and-error approach has vanishing efficiency for those extremely complicated phenomena, ending up in fundamentally limited imitations of intelligent behaviour. We provide the first-principle analysis of unreduced many-body interaction process in the brain revealing its qualitatively new features, which give rise to rigorously defined (...) chaotic, noncomputable, intelligent and conscious behaviour. Based on the obtained universal concepts of unreduced dynamic complexity, intelligence and consciousness, we derive the universal laws of intelligence applicable to any kind of intelligent system interacting with the environment. We finally show why and how these fundamentally substantiated and therefore practically efficient laws of intelligent system dynamics are indispensable for correct AI design and training, which is urgently needed in this time of critical global change towards the truly sustainable development. (shrink)

The unreduced solution to the arbitrary interaction problem, absent in the standard theory framework, reveals many equally real and mutually incompatible system configurations, or "realizations". This is the essence of universal dynamic undecidability, or multivaluedness, and the ensuing causal randomness (unpredictability), non-computability, irreversible time flow (evolution, emergence), and dynamic complexity of every real system, object, or process. This creative undecidability of real-world dynamics provides causal explanations for "quantum mysteries", relativity postulates, cosmological problems, and the huge efficiency of (...) high-complexity phenomena, such as life, intelligence, and consciousness, giving rise to extended applications, far beyond the critical limits of usual science paradigm. (shrink)

We show that critically accumulating "difficult" problems, contradictions and stagnation in modern science have the unified and well-specified mathematical origin in the explicit, artificial reduction of any interaction problem solution to an "exact", dynamically single-valued (or unitary) function, while in reality any unreduced interaction development leads to a dynamically multivalued solution describing many incompatible system configurations, or "realisations", that permanently replace one another in causally random order. We obtain thus the universal concept of dynamic complexity and chaos impossible (...) in unitary mathematics. This huge difference between the unreduced mathematics of real-world dynamics and strongly limited unitary “models” of traditional mathematics inevitably induces a growing series of “unsolvable” problems and other “mysteries” that culminate now in the crisis of the “end of science”, where the stagnating unitary “science” in question includes only the described limitation of the traditional mathematical framework (unfortunately accepted as the unique possible basis for any scientific knowledge). In this brief review, we show that science extension to the unreduced, dynamically multivalued mathematics of the intrinsically complex real-world dynamics provides stagnating problem solutions and reconstitution of the intrinsic causality and unity of science desperately missing in its artificially limited unitary framework. Painful stagnation of the latter should be replaced now by the unlimited new progress of the extended, causally complete knowledge of the universal science of complexity, which provides the urgently needed issue from the current impasse of the global civilisation development. (shrink)

A dynamic semantics for epistemically modalized sentences is an attractive alternative to the orthodox view that our best theory of meaning ascribes to such sentences truth-conditions relative to what is known. This essay demonstrates that a dynamic theory about might and must offers elegant explanations of a range of puzzling observations about epistemic modals. The first part of the story offers a unifying treatment of disputes about epistemic modality and disputes about matters of fact while at the same (...) time avoiding the complexities of alternative theories. The second part of the story extends the basic framework to cover some complicated data about retraction and the interaction between epistemic modality and tense. A comparison between the suggestion made in this essay and current versions of the orthodoxy is provided. (shrink)

Complex problem solving (CPS) and related topics such as dynamic decision-making (DDM) and complex dynamic control (CDC) represent multifaceted psychological phenomena. In a broad sense, CPS encompasses learning, decision-making, and acting in complex and dynamic situations. Moreover, solutions to problems that people face in such situations are often generated in teams or groups. In turn, this adds another layer of complexity to the situation itself because of the emerging issues that arise from the social dynamics of (...) group interactions. This framing of CPS means that it is not a single construct that can be measured by using a particular type of CPS task (e.g. minimal complex system tests), which is a view taken by the psychometric community. The proposed approach taken here is that because CPS is multifaceted, multiple approaches need to be taken to fully capture and understand what it is and how the different cognitive processes associated with it complement each other. (shrink)

This paper argues that the notion of interstitial transformation, whereby new social practices emerge within the gaps of existing orders and gain prominence, is crucial for understanding social change in a complex world. Our work builds on the contributions of two distinct scholarly groups who have explored social complexity. The first are emancipatory radicals who look to bottom-up interstitial change to achieve radical social change. The second is classical or “Open Society” liberals who wish to leverage interstitial complexity (...) within civil society and the economy against a stable background of liberal rules. This paper brings these distinct literatures into conversation with one another, aiming to reconcile their insights and identify weaknesses in their approaches. -/- We argue that both groups overestimate the feasibility of instrumentalising interstitial complexity for their goals. Using a stylised model of interstitial social struggle, we explore the social dynamics occurring in a world populated by both emancipatory radicals and Open Society liberals. We posit an emergent strategic struggle between emancipatory radicals who use prefigurative strategies to affect the fundamental transformation of the liberal legal order and the Open Society governors who resist them. We stress that the overall result of this struggle will likely exceed the intentions of both parties. Our model should provide valuable insights for socialists, anarchists, radicals, and liberals concerned with social transformation and the robustness of the liberal Open Society. (shrink)

We propose a qualitatively new kind of governance for the emerging need to efficiently guide the densely interconnected, ever more complex world development, which is based on explicit and openly presented problem solutions and their interactive implementation practice within the versatile, but unified professional analysis of complex real-world dynamics, involving both the powerful central units and the attached creative worldwide network of professional representatives. We provide fundamental and rigorous scientific arguments in favour of introduction of just that kind of governance (...) at the modern development stage, after the complexity threshold in the real world dynamics evolution (sometimes intuitively designated as "globalisation"), where the traditional spontaneous-empirical kind of development and related administrative governance lose dramatically and inevitably their efficiency and the necessity of explicit consistent understanding and guidance of complex real-world dynamics becomes evident. (shrink)

The deep crisis in modern fundamental science development is ever more evident and openly recognised now even by mainstream, official science professionals and leaders. By no coincidence, it occurs in parallel to the world civilisation crisis and related global change processes, where the true power of unreduced scientific knowledge is just badly missing as the indispensable and unique tool for the emerging greater problem solution and further progress at a superior level of complex world dynamics. Here we reveal the mathematically (...) exact reason for the crisis in conventional science, containing also the natural and unified problem solution in the form of well-specified extension of usual, artificially restricted paradigm. We show how that extended, now causally complete science content provides various "unsolvable" problem solutions and opens new development possibilities for both science and society, where the former plays the role of the main, direct driver for the latter. We outline the related qualitative changes in science organisation, practice and purposes, giving rise to the sustainability transition in the entire civilisation dynamics towards the well-specified superior level of its unreduced, now well understood and universally defined complexity. (shrink)

We explain why exactly the simplified abstract scheme of reality within the standard science paradigm cannot provide the consistent picture of “truly fundamental” reality and how the unreduced, causally complete description of the latter is regained within the extended, provably complete solution to arbitrary interaction problem and the ensuing concept of universal dynamic complexity. We emphasize the practical importance of this extension for both particular problem solution and further, now basically unlimited fundamental science development (otherwise dangerously stagnating within (...) its traditional paradigm). (shrink)

Dynamic Categorization, emerging from the principle of Ontological Instability, represents a revolutionary approach to philosophical categorization that embraces uncertainty and fluctuation as fundamental features of reality rather than problems to be solved. However, this framework's flexibility and recognition of instability create significant vulnerabilities to misuse in contemporary contexts characterized by disinformation campaigns, algorithmic bias, and hyper-fragmented political epistemologies. This paper provides the first comprehensive analysis of these vulnerabilities while developing robust theoretical and practical safeguards to defend the framework's validity (...) and effectiveness. -/- Through systematic examination of three primary misuse patterns—Arbitrary Relativism, Manipulative Flexibility, and Technological Determinism—this analysis demonstrates that Dynamic Categorization can be exploited to justify false equivalencies, amplify algorithmic bias, and contribute to epistemic fragmentation. These misuses threaten both the intellectual integrity of the framework and its capacity to address contemporary challenges requiring sophisticated categorical innovation. -/- The paper develops a comprehensive defensive framework based on three interconnected layers of protection: Institutional Safeguards (independent monitoring, democratic accountability, regulatory frameworks, professional standards), Technical Safeguards (audit trails, bias detection, validation procedures, transparency requirements), and Cultural Safeguards (professional education, public engagement, ethical reflection, community building). This multi-layered approach provides protection against misuse while preserving the creative potential that makes Dynamic Categorization valuable for addressing complex contemporary challenges. -/- The analysis demonstrates that legitimate Dynamic Categorization can be distinguished from its pathological distortions through systematic application of three core principles: the Integrity Principle (maintaining systematic coherence through creative consistency), the Transparency Principle (employing open methodological procedures), and the Responsibility Principle (ensuring ethical accountability in application). These principles provide practical criteria for evaluating categorical applications and preventing exploitation. -/- The paper concludes that Dynamic Categorization, when properly safeguarded, represents an essential tool for navigating contemporary complexity and uncertainty. Rather than abandoning the framework due to its vulnerabilities, the analysis demonstrates that sophisticated defensive mechanisms can protect against misuse while preserving the innovative potential necessary for addressing climate change, technological disruption, global interconnection, and cultural transformation. This work establishes the foundation for responsible development and application of post-essentialist philosophical frameworks in the twenty-first century. (shrink)

Dynamic epistemic logic (DEL) extends purely modal epistemic logic (S5) by adding dynamic operators that change the model structure. Propositional dynamic logic (PDL) extends basic modal logic with programs that allow the de nition of complex modalities. We provide a common generalisation: a logic that is dynamic in both senses, and one that is not limited to S5 as its modal base. It also incorporates, and signi cantly generalises, all the features of existing extensions of DEL (...) such as BMS [3] and LCC [21]. Our dynamic operators work in two steps. First, they provide a multiplicity of transformations of the original model, one for each action in a purely syntactic action structure (in the style of BMS). Second, they specify how to combine these multiple copies to produce a new model. In each step, we use the generality of PDL to specify the transformations. The main technical contribution of the paper is to provide an axiomatisation of this general dynamic dynamic logic (GDDL). This is done by providing a computable translation of GDDL formulas to equivalent PDL formulas, thus reducing the logic to PDL, which is decidable. The proof involves switching between representing programs as terms and as automata. We also show that both BMS and LCC are special cases of GDDL, and that there are interesting applications that require the additional generality of GDDL, namely the modelling of private belief update. More recent extensions and variations of BMS and LCC are also discussed. (shrink)

Complex problem solving (CPS) emerged in the last 30 years in Europe as a new part of the psychology of thinking and problem solving. This paper introduces into the field and provides a personal view. Also, related concepts like macrocognition or operative intelligence will be explained in this context. Two examples for the assessment of CPS, Tailorshop and MicroDYN, are presented to illustrate the concept by means of their measurement devices. Also, the relation of complex cognition and emotion in the (...) CPS context is discussed. The question if CPS requires complex cognition is answered with a tentative “yes.”. (shrink)

We introduce the notion of complexity, first at an intuitive level and then in relatively more concrete terms, explaining the various characteristic features of complex systems with examples. There exists a vast literature on complexity, and our exposition is intended to be an elementary introduction, meant for a broad audience. -/- Briefly, a complex system is one whose description involves a hierarchy of levels, where each level is made of a large number of components interacting among themselves. The (...) time evolution of such a system is of a complex nature, depending on the interactions among subsystems in the next level below the one under consideration and, at the same time, conditioned by the level above, where the latter sets the context for the evolution. Generally speaking, the interactions among the constituents of the various levels lead to a dynamics characterized by numerous characteristic scales, each level having its own set of scales. What is more, a level commonly exhibits ‘emergent properties’ that cannot be derived from considerations relating to its component systems taken in isolation or to those in a different contextual setting. In the dynamic evolution of some particular level, there occurs a self-organized emergence of a higher level and the process is repeated at still higher levels. -/- The interaction and self-organization of the components of a complex system follow the principle commonly expressed by saying that the ‘whole is different from the sum of the parts’. In the case of systems whose behavior can be expressed mathematically in terms of differential equations this means that the interactions are nonlinear in nature. -/- While all of the above features are not universally exhibited by complex systems, these are nevertheless indicative of a broad commonness relative to which individual systems can be described and analyzed. There exist measures of complexity which, once again, are not of universal applicability, being more heuristic than exact. The present state of knowledge and understanding of complex systems is itself an emerging one. Still, a large number of results on various systems can be related to their complex character, making complexity an immensely fertile concept in the study of natural, biological, and social phenomena. -/- All this puts a very definite limitation on the complete description of a complex system as a whole since such a system can be precisely described only contextually, relative to some particular level, where emergent properties rule out an exact description of more than one levels within a common framework. -/- We discuss the implications of these observations in the context of our conception of the so-called noumenal reality that has a mind-independent existence and is perceived by us in the form of the phenomenal reality. The latter is derived from the former by means of our perceptions and interpretations, and our efforts at sorting out and making sense of the bewildering complexity of reality takes the form of incessant processes of inference that lead to theories. Strictly speaking, theories apply to models that are constructed as idealized versions of parts of reality, within which inferences and abstractions can be carried out meaningfully, enabling us to construct the theories. -/- There exists a correspondence between the phenomenal and the noumenal realities in terms of events and their correlations, where these are experienced as the complex behavior of systems or entities of various descriptions. The infinite diversity of behavior of systems in the phenomenal world are explained within specified contexts by theories. The latter are constructs generated in our ceaseless attempts at interpreting the world, and the question arises as to whether these are reflections of `laws of nature' residing in the noumenal world. This is a fundamental concern of scientific realism, within the fold of which there exists a trend towards the assumption that theories express truths about the noumenal reality. We examine this assumption (referred to as a ‘point of view’ in the present essay) closely and indicate that an alternative point of view is also consistent within the broad framework of scientific realism. This is the view that theories are domain-specific and contextual, and that these are arrived at by independent processes of inference and abstractions in the various domains of experience. Theories in contiguous domains of experience dovetail and interpenetrate with one another, and bear the responsibility of correctly explaining our observations within these domains. -/- With accumulating experience, theories get revised and the network of our theories of the world acquires a complex structure, exhibiting a complex evolution. There exists a tendency within the fold of scientific realism of interpreting this complex evolution in rather simple terms, where one assumes (this, again, is a point of view) that theories tend more and more closely to truths about Nature and, what is more, progress towards an all-embracing ‘ultimate theory’ -- a foundational one in respect of all our inquiries into nature. We examine this point of view closely and outline the alternative view -- one broadly consistent with scientific realism -- that there is no ‘ultimate’ law of nature, that theories do not correspond to truths inherent in reality, and that successive revisions in theory do not lead monotonically to some ultimate truth. Instead, the theories generated in succession are incommensurate with each other, testifying to the fact that a theory gives us a perspective view of some part of reality, arrived at contextually. Instead of resembling a monotonically converging series successive theories are analogous to asymptotic series. -/- Before we summarize all the above considerations, we briefly address the issue of the complexity of the {\it human mind} -- one as pervasive as the complexity of Nature at large. The complexity of the mind is related to the complexity of the underlying neuronal organization in the brain, which operates within a larger biological context, its activities being modulated by other physiological systems, notably the one involving a host of chemical messengers. The mind, with no materiality of its own, is nevertheless emergent from the activity of interacting neuronal assemblies in the brain. As in the case of reality at large, there can be no ultimate theory of the mind, from which one can explain and predict the entire spectrum of human behavior, which is an infinitely rich and diverse one. (shrink)

Universal Basic Income (UBI) has been proposed as a potential way in which welfare states could be made more responsive to the ever-shifting evolutionary challenges of institutional adaptation in a dynamic environment. It has been proposed as a tool of “real freedom” (Van Parijs) and as a tool of making the welfare state more efficient. (Friedman) From the point of view of complexity theory and evolutionary economics, I argue that only a welfare state model that is “polycentrically” (Polanyi, (...) Hayek) organized as an evolving network of distributed decision-making can respond to the challenges of complexity in an adaptively efficient manner. UBI has the theoretical possibility to facilitate flexible, bottom-up innovation, since a) it embodies the “rule of law” principles of generality, nondiscrimination, simplicity, and transparency, and b) it grants people widespread freedom to experiment, innovate, and deviate from established practices and norms; but whether it can be made to work as intended depends on a number of variables in its design. I situate my research in the contemporary UBI debate around automation and technological development, which I interpret creatively through the lens of contemporary evolutionary political economy. I advance a syncretic model of institutional design based upon the shared insights of the Santa Fe, Neo-Hayekian, and Neo-Schumpeterian schools of evolutionary economics that, following Adam Thierer and Michael Munger, I call the framework of Permissionless Innovation (PI). Under the PI framework, which is a development of the tradition of evolutionary liberalism (Hume, Smith, Mandeville, Mill, Hayek, Polanyi, Schumpeter, Hodgson), individuals are granted the default right to innovate without having to ask for permission, and the right to basic income, which they can use to support their right to innovate, mutate, and experiment. This framework is a liberal framework to the extent it relies on widespread human freedom as a means of fostering innovation and social learning. And it is an evolutionary framework to the extent that it seeks to facilitate evolutionary socioeconomic processes for the sake of social progress and welfare advancement. I proceed to show, through an analysis of how innovation operates in both the economy and the cultural domain, that UBI can be used as an important cornerstone of an evolutionary liberal model of “ecostructural” governance that treats the socioeconomic order as an ecological garden of spontaneous growths, adaptations, and innovations. I call this the Permissionless Innovation Universal Basic Income (PIUBI) framework. In it, the right to innovate, deviate, and mutate is upheld as the “gold standard” of institutional design whereby individuals are granted widespread freedoms combined with the provision of UBI and other innovation-fostering (carefully bound) services and regulations. In the coming decades, UBI may even enable poor people to take better advantage of Human Enhancement Technologies (HETs) and other experimental innovations. Such a framework is compatible with a fast pace of evolutionary development, but the regulatory model accompanying such a framework must be capable of responding to evolutionary lock-ins, maladaptive innovations, self-destructive behaviour on the part of UBI recipients, and the management of catastrophic and existential risks. In the end, I will have shown that the PIUBI framework can be justified as a plausible default institutional mechanism for responding to radical uncertainty, but it needs to be experimentally adapted to changing circumstances with the help of various situation-specific, polycentric, multi-level, approaches. (shrink)

The Complex Emergent Model of Language Acquisition (CEMLA) offers a new perspective on how humans acquire language, drawing on principles from complexity theory to explain this dynamic, adaptive process. Moving beyond linear and reductionist models, CEMLA views language acquisition as a system of interconnected nodes, feedback loops, and emergent patterns, operating at the edge of chaos. This framework captures the fluidity and adaptivity of language learning, highlighting how understanding and fluency arise through self-organisation, phase transitions, and interaction with (...) diverse linguistic input. By embracing the complexity of language acquisition, CEMLA provides a comprehensive model that reflects the true nature of human communication. (shrink)

Traditional stability theory focuses on a system’s ability to return to a static equilibrium point. However, complex economic and ecological subsystems exist in a state of dynamic persistence termed meta-stability. This paper formalizes the Universal Meta-Stability Framework (UMSF), introducing two primary metrics: the Stability Index (Φ) and the Balance Deviation (Ψ). Through discrete-time simulations of 100-node weighted hypergraphs, we analyze system behavior under structural shocks (Black Swan events) and the efficacy of coordinated policy bailouts. Results demonstrate that system survival (...) is not merely a function of structural strength, but of the synchronization between network topology and feedback latency. (shrink)

The “language-communication-society” triangle defies traditional scientific approaches. Rather, it is a phenomenon that calls for an integration of complex, transdisciplinary perspectives, if we are to make any progress in understanding how it works. The highly diverse agents in play are not merely cognitive and/or cultural, but also emotional and behavioural in their specificity. Indeed, the effort may require building a theoretical and methodological body of knowledge that can effectively convey the characteristic properties of phenomena in human terms. New complexity (...) approaches allow us to rethink our limited and mechanistic images of human societies and create more appropriate emo-cognitive dynamic and holistic models. We have to enter into dialogue with the complexity views coming out of other more ‘material’ sciences, but we also need to take steps in the linguistic and psycho-sociological fields towards creating perspectives and concepts better fitted to human characteristics. Our understanding of complexity is different – but not opposed – to the one that is more commonly found in texts written by people working in physics or computer science, for example. The goal of this book is to extend the knowledge of these other more ‘human’ or socially oriented perspectives on complexity, taking account of the language and communication singularities of human agents in society. (shrink)

Human linguistic phenomenon is at one and the same time an individual, social, and political fact. As such, its study should bear in mind these complex interrelations, which are produced inside the framework of the sociocultural and historical ecosystem of each human community. Understanding this phenomenon is often no easy task, due to the range of elements involved and their interrelations. The absence of valid, clearly developed paradigms adds to the problem and means that the theoretical conclusions that emerge may (...) be unclear on certain points. It is true that in the last fifty years sociolinguistic studies have advanced considerably, and today we have access to an impressive set of data and a wide variety of theoretical reflections. But as a discipline sociolinguistics does not yet have unified, powerful theoretical models able to account rigorously and clearly for the phenomena it studies. Sociolinguistic studies are today a diverse set of contributions in which certain and theoretical schools and lines of research emerged; but as is to be expected in a relatively new field, there is not enough communication between the various schools and they cannot yet be said to be integrated in terms of their conceptual and theoretical postulates. Against this background, our work aims to contribute to the overall, integrated understanding of the processes of language contact. Via an interdisciplinary, eclectic approach, it also aims to aid the theoretical grounding and integration of a unified, common sociolinguistic paradigm. Our strategy will not be merely to combine the contributions from ongoing research lines, but to address the question from a more global viewpoint which, together with the more innovative contemporary scientific disciplines, permits a harmonious integration of the various sociolinguistic perspectives in a broad, deep and unitary approach to the reality. The materials used to construct this unified approach are taken from many sources: Theoretical physics, ecology, the philosophy of science and mind, anthropology, phenomenological and process sociology, cognitive sciences, political science, pragmatics, history, systems theory, approaches to complexity and obviously sociolinguistics, are all involved in a dialogue in this desire for integration. (shrink)

This thesis presents a novel theoretical framework for addressing one of the most pressing challenges in contemporary artificial intelligence: how fluctuational AI-driven decision systems can ethically balance epistemic uncertainty, human autonomy, and social equity in high-stakes environments. Current approaches to AI ethics treat these three dimensions as separate, static concerns to be optimized independently. However, this research demonstrates that in fluctuational AI systems operating in critical domains such as healthcare, criminal justice, and financial services, these elements exist in a state (...) of dynamic tension where changes in one dimension necessarily affect the others. -/- The central contribution of this work is the Dynamic Equilibrium Theory (DET), which introduces the concept of fluctuational ethics—the idea that ethical AI systems must be designed to handle not just uncertainty in data or predictions, but uncertainty in the ethical landscape itself. The framework proposes a triadic model where epistemic uncertainty, human autonomy, and social equity form an interconnected system that must maintain dynamic equilibrium rather than static optimization. -/- Through comprehensive analysis of empirical data from real-world AI deployments, including the COMPAS criminal justice algorithm and healthcare diagnostic systems, this research provides quantitative evidence for the interdependence of these ethical dimensions. The ProPublica analysis of COMPAS reveals that Black defendants were 1.96 times more likely to be falsely classified as high risk compared to White defendants, while White defendants were 1.71 times more likely to be incorrectly classified as low risk—demonstrating how epistemic uncertainty, human autonomy, and social equity interact in complex ways that current frameworks fail to address. -/- The thesis develops mathematical formalizations of the dynamic equilibrium using stochastic differential equations and tensor representations, providing a rigorous foundation for implementation. The framework is validated through case studies across multiple high-stakes domains, showing how different contexts require different equilibrium points while maintaining the fundamental integrity of all three dimensions. -/- This work represents a paradigm shift from static, rule-based ethical frameworks to dynamic, adaptive systems that recognize the inherent fluctuational nature of AI decision-making. The implications extend beyond technical implementation to policy and governance, suggesting new approaches to AI regulation that account for temporal ethical dynamics and contextual sensitivity. (shrink)

Climatology is a paradigmatic complex systems science. Understanding the global climate involves tackling problems in physics, chemistry, economics, and many other disciplines. I argue that complex systems like the global climate are characterized by certain dynamical features that explain how those systems change over time. A complex system's dynamics are shaped by the interaction of many different components operating at many different temporal and spatial scales. Examining the multidisciplinary and holistic methods of climatology can help us better understand the nature (...) of complex systems in general. -/- Questions surrounding climate science can be divided into three rough categories: foundational, methodological, and evaluative questions. "How do we know that we can trust science?" is a paradigmatic foundational question (and a surprisingly difficult one to answer). Because the global climate is so complex, questions like "what makes a system complex?" also fall into this category. There are a number of existing definitions of `complexity,' and while all of them capture some aspects of what makes intuitively complex systems distinctive, none is entirely satisfactory. Most existing accounts of complexity have been developed to work with information-theoretic objects (signals, for instance) rather than the physical and social systems studied by scientists. -/- Dynamical complexity, a concept articulated in detail in the first third of the dissertation, is designed to bridge the gap between the mathematics of contemporary complexity theory (in particular the formalism of "effective complexity" developed by Gell-Mann and Lloyd [2003]) and a more general account of the structure of science generally. Dynamical complexity provides a physical interpretation of the formal tools of mathematical complexity theory, and thus can be used as a framework for thinking about general problems in the philosophy of science, including theories, explanation, and lawhood. -/- Methodological questions include questions about how climate science constructs its models, on what basis we trust those models, and how we might improve those models. In order to answer questions about climate modeling, it's important to understand what climate models look like and how they are constructed. Climate model families are significantly more diverse than are the model families of most other sciences (even sciences that study other complex systems). Existing climate models range from basic models that can be solved on paper to staggeringly complicated models that can only be analyzed using the most advanced supercomputers in the world. I introduce some of the central concepts in climatology by demonstrating how one of the most basic climate models might be constructed. I begin with the assumption that the Earth is a simple featureless blackbody which receives energy from the sun and releases it into space, and show how to model that assumption formally. I then gradually add other factors (e.g. albedo and the greenhouse effect) to the model, and show how each addition brings the model's prediction closer to agreement with observation. After constructing this basic model, I describe the so-called "complexity hierarchy" of the rest of climate models, and argue that the sense of "complexity" used in the climate modeling community is related to dynamical complexity. -/- With a clear understanding of the basics of climate modeling in hand, I then argue that foundational issues discussed early in the dissertation suggest that computation plays an irrevocably central role in climate modeling. "Science by simulation" is essential given the complexity of the global climate, but features of the climate system--the presence of non-linearities, feedback loops, and chaotic dynamics--put principled limits on the effectiveness of computational models. This tension is at the root of the staggering pluralism of the climate model hierarchy, and suggests that such pluralism is here to stay, rather than an artifact of our ignorance. Rather than attempting to converge on a single "best fit" climate model, we ought to embrace the diversity of climate models, and view each as a specialized tool designed to predict and explain a rather narrow range of phenomena. Understanding the climate system as a whole requires examining a number of different models, and correlating their outputs. This is the most significant methodological challenge of climatology. -/- Climatology's role contemporary political discourse raises an unusually high number of evaluative questions for a physical science. The two leading approaches to crafting policy surrounding climate change center on mitigation (i.e. stopping the changes from occurring) and adaptation (making post hoc changes to ameliorate the harm caused by those changes). Crafting an effective socio-political response to the threat of anthropogenic climate change, however, requires us to integrate multiple perspectives and values: the proper response will be just as diverse and pluralistic as the climate models themselves, and will incorporate aspects of both approaches. I conclude by offering some concrete recommendations about how to integrate this value pluralism into our socio-political decision making framework. (shrink)

This paper reviews the recent history of a subset of research in dynamical cognitive science, in particular that subset that allies itself with the sciences of complexity and casts cognitive systems as interaction dominant, noncomputational, and nonmodular. I look at this history in the light of C.S. Peirce's understanding of scientific reasoning as progressing from abduction to deduction to induction. In particular, I examine the development of a controversy concerning the use of the interaction dominance of human cognitive systems (...) as an explanation of the ubiquitous 1/f noise, multifractality, and complexity matching in human behavior. (shrink)

Cities serve as dynamic arenas where citizens negotiate needs and co-create solutions to enhance urban life. To address the complexities of contemporary urban living, planning must respond to social justice, economic vitality, and cultural inclusion. This study explores how demographic factors—age, ethnicity, and economics—influence art engagement in two culturally distinct Texan cities: Lubbock and El Paso. Using qualitative comparative analysis and data from a graduate seminar at Texas Tech University, we examine student-led art projects reflecting community experiences. These creative (...) works reveal how art fosters communication, cultural belonging, and social cohesion. Findings underscore that integrating art into urban planning—through public installations, murals, and participatory design—can revitalize underutilized spaces and address diverse community needs. The study argues for art as a tool of creative placemaking, especially in demographically varied cities, emphasizing that culturally responsive engagement strategies can cultivate inclusive, vibrant urban environments. (shrink)

Why should moral philosophers, moral psychologists, and machine ethicists care about computational complexity? Debates on whether artificial intelligence (AI) can or should be used to solve problems in ethical domains have mainly been driven by what AI can or cannot do in terms of human capacities. In this paper, we tackle the problem from the other end by exploring what kind of moral machines are possible based on what computational systems can or cannot do. To do so, we analyze (...) normative ethics through the lens of computational complexity. First, we introduce computational complexity for the uninitiated reader and discuss how the complexity of ethical problems can be framed within Marr’s three levels of analysis. We then study a range of ethical problems based on consequentialism, deontology, and virtue ethics, with the aim of elucidating the complexity associated with the problems themselves (e.g., due to combinatorics, uncertainty, strategic dynamics), the computational methods employed (e.g., probability, logic, learning), and the available resources (e.g., time, knowledge, learning). The results indicate that most problems the normative frameworks pose lead to tractability issues in every category analyzed. Our investigation also provides several insights about the computational nature of normative ethics, including the differences between rule- and outcome-based moral strategies, and the implementation-variance with regard to moral resources. We then discuss the consequences complexity results have for the prospect of moral machines in virtue of the trade-off between optimality and efficiency. Finally, we elucidate how computational complexity can be used to inform both philosophical and cognitive-psychological research on human morality by advancing the moral tractability thesis. (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)

The concept of time has long been the subject of complex philosophical reflections and scientific research, which have interpreted it differently based on the starting question, context, and level of analysis of the system under investigation. In the present review, we first explore how time has been studied among different scientific fields such as physics, neuroscience, and bioecological sciences. We emphasize the fundamental role of an organism's ability to perceive the passage of time and dynamically adapt to its environment for (...) survival. Growth, reproduction, and communication processes are subject to spatiotemporal variability, and the sense of time allows organisms to structure their interactions, track past, and anticipate future events. Specifically, building on a relational and multilevel approach, this paper proposes an analysis of various aspects of the temporal dimension of plants—ranging from their growth and adaptation rates to behavioral strategies and modes of communication—culminating in a focused examination of research based on the kinematical analysis of plant movement. By adopting a comparative and critical approach, we raise several questions about the temporality of processes from different perspectives. Further insights into the timing of physiological and communication processes in plants will help to recognize the central role of temporality in life and to discover mechanisms, processes, and behavioral strategies that may be common (or similar) across species or unique (species-specific) for some organisms, both with and without nervous systems. (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)

The standing tradition in theorizing about meaning, since at least Frege, identifies meaning with propositions, which are, or determine, the truth-conditions of a sentence in a context. But a recent trend has advocated a departure from this tradition: in particular, it has been argued that modal claims do not express standard propositional contents. This non-propositionalism has received different implementations in expressivist semantics and certain kinds of dynamic semantics. They maintain that the key aspect of interpretation of modal claims is (...) the characteristic dynamic effect they have on the context. I argue that pessimism about truth-conditions arises from an overly simplistic picture of content, context and their interaction. While I agree with the critics that an important aspect of modal meaning is the dynamic effect modals have on the context, I argue that they have mischaracterized the nature and the complexity of this effect. A more nuanced account of the interaction between modals and context shows that far from being incompatible with propositional meaning, the dynamic aspect of meaning is precisely what allows us to predict the correct propositional content of an utterance. (shrink)

Unified Informational Dynamics Theory (UInDT) is an information-first framework for describing how ordered physical and biological regularities can arise, persist, and scale without treating “information” as a human label but as an emergent propagative and evolving threshold function. The project proposes a minimal descriptive architecture centered on thresholded instantiation (0>1) and Trace as a state-order signature, then uses that vocabulary to map how constraints, propagation, and multi-scale continuity can be described in one consistent language across domains. -/- UInDT is positioned (...) as a “core ontology + dynamics grammar”: it prioritizes definitions, commitments, and structured descriptive operators over storytelling. The goal is not to declare a finished unified physical theory, but to supply a rigorous scaffold that can be tested, extended, and compared against existing causal, complex-systems, and philosophy-of-science framings. -/- Primary outputs include the canonical preprint/manuscript, release snapshots, and supporting definitions that stabilize terminology for downstream applied branches. "Full UID Master Index available on GitHub; search for repository: armandosotouidt". (shrink)

Why experiential complexity proliferates, diversifies, regresses, and occasionally collapses over evolutionary time remains an unresolved problem for theories of consciousness. Traditional evolutionary accounts either treat consciousness as a directly selected trait, reduce it to cognitive or behavioral function, or dismiss it as an incidental byproduct of intelligence. This paper advances an alternative framework grounded in Structural Resonance Theory (SRT), arguing that experiential complexity emerges as a necessary consequence of selection acting on resonance architectures capable of sustaining integration, stability, (...) and constraint navigation under finite energetic and environmental conditions. -/- Within this framework, evolution is recast as selection over dynamic resonance architectures rather than over representational content, intelligence, or subjective experience itself. Experience is not posited as a target of selection, nor as epiphenomenal noise, but as a structurally contingent outcome that arises when architectures cross integration thresholds defined by spatiotemporal coherence, identity anchoring, and noise tolerance. The paper formalizes the concept of experiential regimes as structural classes characterized by block duration, fingerprint diversity, and identity persistence, providing a non-hierarchical account of experiential variation across species. -/- The analysis introduces resonant selection as a structural analogue to adaptive fitness, explaining convergent and divergent experiential architectures without invoking homology, teleology, or linear scaling with neural complexity. Mutation and noise are reinterpreted as enforced exploration mechanisms required by the incompleteness of constraint satisfaction in finite systems, while regression, fragmentation, and collapse are shown to be expected outcomes under constraint tightening, energetic limitation, and over-specialization. The paper further articulates why experiential expansion exhibits directionality without goals, inevitability without destiny, and open-endedness without convergence toward perfection. -/- Finger III integrates directly with the foundational axioms of Structural Resonance Theory and its prior analyses of experiential blocks and neural instantiation, extending the framework to evolutionary timescales without introducing new ontological commitments. The resulting account positions experiential complexity as historically contingent, structurally constrained, and evolutionarily fragile—neither accidental nor inevitable in outcome, but unavoidable in possibility under sustained exploration. (shrink)

This thesis addresses a fundamental challenge in post-essentialist metaphysics: whether a metaphysical system rooted in fluctuation and uncertainty can sustain coherent ontological categories, or must reject categorization altogether. Through rigorous philosophical analysis, this investigation demonstrates that the apparent tension between ontological instability and categorical coherence dissolves when categorization itself is reconceptualized as a dynamic process rather than a static structure. The thesis develops "Dynamic Categorization" as a revolutionary approach that can work creatively with instability while maintaining systematic effectiveness. (...) Five novel concepts are introduced: Categorical Flows, Fluctuational Taxonomies, Metamorphic Classifications, Assemblage Categories, and Rhizomatic Typologies. These concepts, supported by innovative methodological approaches and visual models, establish that coherent categorization not only remains possible within fluctuational metaphysics but becomes more effective and responsive to contemporary challenges. The thesis concludes that Dynamic Categorization represents a necessary evolution in philosophical thinking that can address the complexity and instability that characterize twenty-first-century reality. (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)

Today decision-makers face surging increases in overall system complexity leading up to more unstable and unpredictable business environments. Leaders and decision-makers are confronted with volatility (dynamic and intense changes), uncertainty (lack of predictability), complexity (interconnection of parts which is sometimes overwhelmingly difficult to process), and ambiguity (unclear relationships), namely the VUCA-world. The implications of the VUCA-world for business and strategy can be applied to the rise of complex cyber-physical systems in Industry 4.0. There is an expressed need (...) to develop complexity management frameworks that integrate different individual measures of dealing with industrial system complexity into a synergetic strategic framework. In this regard new frameworks that reflect “real-life complexity” of industrial systems and their practitioners are being called for. It is therefore the core aim of the thesis to develop and apply a strategic complexity management framework (SCM) that fits in the individual reality of the decision-making practitioner by integrating different complexity dimensions of industrial systems in a holistic, synergetic, and strategic way. As a starting point for achieving this aim, an investigation and exploration of relevant theoretical frameworks is conducted and accumulates in the proposition of a set of hypotheses H1-H13 as an explanatory approach to achieve a multi-dimensional definition of industrial system complexity and to explore its impact on decision-making based on information growth in industrial systems. In a second step, H1-H13 are applied to develop a theoretical complexity space model for industrial systems. In the model the static and dynamic complexity of an industrial system are integrated in a complexity space modelling approach, where information complexity boundaries expand over time in a static compound space of a system and serve as an indicator for system instability in a static complexity space. The capabilities of the complexity space model are theoretically demonstrated, alongside a set of assumptions concerning the behavior of industrial system complexity. The developed complexity space model represents the core theoretical foundation for the establishment of the SCM in a third step. In the third step the complexity of an industrial system is captivated via the strategic complexity management framework (SCM) in the form of a strategic 8- quadrant matrix in adherence to the axioms of the paradigm of strategic complexity engineering which are to acknowledge, characterize, anticipate, and manage complexity. Definitions of static, dynamic and environmental conception of complexity of industrial systems are holistically integrated to capture the internal and external strategic management perspective in the SCM framework and the strategic capabilities of the SCM framework are theoretically demonstrated based on a set of generic norm strategies. In a fourth step the SCM is applied for strategic complexity management purposes to four different real-world cases of industrial manufacturing systems with the goal to test, explore and discuss the practical decision-aiding applicability of the framework via an interventionistic multi-case study based on qualitative document review. The individual results of the SCM application on the four different cases are described and discussed. Key-learnings across cases are identified and discussed as a conclusion. Finally, a research outlook is provided. (shrink)

This paper proposes a new axiomatic system called The Solomonian Geometry, which extends geometry beyond Euclidean and non-Euclidean structures. By intro- ducing the concept of Tenson Time, geometry is redefined as a dynamic resonance between energy and information, expressed through a complex tensor field. Points, lines, and time are treated not as static entities but as living flows of meaning. This theory presents a unified framework between mathematics and metaphysics.

I examine to what extent accounts of mechanisms based on formal interventionist theories of causality can adequately represent biological mechanisms with complex dynamics. Using a differential equation model for a circadian clock mechanism as an example, I first show that there exists an iterative solution that can be interpreted as a structural causal model. Thus, in principle, it is possible to integrate causal difference-making information with dynamical information. However, the differential equation model itself lacks the right modularity properties for a (...) full integration. A formal mechanistic model will therefore have to leave out either noncausal or causal explanatory relations. (shrink)

This paper presents a framework for dynamic systems modeling, integrating maximal entropy principles, minimal state variables, feedback loops, delays, and catalysts. We demonstrate how these mechanisms enable small systems to retain memory, exploit environmental affordances, and achieve stable yet creative growth trajectories. The framework further reveals quantum-like behavior: unobserved systems explore multiple trajectories in parallel, while observed systems are constrained to specific, often more complex, paths. We extend the analysis to reinforcement learning (RL) combined with affective computing, showing that (...) agents achieve maximal learning efficiency when operating in a Hidden/Black Box state, with observation used later for stabilization and control. (shrink)

Chaos theory is an interdisciplinary area of scientific study and branch of mathematics focused on underlying patterns and deterministic laws of dynamical systems that are highly sensitive to initial conditions. While the rules describing chaotic dynamical systems are well-specified and simple, the behaviour of many such systems is remarkably complex and produces output that appears random and for which long-term prediction is limited. The book begins by laying out preliminary material needed to understand the literature on chaos, providing the background (...) that any reader would need to be able to navigate the literature. It goes on to discuss the history of the field, the different definitions of chaos, differences between classical and quantum chaos and the implications of chaos for modelling phenomena and forecasting system behaviour before rounding out with broader implications. (shrink)

Our intuition tells us that there is a general trend in the evolution of nature, a trend towards greater complexity. However, there are several definitions of complexity and hence it is difficult to argue for or against the validity of this intuition. Christoph Adami has recently introduced a novel measure called physical complexity that assigns low complexity to both ordered and random systems and high complexity to those in between. Physical complexity measures the amount (...) of information that an organism stores in its genome about the environment in which it evolves. The theory of physical complexity predicts that evolution increases the amount of ‘knowledge’ an organism accumulates about its niche. It might be fruitful to generalize Adami’s concept of complexity to the entire evolution (including the evolution of man). Physical complexity fits nicely into the philosophical framework of cognitive biology which considers biological evolution as a progressing process of accumulation of knowledge (as a gradual increase of epistemic complexity). According to this paradigm, evolution is a cognitive ‘ratchet’ that pushes the organisms unidirectionally towards higher complexity. Dynamic environment continually creates problems to be solved. To survive in the environment means to solve the problem, and the solution is an embodied knowledge. Cognitive biology (as well as the theory of physical complexity) uses the concepts of information and entropy and views the evolution from both the information-theoretical and thermodynamical perspective. Concerning humans as conscious beings, it seems necessary to postulate an emergence of a new kind of knowledge - a self-aware and self-referential knowledge. Appearence of selfreflection in evolution indicates that the human brain reached a new qualitative level in the epistemic complexity. (shrink)