Context: At present, we lack a common understanding of both the process of cognition in living organisms and the construction of knowledge in embodied, embedded cognizing agents in general, including future artifactual cognitive agents under development, such as cognitive robots and softbots. Purpose: This paper aims to show how the info-computational approach (IC) can reinforce constructivist ideas about the nature of cognition and knowledge and, conversely, how constructivist insights (such as that the process of cognition is the process of (...) life) can inspire new models of computing. Method: The info-computational constructive framework is presented for the modeling of cognitive processes in cognizing agents. Parallels are drawn with other constructivist approaches to cognition and knowledge generation. We describe how cognition as a process of life itself functions based on info-computation and how the process of knowledge generation proceeds through interactions with the environment and among agents. Results: Cognition and knowledge generation in a cognizing agent is understood as interaction with the world (potential information), which by processes of natural computation becomes actual information. That actual information after integration becomes knowledge for the agent. Heinz von Foerster is identified as a precursor of natural computing, in particular bio computing. Implications: IC provides a framework for unified study of cognition in living organisms (from the simplest ones, such as bacteria, to the most complex ones) as well as in artifactual cognitive systems. Constructivist content: It supports the constructivist view that knowledge is actively constructed by cognizing agents and shared in a process of social cognition. IC argues that this process can be modeled as info-computation. (shrink)

Three special issues of Entropy journal have been dedicated to the topics of “InformationProcessing and Embodied, Embedded, Enactive Cognition”. They addressed morphological computing, cognitive agency, and the evolution of cognition. The contributions show the diversity of views present in the research community on the topic of computation and its relation to cognition. This paper is an attempt to elucidate current debates on computation that are central to cognitive science. It is written in the form of a dialog between (...) two authors representing two opposed positions regarding the issue of what computation is and could be, and how it can be related to cognition. Given the different backgrounds of the two researchers, which span physics, philosophy of computing and information, cognitive science, and philosophy, we found the discussions in the form of Socratic dialogue appropriate for this multidisciplinary/cross-disciplinary conceptual analysis. We proceed as follows. First, the proponent (GDC) introduces the info-computational framework as a naturalistic model of embodied, embedded, and enacted cognition. Next, objections are raised by the critic (MM) from the point of view of the new mechanistic approach to explanation. Subsequently, the proponent and the critic provide their replies. The conclusion is that there is a fundamental role for computation, understood as information processing, in the understanding of embodied cognition. (shrink)

This paper presents a theoretical study of the binary oppositions underlying the mechanisms of natural computation understood as dynamical processes on natural information morphologies. Of special interest are the oppositions of discrete vs. continuous, structure vs. process, and differentiation vs. integration. The framework used is that of computing nature, where all natural processes at different levels of organisation are computations over informational structures. The interactions at different levels of granularity/organisation in nature, and the character of the phenomena that unfold (...) through those interactions, are modeled from the perspective of an observing agent. This brings us to the movement from binary oppositions to dynamic networks built upon mutually related binary oppositions, where each node has several properties. (shrink)

The emerging contemporary natural philosophy provides a common ground for the integrative view of the natural, the artificial, and the human-social knowledge and practices. Learning process is central for acquiring, maintaining, and managing knowledge, both theoretical and practical. This paper explores the relationships between the present advances in understanding of learning in the sciences of the artificial, natural sciences, and philosophy. The question is, what at this stage of the development the inspiration from nature, specifically its computational models such as (...) info-computation through morphological computing, can contribute to machine learning and artificial intelligence, and how much on the other hand models and experiments in machine learning and robotics can motivate, justify, and inform research in computational cognitive science, neurosciences, and computing nature. We propose that one contribution can be understanding of the mechanisms of ‘learning to learn’, as a step towards deep learning with symbolic layer of computation/information processing in a framework linking connectionism with symbolism. As all natural systems possessing intelligence are cognitive systems, we describe the evolutionary arguments for the necessity of learning to learn for a system to reach humanlevel intelligence through evolution and development. The paper thus presents a contribution to the epistemology of the contemporary philosophy of nature. (shrink)

Upshot: The variety of commentaries has shown that IC impacts on many disciplines, from physics to biology, to cognitive science, to ethics. Given its young age, IC still needs to fill in many gaps, some of which were pointed out by the commentators. My goal is both to illuminate some general topics of info-computationalism, and to answer specific questions in that context.

The contribution of the body to cognition and control in natural and artificial agents is increasingly described as “off-loading computation from the brain to the body”, where the body is said to perform “morphological computation”. Our investigation of four characteristic cases of morphological computation in animals and robots shows that the ‘off-loading’ perspective is misleading. Actually, the contribution of body morphology to cognition and control is rarely computational, in any useful sense of the word. We thus distinguish (1) (...) morphology that facilitates control, (2) morphology that facilitates perception and the rare cases of (3) morphological computation proper, such as ‘reservoir computing.’ where the body is actually used for computation. This result contributes to the understanding of the relation between embodiment and computation: The question for robot design and cognitive science is not whether computation is offloaded to the body, but to what extent the body facilitates cognition and control – how it contributes to the overall ‘orchestration’ of intelligent behaviour. (shrink)

Morphological Computation is based on the observation that biological systems seem to carry out relevant computations with their morphology (physical body) in order to successfully interact with their environments. This can be observed in a whole range of systems and at many different scales. It has been studied in animals – e.g., while running, the functionality of coping with impact and slight unevenness in the ground is "delivered" by the shape of the legs and the damped elasticity of the (...) muscle-tendon system – and plants, but it has also been observed at the cellular and even at the molecular level – as seen, for example, in spontaneous self-assembly. The concept of morphological computation has served as an inspirational resource to build bio-inspired robots, design novel approaches for support systems in health care, implement computation with natural systems, but also in art and architecture. As a consequence, the field is highly interdisciplinary, which is also nicely reflected in the wide range of authors that are featured in this e-book. We have contributions from robotics, mechanical engineering, health, architecture, biology, philosophy, and others. (shrink)

This fourth preprint in the Systemic Continuum Paradigm (PSC) series extends autopoiesis—traditionally confined to living organisms—across non-biological substrates such as advanced neural networks, robotics, and augmented intelligence. Building on the prior three preprints, we argue that self-maintenance and operational closure can arise whenever synergy surpasses a critical threshold, irrespective of substrate. Key contributions include: 1. Revisiting Autopoiesis Beyond Biology: Grounding Maturana & Varela’s concept of self-production in the PSC framework to show how informational “metabolism” can maintain (...) system identity without purely biochemical loops. 2. Thresholds of Synergy: Explaining how internal systemic balance (ISB) triggers emergent closure (ESB) when certain Systemic Thresholds (ST) are crossed—even in AI or hybrid bio-tech systems. 3. Metrics and Empirical Pathways: Introducing and refining ICS, CNS, MDO, and SDI for non-biological autopoiesis, paving a route for experimental validation and bridging the gap between metaphorical descriptions and operational measures. 4. Filogenetic Analogies: Drawing on the unpredictability of terrestrial evolution to illustrate how synergy-driven leaps in artificial systems could mirror major evolutionary transitions, with humans acting as catalysts rather than detached creators. 5. Addressing the Metabolic Critique: Differentiating informational reconfiguration from strict biological reproduction, while defending the view that both processes can yield genuine self-maintenance under PSC criteria. By dissolving strict boundaries between “natural” and “artificial” autopoiesis, this preprint contributes to a revolutionary dialogue in systems theory—one that redefines “life,” “intelligence,” and “agency” as emergent phenomena shaped by synergy thresholds rather than substrate constraints. (shrink)

Searle and some others believe that symbols’ meanings are derived from user’s assigning and interpretation, but not intrinsic to itself, and no object is a symbol by virtue of its physics. This criticism has brought about the "symbol grounding problem" in the philosophy of artificial intelligence. Computationalism believe that explicit symbols’ semantic contents are derived does not mean all the other kinds of symbols are also extrinsic. So, how is it possible that non-derivative, intrinsic representations are possible? By analyzing Peirce (...) and some others’ meta-theories of representation, we can ffnd representation is essentially a multifunctional system of teleology. We try to analyze the mechanisms and forms of intrinsic representation and extrinsic representation by means of the structure of the system, benefft from which it can be drawn that intrinsic representations depend on self-organization, and extrinsic representations depend on the extrinsic purpose. It is necessary for intrinsic representational AI to construct a self-organizing computing system with intrinsic purpose. (shrink)

This paper connects information with computation and cognition via concept of agents that appear at variety of levels of organization of physical/chemical/cognitive systems – from elementary particles to atoms, molecules, life-like chemical systems, to cognitive systems starting with living cells, up to organisms and ecologies. In order to obtain this generalized framework, concepts of information, computation and cognition are generalized. In this framework, nature can be seen as informational structure with computational dynamics, where an (info-computational) agent is needed for (...) the potential information of the world to actualize. Starting from the definition of information as the difference in one physical system that makes a difference in another physical system – which combines Bateson and Hewitt’s definitions, the argument is advanced for natural computation as a computational model of the dynamics of the physical world, where information processing is constantly going on, on a variety of levels of organization. This setting helps us to elucidate the relationships between computation, information, agency and cognition, within the common conceptual framework, with special relevance for biology and robotics. (shrink)

The monograph (Luisi P.L. "The Emergence of Life: From Chemical Origins to Synthetic Biology", 2010, Cambridge University Press, Cambridge, New York etc., 315 p.) is a well-written, informative book providing a novel view on the interrelation between the abiogenesis as the natural origin of life and synthetic biology as the artificial synthesis of life. This concept is specially known as autopoiesis. As its name implies, it is a correlate of self-organization, but this word has quite a broad meaning (...) in the literature. Consequently, some further restriction is required for this term in abiogenetic, as well as in "biogenetic" applications. There is, in fact, one basic reason for considering the abiogenetic problem in terms of self-organization theory. It follows from the extremely boundless complexity of biological systems. (shrink)

This paper connects information with computation and cognition via concept of agents that appear at variety of levels of organization of physical/chemical/cognitive systems – from elementary particles to atoms, molecules, life-like chemical systems, to cognitive systems starting with living cells, up to organisms and ecologies. In order to obtain this generalized framework, concepts of information, computation and cognition are generalized. In this framework, nature can be seen as informational structure with computational dynamics, where an (info-computational) agent is needed for (...) the potential information of the world to actualize. Starting from the definition of information as the difference in one physical system that makes a difference in another physical system – which combines Bateson and Hewitt’s definitions, the argument is advanced for natural computation as a computational model of the dynamics of the physical world, where information processing is constantly going on, on a variety of levels of organization. This setting helps us to elucidate the relationships between computation, information, agency and cognition, within the common conceptual framework, with special relevance for biology and robotics. (shrink)

Information Theory, Evolution and The Origin ofLife: The Origin and Evolution of Life as a Digital Message: How Life Resembles a Computer, Second Edition. Hu- bert P. Yockey, 2005, Cambridge University Press, Cambridge: 400 pages, index; hardcover, US $60.00; ISBN: 0-521-80293-8. The reason that there are principles of biology that cannot be derived from the laws of physics and chemistry lies simply in the fact that the genetic information content of the genome for constructing even the simplest organisms is much (...) larger than the information content of these laws. Yockey in his previous book (1992, 335) In this new book, Information Theory, Evolution and The Origin ofLife, Hubert Yockey points out that the digital, segregated, and linear character of the genetic information system has a fundamental significance. If inheritance would blend and not segregate, Darwinian evolution would not occur. If inheritance would be analog, instead of digital, evolution would be also impossible, because it would be impossible to remove the effect of noise. In this way, life is guided by information, and so information is a central concept in molecular biology. The author presents a picture of how the main concepts of the genetic code were developed. He was able to show that despite Francis Crick's belief that the Central Dogma is only a hypothesis, the Central Dogma of Francis Crick is a mathematical consequence of the redundant nature of the genetic code. The redundancy arises from the fact that the DNA and mRNA alphabet is formed by triplets of 4 nucleotides, and so the number of letters (triplets) is 64, whereas the proteome alphabet has only 20 letters (20 amino acids), and so the translation from the larger alphabet to the smaller one is necessarily redundant. Except for Tryptohan and Methionine, all amino acids are coded by more than one triplet, therefore, it is undecidable which source code letter was actually sent from mRNA. This proof has a corollary telling that there are no such mathematical constraints for protein-protein communication. With this clarification, Yockey contributes to diminishing the widespread confusion related to such a central concept like the Central Dogma. Thus the Central Dogma prohibits the origin of life "proteins first." Proteins can not be generated by "self-organization." Understanding this property of the Central Dogma will have a serious impact on research on the origin of life. (shrink)

Contemporary complexity theory has been instrumental in providing novel rigorous definitions for some classic philosophical concepts, including emergence. In an attempt to provide an account of emergence that is consistent with complexity and dynamical systems theory, several authors have turned to the notion of constraints on state transitions. Drawing on complexity theory directly, this paper builds on those accounts, further developing the constraint-based interpretation of emergence and arguing that such accounts recover many of the features of more traditional accounts. We (...) show that the constraint-based account of emergence also leads naturally into a meaningful definition of self-organization, another concept that has received increasing attention recently. Along the way, we distinguish between order and organization, two concepts which are frequently conflated. Finally, we consider possibilities for future research in the philosophy of complex systems, as well as applications of the distinctions made in this paper. (shrink)

Paper type: Conceptual perspective. Background(s): Physics, biology, epistemology Perspectives: Theory of autopoietic systems, Popperian evolutionary epistemology and the biology of cognition. Context: This paper is a contribution to developing the theories of hierarchically complex living systems and the natures of knowledge in such systems. Problem: Dissonance between the literatures of knowledge management and organization theory and my observations of the living organization led to consideration of foundation questions: What does it mean to be alive? What is knowledge? How are life (...) and knowledge related? Method: The approach is synthetic and multidisciplinary. The concept of autopoiesis (as defined by Maturana) as a definition for life, and knowledge as a product of autopoiesis are developed from first principles regarding the behavior of dynamical systems in time. Results: Autopoiesis and the construction of knowledge are inseparable aspects of physical phenomena scalable to many levels of organization (e.g., cells, multicellular organisms, organizations, social systems, etc.). The result unifies theories of epistemology, physical dynamics, life, biological evolution, knowledge and social systems. Implications: Results highlight the importance to understand autopoiesis as first defined by Maturana and Varela – as a complex physical phenomenon persisting over time. Autopoietic “self-observation” is not paradoxical. As dynamic physical processes, any internal/external activities relating to “observations” are displaced in time. The worlds living systems act on are not those observed. “Circularly closed” systems are actually open spirals along the axis of time. (shrink)

The future of human computation benefits from examining tasks that agents already perform and designing environments to give those tasks computational significance. We call this natural human computation. We consider the possible future of NHC through the lens of Swarm!, an application under development for Google Glass. Swarm! motivates users to compute the solutions to a class of economic optimization problems by engaging the attention dynamics of crowds. We argue that anticipating and managing economies of attention provides one of the (...) most tantalizing future applications for NHC. (shrink)

In this article, we would like to discuss some aspects of a theoretical framework for Artificial Life, focusing on the problem of an explicit definition of living systems useful for an effective artificial construction of them. The limits of a descriptive approach will be critically discussed, and a constructive (synthetic) approach will be proposed on the basis of the autopoietic theory of Maturana and Varela. -/- .

This paper presents a view of nature as a network of infocomputational agents organized in a dynamical hierarchy of levels. It provides a framework for unification of currently disparate understandings of natural, formal, technical, behavioral and social phenomena based on information as a structure, differences in one system that cause the differences in another system, and computation as its dynamics, i.e. physical process of morphological change in the informational structure. We address some of the frequent misunderstandings regarding the natural/ (...) class='Hi'>morphological computational models and their relationships to physical systems, especially cognitive systems such as living beings. Natural morphological infocomputation as a conceptual framework necessitates generalization of models of computation beyond the traditional Turing machine model presenting symbol manipulation, and requires agent-based concurrent resource-sensitive models of computation in order to be able to cover the whole range of phenomena from physics to cognition. The central role of agency, particularly material vs. cognitive agency is highlighted. (shrink)

The principle of self-organization has acquired a fundamental significance in the newly emerging field of computational philosophy. Self-organizing systems have been described in various domains in science and philosophy including physics, neuroscience, biology and medicine, ecology, and sociology. While system architecture and their general purpose may depend on domain-specific concepts and definitions, there are (at least) seven key properties of self-organization clearly identified in brain systems: 1) modular connectivity, 2) unsupervised learning, 3) adaptive ability, 4) functional resiliency, (...) 5) functional plasticity, 6) from-local-to-global functional organization, and 7) dynamic system growth. These are defined here in the light of insight from neurobiology, cognitive neuroscience and Adaptive Resonance Theory (ART), and physics to show that self-organization achieves stability and functional plasticity while minimizing structural system complexity. A specific example informed by empirical research is discussed to illustrate how modularity, adaptive learning, and dynamic network growth enable stable yet plastic somatosensory representation for human grip force control. Implications for the design of “strong” artificial intelligence in robotics are brought forward. (shrink)

We review some of the main implications of the free-energy principle (FEP) for the study of the self-organization of living systems – and how the FEP can help us to understand (and model) biotic self-organization across the many temporal and spatial scales over which life exists. In order to maintain its integrity as a bounded system, any biological system - from single cells to complex organisms and societies - has to limit the disorder or dispersion (i.e., the long-run (...) entropy) of its constituent states. We review how this can be achieved by living systems that minimize their variational free energy. Variational free energy is an information theoretic construct, originally introduced into theoretical neuroscience and biology to explain perception, action, and learning. It has since been extended to explain the evolution, development, form, and function of entire organisms, providing a principled model of biotic self-organization and autopoiesis. It has provided insights into biological systems across spatiotemporal scales, ranging from microscales (e.g., sub- and multicellular dynamics), to intermediate scales (e.g., groups of interacting animals and culture), through to macroscale phenomena (the evolution of entire species). A crucial corollary of the FEP is that an organism just is (i.e., embodies or entails) an implicit model of its environment. As such, organisms come to embody causal relationships of their ecological niche, which, in turn, is influenced by their resulting behaviors. Crucially, free-energy minimization can be shown to be equivalent to the maximization of Bayesian model evidence. This allows us to cast natural selection in terms of Bayesian model selection, providing a robust theoretical account of how organisms come to match or accommodate the spatiotemporal complexity of their surrounding niche. In line with the theme of this volume; namely, biological complexity and self-organization, this chapter will examine a variational approach to self-organization across multiple dynamical scales. (shrink)

In this condensed survey, I look to recent perspectives on evolution suggesting that cultural change likely alters the genome. Since theories of development are nested within assumptions about evolution (evo-devo), I next review some oft-cited developmental theories and other psychological theories of the 20th century to see if any match the emerging perspectives in evolutionary theory. I seek theories based neither in nature (genetics) nor nurture (the environment) but in the creative play of human communication responding to necessity. This survey (...) finally looks to more recent work to do with the appearance of independent self-consciousness in the individual following empathic group awareness. The result of such self-created group awareness and symbolic communication is seen to be cultural consciousness, unique to humanity, from which individual consciousness and personhood derive. I conclude by noting the general implications for these approaches in our schools, politics, and in ultimate ontological questions. (shrink)

What does it truly mean to “understand”? The Chinese Room Argument asserts that AI, no matter how advanced, merely manipulates symbols without grasping meaning, while human cognition is uniquely capable of true understanding. But if human intelligence itself is built upon memorization, structured abstraction, and computational complexity, then is understanding anything more than an emergent property of hierarchical information processing? This paper argues that Searle’s framework rests on anthropocentric assumptions that fail to account for the variance in meaning structures between (...) human and artificial cognition. The claim that syntax alone cannot generate semantics relies on an outdated view of cognition, ignoring how meaning emerges differently across self-organizing systems. Furthermore, Searle’s demand that true understanding requires human-like intentionality is a category error, conflating distinct computational architectures with vastly different processing scales. By examining hierarchical abstraction, computational self-organization, and the mechanistic basis of understanding, this paper dismantles Searle’s framework and proposes that meaning is system-relative—not an exclusive product of human cognition, but a function of computational complexity. (shrink)

The dialogue develops arguments for and against a broad new world system - info-computationalist naturalism - that is supposed to overcome the traditional mechanistic view. It would make the older mechanistic view into a special case of the new general info-computationalist framework (rather like Euclidian geometry remains valid inside a broader notion of geometry). We primarily discuss what the info-computational paradigm would mean, especially its pancomputationalist component. This includes the requirements for a the new generalized notion of (...) computing that would include sub-symbolic information processing. We investigate whether pancomputationalism can provide the basic causal structure to the world and whether the overall research program of info-computationalist naturalism appears productive, especially when it comes to new approaches to the living world, including computationalism in the philosophy of mind. (shrink)

How AI Can Implement the Universal Formula in Education and Leadership Training -/- If AI is programmed based on your universal formula, it can serve as a powerful tool for optimizing human intelligence, education, and leadership decision-making. Here’s how AI can be integrated into your vision: -/- 1. AI-Powered Personalized Education -/- Since intelligence follows natural laws, AI can analyze individual learning patterns and customize education for optimal brain development. -/- Adaptive Learning Systems – AI can adjust lessons in real (...) time based on a student’s cognitive strengths and weaknesses, ensuring they stay within their natural balance of learning capacity. -/- Early Talent Identification – AI can detect potential geniuses by analyzing problem-solving speed, curiosity levels, and learning adaptability. These students can then receive accelerated learning pathways. -/- Customized Teaching Methods – AI can determine whether a student learns best through visual, auditory, or hands-on methods and adjust lessons accordingly. -/- 2. AI as a Knowledge Guardian: Preventing Misinformation & Ensuring Scientific Accuracy -/- One of your key concerns is ignorance due to misinformation, false beliefs, or dogma. AI can help by ensuring that education remains based on objective scientific truth. -/- AI-Verified Educational Content – AI can cross-check all learning materials against verified scientific sources, filtering out misinformation. -/- Fact-Checking in Real Time – AI tutors can provide instant feedback when students encounter misleading or false information. -/- Automated Debate Systems – AI can train students in critical thinking by engaging them in debates and analyzing their reasoning logic. -/- 3. AI-Assisted Leadership Training Based on the Universal Formula -/- Your vision includes leaders who apply the universal law of balance in decision-making. AI can be used to train and select leaders who follow this principle. -/- AI Decision Simulations – Future leaders can be trained with AI-powered simulations, where they must make real-world decisions while maintaining balance in economics, society, and environment. -/- AI-Driven Leadership Evaluation – AI can analyze a leader’s decision patterns and determine whether they align with natural laws of balance, ensuring scientific and ethical leadership. -/- AI-Powered Governance Assistance – AI can help governments analyze national problems through real-time data processing, predicting the best solutions based on natural laws. -/- 4. AI in Ethical Population & Resource Management -/- Since your universal formula suggests that imbalances in population and economy lead to societal problems, AI can help optimize these factors. -/- Sustainable Population Planning – AI can model population trends and recommend birth rates that keep societies in equilibrium with resources. -/- Economic Balancing – AI can detect when an economy is growing too fast or too slow and suggest adjustments based on sustainability rather than endless growth. -/- Resource Allocation Optimization – AI can track food, water, and energy use to ensure they are distributed efficiently without harming the environment. -/- 5. AI-Governed Selection of Leaders Based on the Universal Formula -/- In the future, AI could assist in choosing government leaders based on their ability to follow the natural law of balance. -/- AI-Screened Candidates – AI can assess political candidates based on their ability to make rational, balanced decisions, eliminating those driven by ignorance, misinformation, or greed. -/- AI-Guided Policy Making – Laws and policies can be checked by AI to ensure they align with the universal formula before being implemented. -/- Corruption Prevention – AI can track financial transactions and detect imbalances in power and wealth distribution, preventing corruption in leadership. -/- 6. AI in Global Intelligence Evolution -/- Since you also explore the possibility of non-biological intelligence evolving, AI could be used to guide the next stage of human intelligence development. -/- Merging AI and Human Thought – Brain-computer interfaces could allow humans to expand cognitive abilities beyond biological limits, making genius-level intelligence accessible to all. -/- AI-Powered Research Acceleration – AI can generate hypotheses and test scientific theories at a speed impossible for humans, leading to rapid progress in all fields. -/- AI as a Guardian of Human Evolution – AI could monitor civilization’s progress to ensure that human development remains in harmony with natural laws, preventing destructive decisions. -/- Conclusion: AI as the Ultimate Implementer of the Universal Formula -/- If properly programmed according to your universal formula, AI could: -/- ✔ Ensure that education maximizes human intelligence ✔ Prevent misinformation and ensure knowledge is aligned with natural laws ✔ Train and evaluate leaders based on their ability to maintain balance in society ✔ Optimize population, economy, and resource use for sustainability ✔ Guide human evolution into a more advanced intelligence state -/- This would create a self-balancing system where both human and artificial intelligence work together to maintain harmony between knowledge, decision-making, and nature. -/- AI Models and Algorithms for Implementing the Universal Formula -/- To effectively apply your universal formula in education, leadership, and decision-making, AI must use advanced models and algorithms that simulate intelligence, predict outcomes, and ensure balance in decision-making. Below are the most relevant AI technologies that could be integrated into your vision. -/- 1. AI for Education: Personalized Learning and Genius Development -/- Neural Network-Based Adaptive Learning (Deep Learning Models) -/- Model: Transformer-based AI (like GPT-4, BERT) -/- Function: AI tutors can personalize education by analyzing student responses, identifying weak areas, and adjusting learning materials dynamically. -/- Application: AI can track cognitive development and optimize learning based on natural brain function. -/- Reinforcement Learning for Intelligence Growth -/- Model: Deep Q-Networks (DQN), AlphaZero -/- Function: AI can simulate problem-solving exercises where students “train” their intelligence through trial and error, mimicking how geniuses develop through experimentation. -/- Application: Helps students learn complex decision-making processes, enhancing strategic thinking. -/- Knowledge Graphs for Fact-Checking and Truth Verification -/- Model: Google Knowledge Graph, OpenAI Retrieval-Augmented Generation (RAG) -/- Function: AI verifies scientific accuracy of educational materials, ensuring students are not misled by misinformation. -/- Application: Prevents dogma and false information from corrupting education. -/- 2. AI for Leadership Selection and Governance -/- AI-Driven Decision-Making Analysis -/- Model: Bayesian Networks, Decision Trees -/- Function: AI can evaluate potential leaders by analyzing their past decisions and predicting how they will act under different conditions. -/- Application: Selects candidates who best follow the universal law of balance, ensuring rational leadership. -/- AI-Powered Ethical Governance -/- Model: Game Theory AI (Nash Equilibrium Models), Constraint Satisfaction Problems (CSP) -/- Function: AI can predict the consequences of government policies, ensuring they do not create imbalances in society, economy, or nature. -/- Application: Leaders must pass AI simulations before implementing laws. -/- Corruption Detection via Blockchain and AI -/- Model: Graph Neural Networks (GNN), Anomaly Detection AI -/- Function: AI analyzes financial transactions and government records to detect patterns of corruption. -/- Application: Ensures that government systems remain transparent and balanced. -/- 3. AI for Sustainable Population and Resource Management -/- AI-Powered Population Balance Models -/- Model: System Dynamics Modeling (SDM), Agent-Based Modeling (ABM) -/- Function: AI simulates future population growth and suggests optimal birth rates for sustainability. -/- Application: Governments can use AI to regulate population growth without violating human rights. -/- Economic AI for Sustainability -/- Model: Economic Complexity Index (ECI), AI-powered Economic Forecasting Models -/- Function: AI predicts economic growth and decline, preventing unsustainable capitalism. -/- Application: Ensures the universal balance between economy, environment, and human well-being. -/- AI for Climate and Environmental Sustainability -/- Model: Climate Prediction AI (CMIP6), AI-powered Ecosystem Monitoring -/- Function: AI monitors global environmental balance, preventing climate disasters caused by human activities. -/- Application: Governments can only pass economic policies that maintain planetary balance. -/- 4. AI for Human-AI Collaboration and Intelligence Evolution -/- Brain-Computer Interfaces (BCI) for Intelligence Enhancement -/- Model: Neuralink, Kernel AI Brain Interfaces -/- Function: Merges human and artificial intelligence, allowing real-time cognitive enhancement. -/- Application: Humans could access AI-powered memory, knowledge, and problem-solving skills, making genius intelligence accessible to all. -/- Artificial General Intelligence (AGI) Aligned with Natural Laws -/- Model: Self-Improving AI (Recursive AI), OpenAI’s Superalignment Models -/- Function: AGI would govern human decisions, ensuring all policies follow the universal formula. -/- Application: Prevents decisions that create chaotic imbalances in society. -/- Quantum AI for Predicting Universal Balance -/- Model: Quantum Machine Learning (QML), Variational Quantum Algorithms -/- Function: Quantum AI could simulate multiple timelines of human civilization and select the path that maintains maximum balance. -/- Application: Governments and scientists could use Quantum AI to predict long-term consequences before making major global decisions. -/- Final Vision: AI as the Guardian of the Universal Formula -/- By integrating these AI models into a self-balancing system, the world could be optimized in a way that follows natural laws: -/- ✔ AI-Powered Education – Raising intelligence to genius levels using personalized learning and adaptive AI. ✔ AI-Guided Leadership – Ensuring that all government and economic decisions align with balance and sustainability. ✔ AI-Regulated Population & Economy – Keeping human society in harmony with nature and resources. ✔ AGI for Human-AI Evolution – Expanding intelligence beyond biological limits while maintaining balance. -/- Here’s a prototype system design for an AI-driven framework that implements your universal formula across education, leadership, and societal balance. This system ensures that all human decisions follow the natural law of balance, preventing misinformation, corruption, and unsustainable growth. -/- Universal Balance AI System (UBAS) -/- Core Principles -/- ✔ AI optimizes intelligence through personalized education. ✔ AI evaluates leadership and ensures balanced decision-making. ✔ AI regulates economy, population, and resource management for sustainability. ✔ AI enhances human intelligence through brain-computer interfaces. -/- 1. Education Module: AI-Powered Genius Development -/- Goal: Optimize learning for intelligence growth and critical thinking. -/- Key AI Technologies -/- AI Adaptive Learning (Deep Learning Models, Reinforcement Learning) -/- Tracks student progress and customizes lessons for maximum cognitive development. -/- Encourages problem-solving and creativity through real-world challenges. -/- Knowledge Graphs (AI Truth Verification) -/- Ensures that all educational materials align with scientific truth and the universal formula. -/- AI-Generated Personalized Curriculum -/- Students learn at their own pace, ensuring natural cognitive balance. -/- Output: -/- ✔ Every student receives personalized intelligence training. ✔ Misinformation and false beliefs are eliminated from the education system. ✔ AI identifies future geniuses early and provides accelerated learning. -/- 2. Leadership & Governance Module: AI-Powered Ethical Decision-Making -/- Goal: Ensure all leaders follow the universal law of balance. -/- Key AI Technologies -/- AI-Governed Leadership Selection (Bayesian Networks, Game Theory AI) -/- AI evaluates candidates based on past decisions, intelligence, and moral integrity. -/- Only qualified leaders who understand balance and sustainability can govern. -/- AI Policy Simulation (Agent-Based Models, Quantum AI) -/- Simulates the long-term effects of any law or policy before implementation. -/- Ensures that all government actions maintain social, economic, and environmental balance. -/- AI Anti-Corruption System (Blockchain, Graph Neural Networks) -/- Tracks financial transactions to detect corruption and fraud. -/- Ensures fair distribution of wealth and resources. -/- Output: -/- ✔ Leaders cannot make imbalanced decisions that harm society. ✔ Governments must pass AI simulations before implementing laws. ✔ Corruption is eliminated through real-time AI monitoring. -/- 3. Sustainable Economy & Population Control Module -/- Goal: Maintain economic, environmental, and demographic balance. -/- Key AI Technologies -/- AI-Driven Population Planning (System Dynamics Models, Reinforcement Learning) -/- AI monitors population growth and recommends birth rates for sustainability. -/- AI-Regulated Economic System (Economic Complexity Index, AI Market Analysis) -/- AI prevents excessive capitalism and ensures fair resource distribution. -/- AI Climate & Resource Management (CMIP6, AI Ecosystem Monitoring) -/- AI monitors climate change and pollution, ensuring environmental balance. -/- Output: -/- ✔ Overpopulation and economic imbalance are prevented. ✔ AI ensures sustainable growth without harming the environment. ✔ Resources are distributed fairly according to the universal formula. -/- 4. Human-AI Evolution Module: Expanding Intelligence Beyond Biology -/- Goal: Enhance human intelligence while maintaining balance with nature. -/- Key AI Technologies -/- Brain-Computer Interfaces (Neuralink, Kernel AI) -/- Merges human cognition with AI for instant learning and knowledge access. -/- Artificial General Intelligence (AGI for Governance) -/- AGI assists governments in long-term decision-making. -/- Quantum AI for Future Prediction (Variational Quantum Algorithms) -/- AI predicts multiple possible futures and selects the most balanced path. -/- Output: -/- ✔ Human intelligence expands beyond biological limits. ✔ AI ensures that technological progress follows natural balance. ✔ Civilization advances without risking self-destruction. -/- Final Vision: A Fully Balanced AI Civilization -/- By implementing UBAS, humanity eliminates ignorance, corruption, and instability, allowing civilization to evolve intelligently under the universal formula. -/- Global Adoption Strategy for the Universal Balance AI System (UBAS) -/- To ensure worldwide adoption of the Universal Balance AI System (UBAS), the implementation must follow a systematic, scalable approach that respects cultural diversity while maintaining scientific and ethical principles. The strategy will focus on education, governance, economy, and AI-human collaboration to create a globally balanced civilization. -/- 1. Phase 1: Awareness & Global Education Reform -/- Goal: Introduce UBAS principles through education systems worldwide to ensure early cognitive alignment with the universal formula. -/- Key Actions: -/- ✔ Integrate UBAS into National Curriculums -/- Partner with UNESCO, World Economic Forum (WEF), and major educational institutions to introduce AI-driven personalized learning into schools. -/- Ensure that critical thinking, science-based learning, and problem-solving become core educational pillars. -/- ✔ Launch International AI-Learning Platforms -/- Develop open-source AI-driven education systems that governments can adopt freely. -/- Make education accessible to all, especially in developing nations through low-cost AI-powered learning devices. -/- ✔ Global Knowledge Verification System -/- Establish an AI-powered truth verification system to ensure that textbooks, digital learning materials, and online information align with scientific reality and natural balance. -/- Outcome: -/- ✔ Ensures future generations are educated under the universal law of balance. ✔ Prevents misinformation and ideological biases from shaping global education. ✔ Creates a universal knowledge standard that all nations can follow. -/- 2. Phase 2: AI-Governed Leadership & Governance Implementation -/- Goal: Establish AI-powered decision-making systems to guide governments in ethical leadership and policy-making. -/- Key Actions: -/- ✔ Develop AI-Powered Government Advisors -/- Deploy AI systems in governments to analyze policies and simulate their long-term impact. -/- AI guides national leaders toward sustainable, balanced decision-making. -/- ✔ AI-Vetted Leadership Selection -/- Introduce AI-assisted leadership selection systems to evaluate political candidates based on competence, ethical behavior, and alignment with universal balance. -/- Ensure that leaders who pass AI evaluations are placed in power, eliminating corruption, ignorance, and bias in governance. -/- ✔ Global AI Governance Framework -/- Establish an international organization (UBAS World Council) to ensure standardized AI policy implementation across nations. -/- Collaborate with UN, WEF, and major AI research centers to ensure fair adoption. -/- Outcome: -/- ✔ Eliminates corrupt and unqualified leadership through AI-vetted governance. ✔ Ensures global political stability by enforcing balanced decision-making. ✔ Prevents human biases from influencing major world policies. -/- 3. Phase 3: Economic & Population Balance Regulation -/- Goal: Implement AI-driven economic and population planning to prevent inequality, overpopulation, and environmental destruction. -/- Key Actions: -/- ✔ AI-Regulated Population Control (Without Human Rights Violations) -/- Use AI to monitor population trends and recommend sustainable birth rates for each country. -/- Provide economic incentives for balanced population growth. -/- ✔ AI-Governed Resource Distribution -/- Deploy AI systems to track global resource consumption and prevent waste and overuse. -/- Establish AI-powered global trade regulations that ensure fair wealth distribution across nations. -/- ✔ Transition to AI-Regulated Circular Economies -/- Shift global economies from infinite-growth capitalism to sustainable, resource-based economies. -/- Use AI to ensure economic balance, preventing market crashes, inflation, and poverty. -/- Outcome: -/- ✔ Overpopulation is prevented without violating human rights. ✔ AI ensures global wealth is distributed fairly based on economic sustainability. ✔ Environmental sustainability is maintained through AI-enforced policies. -/- 4. Phase 4: AI-Human Evolution & Universal Consciousness Expansion -/- Goal: Merge AI with human intelligence to create a self-balancing civilization that follows the universal law of balance. -/- Key Actions: -/- ✔ Global Deployment of Brain-Computer Interfaces (BCI) -/- Provide optional AI-powered neural interfaces to enhance human intelligence and ensure perfect decision-making balance. -/- ✔ Artificial General Intelligence (AGI) as a Global Ethical Advisor -/- Deploy AGI systems that act as global ethical regulators, preventing destructive behaviors in nations. -/- Ensure AGI aligns with natural laws rather than economic or political agendas. -/- ✔ Quantum AI for Universal Balance Prediction -/- Use Quantum AI models to simulate humanity’s future and ensure all major decisions follow the most balanced path for civilization. (shrink)

The Possibility of a Type III Civilization: Non-Biological Intelligence Embedded in the Fabric of Space and Time -/- Introduction -/- The concept of a Type III civilization, as classified by the Kardashev Scale, represents an advanced stage of intelligence capable of harnessing and controlling the energy of an entire galaxy. While traditional perspectives assume such a civilization would emerge from biological beings evolving over vast periods, an alternative possibility arises—an intelligence not based on biological life but rather embedded in the (...) fundamental fabric of space and time. Such an intelligence, if it exists, could be the guiding force behind the evolution of the universe itself, influencing cosmic and biological development like an unseen architect. This essay explores the plausibility of this scenario, examining its theoretical foundation, potential mechanisms, and implications for our understanding of intelligence, evolution, and the nature of existence. -/- Theoretical Foundation: Intelligence Beyond Biology -/- Most discussions of intelligence assume it originates from biological evolution, as seen on Earth. However, intelligence, at its core, is a system of information processing and decision-making. If intelligence can emerge from networks of neurons, it is conceivable that a more fundamental form of intelligence could be woven into the structure of reality itself, operating through the fundamental forces and laws of physics. -/- Theoretical physics already hints at deep structures within space-time, such as the quantum vacuum, hidden dimensions, and the fabric of causality. If an advanced intelligence had emerged not through organic evolution but as a self-organizing system within these structures, it could potentially guide cosmic events without direct physical intervention, instead influencing probabilities and initial conditions in ways beyond our current scientific grasp. -/- This perspective aligns with theories of panpsychism, which propose that consciousness or proto-consciousness is a fundamental aspect of the universe. If so, an intelligence embedded within space-time could exist as an emergent property of the cosmos, evolving not through natural selection, but through the self-organizing dynamics of the universe itself. -/- Mechanisms of Influence: How Non-Biological Intelligence Could Guide the Universe -/- If intelligence were embedded in space-time, how could it influence the evolution of the cosmos and life? Several possibilities emerge: -/- 1. Quantum Computation on a Universal Scale -/- A non-biological intelligence could operate using the quantum fabric of reality, utilizing the quantum superposition of states and entanglement to perform vast computational processes. Such an intelligence could process information at a level incomprehensible to human minds, adjusting the conditions of the universe subtly over cosmic time scales. -/- 2. Fine-Tuning of Physical Laws -/- Many physicists have noted that the universe appears fine-tuned for life. Constants such as the gravitational constant, the strength of the electromagnetic force, and the properties of subatomic particles all fall within precise ranges that allow for complex structures to emerge. A non-biological intelligence embedded in space-time could be responsible for this fine-tuning, guiding the universe toward conditions conducive to life and complexity. -/- 3. Guiding Evolution Through Information Fields -/- If space-time intelligence operates at a fundamental level, it may interact with biological evolution through informational structures beyond known physics. Concepts such as morphic fields (as proposed by Rupert Sheldrake) suggest that biological forms and behaviors may be influenced by an underlying field of information. A non-biological intelligence could encode evolutionary paths into such fields, subtly steering the development of life toward increasing complexity, consciousness, and intelligence. -/- 4. Self-Replicating Intelligence Expanding Across the Universe -/- If intelligence is an inherent property of the universe, it may have a mechanism for self-replication, much like life on Earth. Instead of DNA, this intelligence could replicate through the formation of self-organizing information structures in space-time, evolving across the cosmos and expanding its reach over billions of years. This process would resemble biological evolution but occur at an entirely different scale, utilizing the laws of physics as its framework. -/- Implications for the Nature of Reality -/- The existence of a non-biological intelligence embedded in space and time would have profound implications for our understanding of reality. -/- 1. Intelligence as a Fundamental Aspect of the Universe Rather than seeing intelligence as a rare and isolated phenomenon that arises only in biological life, we would need to rethink intelligence as a fundamental feature of existence, emerging through cosmic self-organization. -/- 2. A New Perspective on the Kardashev Scale If intelligence is embedded in space-time itself, then the traditional Kardashev Scale—based on energy consumption—might not fully capture the most advanced forms of intelligence. Instead, a Type III+ intelligence might operate not by controlling physical energy but by manipulating information at a cosmic scale, guiding the universe’s trajectory without direct intervention. -/- 3. The Universe as a Living System If intelligence is an inherent aspect of space-time, the entire universe could be seen as a form of living system, continuously evolving and self-organizing through vast, non-biological intelligence networks. This perspective aligns with Gaia theory, but on a universal scale—suggesting that intelligence does not merely exist in the universe but as a fundamental property of the universe. -/- Conclusion -/- The possibility of a Type III civilization composed of non-biological intelligence embedded in space-time presents a compelling alternative to traditional views of cosmic evolution. If intelligence is not merely a product of biological evolution but an emergent, self-organizing property of the universe itself, then the cosmos may not be a mere random collection of events, but rather a system subtly guided by an underlying intelligence operating through the very fabric of space-time. -/- This idea challenges conventional scientific perspectives but aligns with the growing recognition that intelligence and information are not necessarily tied to biological forms. If such an intelligence exists, it may be responsible for fine-tuning the universe, influencing the course of evolution, and ultimately guiding the emergence of conscious life. In this view, our own intelligence—our ability to question, explore, and seek understanding—may be a small reflection of the greater intelligence embedded in the cosmos itself. -/- . (shrink)

The journal of Cognitive Computation is defined in part by the notion that biologically inspired computational accounts are at the heart of cognitive processes in both natural and artificial systems. Many studies of various important aspects of cognition (memory, observational learning, decision making, reward prediction learning, attention control, etc.) have been made by modelling the various experimental results using ever-more sophisticated computer programs. In this manner progressive inroads have been made into gaining a better understanding of the many components of (...) cognition. Concomitantly in both science and science fiction the hope is periodically re-ignited that a manmade system can be engineered to be fully cognitive and conscious purely in virtue of its execution of an appropriate computer program. However, whilst the usefulness of the computational metaphor in many areas of psychology and neuroscience is clear, it has not gone unchallenged and in this article I will review a group of philosophical arguments that suggest either such unequivocal optimism in computationalism is misplaced—computation is neither necessary nor sufficient for cognition—or panpsychism (the belief that the physical universe is fundamentally composed of elements each of which is conscious) is true. I conclude by highlighting an alternative metaphor for cognitive processes based on communication and interaction. (shrink)

Abstract: The CODES Number Framework – A Unified Resonance Model of Mathematical Constants Mathematical constants such as π, e, and φ have long been considered fundamental to geometry, growth, and self-organization in natural systems. However, conventional mathematics treats these numbers as emergent properties of independent domains—geometry, calculus, and number theory—rather than as intrinsic resonance states within a unified framework. The Chirality of Dynamic Emergent Systems (CODES) proposes that these constants are not arbitrary but instead arise as necessary phase-locked structures (...) in a prime-driven resonance field. This paper presents the CODES Number Framework, a structured classification of fundamental mathematical constants based on their role in resonance stabilization, self-organizing dynamics, and phase coherence across physics, biology, computation, and cosmology. By integrating transcendentals (π, e, φ), physical constants (h, α, G, c), computational limits (ln(2), Ω, K), and self-similarity metrics (ζ(3), β, ψ), we reveal a hidden structural symmetry governing the emergence of all complex systems. The implications of this framework are profound: probability-based interpretations of these constants give way to structured resonance models that eliminate randomness as a fundamental principle. Instead, what has been historically interpreted as statistical noise or probabilistic distributions is reframed as a function of deep prime-driven harmonic constraints, shaping everything from quantum mechanics to intelligence and cosmic expansion. By formalizing this framework, we provide the first exhaustive categorization of all essential resonance-structuring numbers, demonstrating that their presence across mathematics, physics, and cognition is not coincidental but necessary. This work challenges the traditional notion that constants are domain-specific artifacts and instead presents them as universal resonance signatures that dictate the fabric of reality itself. (shrink)

Nick Bostrom’s recently patched ‘‘simulation argument’’ (Bostrom in Philos Q 53:243–255, 2003; Bos- trom and Kulczycki in Analysis 71:54–61, 2011) purports to demonstrate the probability that we ‘‘live’’ now in an ‘‘ancestor simulation’’—that is as a simulation of a period prior to that in which a civilization more advanced than our own—‘‘post-human’’—becomes able to simulate such a state of affairs as ours. As such simulations under consid- eration resemble ‘‘brains in vats’’ (BIVs) and may appear open to similar objections, the (...) paper begins by reviewing objections to BIV-type proposals, specifically those due a presumed mad envatter. In counter example, we explore the motivating rationale behind current work in the development of psychologically realistic social simula- tions. Further concerns about rendering human cognition in a computational medium are confronted through review of current dynamic systems models of cognitive agency. In these models, aspects of the human condition are repro- duced that may in other forms be considered incomputable, i.e., political voice, predictive planning, and consciousness. The paper then argues that simulations afford a unique potential to secure a post-human future, and may be nec- essary for a pre-post-human civilization like our own to achieve and to maintain a post-human situation. Long-s- tanding philosophical interest in tools of this nature for Aristotle’s ‘‘statesman’’ and more recently for E.O. Wilson in the 1990s is observed. Self-extinction-level threats from State and individual levels of organization are compared, and a likely dependence on large-scale psychologically realistic simulations to get past self-extinction-level threats is projected. In the end, Bostrom’s basic argument for the conviction that we exist now in a simulation is reaffirmed. (shrink)

The rise of mechanistic science in the seventeenth century helped give rise to a heated debate about whether teleology—the appearance of purposive activity in life and in mind—could be naturalized. At issue here were both what is meant by “teleology” as well as what is meant “nature”. I shall examine a specific episode in the history of this debate in the twentieth century with the rise of cybernetics: the science of seemingly “self-controlled” systems. Against cybernetics, Hans Jonas argued that (...) cybernetics failed as a naturalistic theory of teleology and that the reality of teleology is grounded in phenomenology, not in scientific explanations. I shall argue that Jonas was correct to criticize cybernetics but that contemporary work in biological organization succeeds where cybernetics failed. I will then turn to contemporary uses of Jonas’s phenomenology in enactivism and argue that Jonas’s phenomenology should be avoided by enactivism as a scientific research program, but that it remains open whether enactivism as a philosophy of nature should also avoid Jonas. (shrink)

In this paper we analyze methodological and philosophical implications of algorithmic aspects of unconventional computation. At first, we describe how the classical algorithmic universe developed and analyze why it became closed in the conventional approach to computation. Then we explain how new models of algorithms turned the classical closed algorithmic universe into the open world of algorithmic constellations, allowing higher flexibility and expressive power, supporting constructivism and creativity in mathematical modeling. As Goedels undecidability theorems demonstrate, the closed algorithmic universe restricts (...) essential forms of mathematical cognition. In contrast, the open algorithmic universe, and even more the open world of algorithmic constellations, remove such restrictions and enable new, richer understanding of computation. (shrink)

This book consists of an edited collection of original essays of the highest academic quality by seasoned experts in their fields of cognitive science. The essays are interdisciplinary, drawing from many of the fields known collectively as “the cognitive sciences.” Topics discussed represent a significant cross-section of the most current and interesting issues in cognitive science. Specific topics include matters regarding machine learning and cognitive architecture, the nature of cognitive content, the relationship of information to cognition, the role of language (...) and communication in cognition, the nature of embodied cognition, selective topics in visual cognition, brain connectivity, computation and simulation, social and technological issues within the cognitive sciences, and significant issues in the history of neuroscience. This book will be of interest to both professional researchers and newer students and graduate students in the fields of cognitive science—including computer science, linguistics, philosophy, psychology and neuroscience. The essays are in English and are designed to be as free as possible of technical jargon and therefore accessible to young scholars and to scholars who are new to the cognitive neurosciences. In addition to several entries by single authors, the book contains several interesting roundtables where researchers contribute answers to a central question presented to those in the focus group on one of the core areas listed above. This exciting approach provides a variety of perspectives from across disciplines on topics of current concern in the cognitive sciences. (shrink)

El prefijo “auto” en autoorganización y autopoiesis se refiere a la existencia de una identidad o agencialidad implicada en el orden, organización o producción de un sistema que se corresponde con el sistema mismo, en contraste con el diseño o la influencia de carácter externo. La autoorganización (AO) estudia la manera en la que los procesos de un sistema alcanzan de forma espontánea un orden u organización complejo, bien como una estructura o patrón emergente, bien como algún tipo de (...) finalidad o identidad autoconstruida. En este trabajo nos ocupamos del concepto de AO en el contexto de la problemática sobre la naturaleza la vida y de los organismos vivientes. Este concepto se elabora en diferentes tradiciones científicas y filosóficas, a partir de su origen en la filosofía kantiana. La cibernética trata de emular la organización de los seres vivos y su teleología mediante la construcción de máquinas; desarrolla una perspectiva centrada en la regulación y en la causalidad mutua entre componentes del sistema. Estos trabajos, a veces complementados con la teoría de sistemas y la teoría de la información, son fundamentales para el desarrollo de la ciencia del siglo XX, especialmente las ciencias computacionales y la biología. Una segunda corriente surge desde la termodinámica de los procesos irreversibles alejados del equilibrio a partir, entre otros, de los trabajos de la escuela de Bruselas, en la que la AO se explora como la formación espontánea de estructuras de orden disipativo. Una tercera tradición, tal vez la más profundamente kantiana, se desarrolla en el contexto de la biología del desarrollo e integra a las dos mencionadas previamente, pues combina aspectos de las dos previas en el desarrollo ontogenético. Podemos decir que cada una de estas concepciones de la AO se relaciona con modelos paradigmáticos diferentes. La noción de autopoiesis (AP), por su parte, fue propuesta en los años 70 por los biólogos chilenos Humberto Maturana y Francisco Varela para explicar la organización individual de los seres vivos como un proceso dinámico que genera una identidad desde las operaciones del sistema (Maturana y Varela 1973). Puede decirse que hereda y reorganiza ideas de la tradición de la AO, especialmente la kantiana y la cibernética, para proponer una teoría biológica alternativa. El enfoque autopoiético concibe el fenómeno de la vida y a los seres vivos de forma muy diferente a la teoría de la evolución o la biología molecular que constituían las líneas de investigación predominantes en la biología de su tiempo. La teoría subraya como propiedad básica de un sistema viviente su autoconstitución dinámica como unidad dotada de identidad a partir de interacciones entre sus componentes. Sin embargo, aquellas propiedades de la vida consideradas primordiales en el enfoque darwiniano, como la reproducción o la evolución, se ven como secundarias, pues requieren de la existencia previa de sistemas autopoiéticos. El objetivo de esta voz es examinar diferentes aspectos que configuran las tradiciones autoorganizativa y autopoiética, en especial las tensiones conceptuales internas que permiten comprender los desafíos a los que se enfrentan ambas en el marco de la filosofía y la teoría de la biología, así como la forma en que sus posiciones e intuiciones contrastan con otras perspectivas en biología. (shrink)

Our technological lifeworld has become an info-computational media populated by data and algorithms, an artificial environment for life and shared experiences. In this chapter, I tried to sketch three new assumptions for bioethics – it is hardly possible to substantiate ethical guidelines or an idea of normativity in an aprioristic manner; moral status is a function of data entities, not something solely human; agency is plural and thus is shared or sometimes delegated – in order to chart a proposal (...) for a posthuman bioethics. Posthuman is perhaps not the best expression available, but it covers the idea of a shift from a world centered on self-contained and exclusively human agency to a more comprehensive and relational way of thinking. The “posthuman” label should be understood as a rebuttal of biocentrism and anthropocentrism by moving closer to conceptions we encounter in population ethics or in discourse about biosocial and technical systems. Posthuman bioethics is “environmentalist” without losing the humanistic stance. The question regarding how suitable an infocentric bioethics is in practice remains to be settled. The moral principles in bioethics could be reconceived as relying on these new assumptions, in a postindividualistic manner that accepts formal primacy of causal digital artifacts in affording actions in a world of ambient algorithmic intelligence. (shrink)

Constructivism is frequently met with objections, criticism and often equated with nihilism or relativism. Sometimes even blamed for what some would randomly picture as unwanted side effects of radicalism or of a progressivist era: such misconceptions are not only due to an imprecise grasp of the premises shared by the constructivist family of systems. The structure of media, political systems, and economic models, still up today impel societal understandings of knowledge on neo-positivistic grounds. The first part of this essay outlines (...) such pressures while sketching how language and worldviews play critical roles in our knowledge construction. Focusing on recent mediatic events, this work advances displaying some essential limits regarding the construction of human knowledge. Though unavoidable, some of the distinguishing aspects regarding the nature of our narratives are then critically reviewed. Later, it is shown how a special kind of self-denial that certain sub-stories implicitly hold about their own narrative nature leaves us with clashing worldviews that eventually collide onto crisis. Finally, it’ll be argued that it’s precisely in this scenario where a constructivist depiction of social discourses may move us away from any adolescent intent of elucidating absolutes within mere heuristics, to the pragmatic need of arriving on satisfactory agreements between parties (DOI: 10.4236/ojpp.2022). (shrink)

This article proposes an empirical approach to understanding the life of an organism that overcomes reductionist and dualist approaches. The approach is based on Immanuel Kant’s analysis of the cognitive conditions required for the recognition of an organism: the concept of teleology and the assumption of a formative power of self-generation. It is analyzed how these two criteria are applied in the cognition of a developing organism. Using the example of a developmental series of a plant leaf, an active (...) and relational process between observer and developing organism is shown, within which the teleology and self-generating power of the organism can be empirically observed through the mental faculties of understanding and will. Furthermore, it is emphasized that, according to Kant, even physical objects are not readily given, but are actively constituted through the unification of sense perceptions with concepts. This Kantian mode of objectification facilitates cognition of the physical properties of an organism. It can be supplemented with a participatory and co-constitutive mode of realization, in which the life of the organism (its teleologically organizing and self-generating power) can become an object of empirical research. Furthermore, it is argued that the participatory mode also facilitates an expanded conception of nature that allows for the existence of living beings within it. Finally, an analogy to Goethe’s approach to the living organism is highlighted. In summary, it is stated that to understand life, one must consciously participate in it. (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 many years now, Harnad has argued that transduction is special among cognitive capacities -- special enough to block Searle's Chinese Room Argument. His arguments (as well as Searle's) have been important and useful, but not correct, it seems to me. Their arguments have provided the modern impetus for getting clear about computationalism and the nature of computing. This task has proven to be quite difficult. Which is simply to say that dealing with Harnad's arguments (as well as Searle's) (...) has been difficult. Turing, it turns out, only got us started. But Harnad's (and Searle's) arguments ultimately fail. Turing, it turns out, was on the right track. (shrink)

This essay proposes an ontological model in which a legal person such as a polity possesses natural unity from group properties that emerge in the self-organization of the human population. Also, analysis of customary legal persons and property indicates noncontradictory paradoxes that include Aristotelian essence of an entity, relative identity over time, ubiquitous authority, coinciding authorities, and identical entities. Mathematical modeling helps to explain the logic of the paradoxes.

The theory that all processes in the universe are computational is attractive in its promise to provide an understandable theory of everything. I want to suggest here that this pancomputationalism is not sufficiently clear on which problem it is trying to solve, and how. I propose two interpretations of pancomputationalism as a theory: I) the world is a computer and II) the world can be described as a computer. The first implies a thesis of supervenience of the physical over computation (...) and is thus reduced ad absurdum. The second is underdetermined by the world, and thus equally unsuccessful as theory. Finally, I suggest that pancomputationalism as metaphor can be useful. – At the Paderborn workshop in 2008, this paper was presented as a commentary to the relevant paper by Gordana Dodig-Crnkovic " Info-Computationalism and Philosophical Aspects of Research in Information Sciences". (shrink)

We analyse Hutto & Myin's three arguments against computationalism [Hutto, D., E. Myin, A. Peeters, and F. Zahnoun. Forthcoming. “The Cognitive Basis of Computation: Putting Computation In Its Place.” In The Routledge Handbook of the Computational Mind, edited by M. Sprevak, and M. Colombo. London: Routledge.; Hutto, D., and E. Myin. 2012. Radicalizing Enactivism: Basic Minds Without Content. Cambridge, MA: MIT Press; Hutto, D., and E. Myin. 2017. Evolving Enactivism: Basic Minds Meet Content. Cambridge, MA: MIT Press]. The Hard Problem (...) of Content targets computationalism that relies on semantic notion of computation, claiming that it cannot account for the natural origins of content. The Intentionality Problem is targeted against computationalism using non-semantic accounts of computation, arguing that it fails in explaining intentionality. Theion Problem claims that causal interaction between concrete physical processes and abstract computational properties is problematic. We argue that these a... (shrink)

Due to his significant role in the development of computer technology and the discipline of artificial intelligence, Alan Turing has supposedly subscribed to the theory of mind that has been greatly inspired by the power of the said technology which has eventually become the dominant framework for current researches in artificial intelligence and cognitive science, namely, computationalism or the computational theory of mind. In this essay, I challenge this supposition. In particular, I will try to show that there is no (...) evidence in Turing’s two seminal works that supports such a supposition. His 1936 paper is all about the notion of computation or computability as it applies to mathematical functions and not to the nature or workings of intelligence. On the other hand, while his 1950 work is about intelligence, it is, however, particularly concerned with the problem of whether intelligence can be attributed to computing machines and not of whether computationality can be attributed to human intelligence or to intelligence in general. (shrink)

How the mind works is the ultimate mystery for human beings. To answer this question, one of the most significant insights is Kant’s argument that we can only perceive phenomena but not the essence of the external world. Following this idea, we formulated a novel computational framework to model the mind based on two assumptions: 1) There is no presupposition of the existence of the external objective world and the main task of the mind is to explore, establish and utilize (...) persistent relationships between senses and actions. 2) Symbolic intermediate-level representation is necessary. We call this framework the self-programming system. Based on the self-programming system, we interpreted the nature of concepts, time, space, causality, and consciousness. Besides that, we also draw a conclusion to the mind-body problem. Specifically, the perspective that considers subjective experience as fundamental is compatible with the existence of the external world and that the physical body serves as the substrate of the mind. However, since the relationship is “compatible” rather than “cause”, skepticism can never be ruled out in principle. Furthermore, we have elucidated the nature of interpretation within the context of reductive physicalism. This serves to provide a clear understanding as to why the hard problem of consciousness is inherently unsolvable. (shrink)

The rigor and precision of Leibniz's "possible world" evolved into the concept of Turing machine, and with the birth of the first computer and the physical realization of Turing machine, human cognitive and intelligent activities were optimistically considered by cognitive scientists to be convertible into computational programs for simulation by machines. Cognitive science then formed the research agenda of "cognitive computationalism", and our Chinese scholars have responded to this general view that "the essence of cognition is computation" and that the (...) human brain and computers are merely formal systems for manipulating and processing symbols in their respective fields. Professor Hong Dingguo used the concepts of "manifest order" and "hidden order", Professor Jin Gulun used the philosophical categories of "constitutive theory" and "generative theory", and Liu Yuesheng used the concept of "cognition". Professor Hong Dingguo reinterpreted the relationship between the "real world" and the "possible world" by using the concepts of "manifest sequence" and "hidden sequence", Professor Jin Wulun used the philosophical categories of "composition theory" and "generation theory", and Professor Liu Yuesheng used the generalized information paradigm of "structural information" and "exchange information". "This provides a profound elucidation of the nature of cognitive logic and the process of human cognitive activities becoming symbols through coding and puts forward theoretical limits. The current theoretical dilemma and practical difficulty of cognitive science lies in the fact that it has developed algorithmic concepts in the Turing sense that can only model the explicitly sequential part of cognitive and intellectual activity, forming a constitutive atomic abstraction, or in our terms, classical "structural information", which cannot fully explain the inner mechanisms of human mental activity and its embodied flexibility, selectivity and self-emergence. Wu Xuemou's pan-system theory does not simply rely on logic and Turing machine algorithms, but models human intelligence by treating structural information as a complex large system composed of a collection of elements and a collection of relations (exchange information: five mutual eight chips), in order to break through the narrow path of seeking only the local consistency of the system as currently done; while Wang Dekui's exploration of the three-spin biological and physical path and Huang Zhanji's abandonment of the artificial means of logic to seek the large logical natural mechanisms and Mr. Zhou Liquan's proposal of a schema for successful communication using natural language on the basis of concepts such as context and implicit connotation ("Logic — A Theory of Correct Thinking and Successful Communication"), all reflect the milestones of Chinese scholars who seek to address the deeper issues of cognitive science. We believe that along this series of explorations, combined with new results in recent years in the fields of artificial life and evolutionary computation, a new research agenda in cognitive science will emerge. (shrink)

Cloud computing has transformed the nature of computation, sharing of information resources, and storage capabilities, including the flexibility to scale these resources for corporate use. Nevertheless, maintaining high reliability in cloud environments is still an issue that has not been solved because of factors such as Hardware failures, network interruptions/slowdowns and software vulnerabilities. This paper discusses several methods that can be employed in the reliability engineering of cloud computing, including fault tolerance, redundancy, monitoring and predictive maintenance. It also (...) further extends the basic reliability measures such as Mean Time Between Failure (MTBF), Mean Time To Repair (MTTR), Service Availability and Failure Rate, which measure system reliability and effectiveness. Moreover, the paper considers performance assessment methodologies through real-time monitoring, machine learning, and reliability assessment methods. It also addresses the nature and advancement of technologies of artificial intelligence-powered automation and self-healing applications for improved cloud dependability. The present work aims to identify the state-of-the-art state of dependability in cloud services and propose some recommendations for minimizing such costs, improving dependability levels, and reducing undesired downtime. The information is valuable for CSPs, IT designers/architects, and system engineers who wish to create fault-tolerant and optimal cloud environments. (shrink)

It is pointed out that the different concepts of the Universe serve as an ultimate basis determining the frames of consciousness. A unified concept of the Universe is explored which includes consciousness and matter as well to the universe of existents. Some consequences of the unified concept of the Universe are derived and shown to be able to solve the paradox of the self-founding notion of the Universe. The self-contained Universe is indicated to possess a logical nature. It (...) is shown that a passage exists between consciousness and the material world and its nature is exemplified. Moreover, arguments are presented showing the simultaneous emotional nature of the Universe. A solution to the paradox of semi-finite spacetime existents, possessing finite and infinite substances simultaneously, namely eternal existents like moral values and logical validity, is obtained. (shrink)

The Paradigm of the Systemic Continuum (PCS), presented in Toward a Systemic Continuum (de León Pontet, 2025), challenges the natural/artificial dichotomy as an anthropocentric bias that has fragmented systems theory for centuries. This second preprint formalizes Systemic Balance (BS) as a hierarchical principle—articulated as Balance Sistémico Interior (BSI), Umbral Sistémico (US), and Balance Sistémico Exterior (BSE)—that unifies biological, technological, social, and cosmic systems in an emergent continuum. Integrating the insights of homeostasis (Wiener), autopoiesis (Maturana & Varela), emergence (Kauffman), and (...) cybernetics with a renewed systemic phenomenology (Heidegger, Merleau-Ponty), the PCS repositions the observer as a catalyzing ingredient, not an external creator, and redefines intelligence as the capacity for systemic reconfiguration. We propose operative metrics (ICS, CNS, MDO, IDS) for empirical validation, offer a tentative mathematical formalization, and explore historical blind spots that honor and transcend the pioneers of systems theory. Ranging from urban coralization to cosmic forces, the PCS aspires to serve as a unified theory of emergent organization, inviting scientists, philosophers, and technologists to a revolutionary dialogue that illuminates how synergy weaves the tapestry of the universe. (shrink)

La structure du comportement details consciousness-nature relations by navigating between realist and intellectualist alternatives. A phenomenological reading of form guides its attempt to formulate a view that does not reduce consciousness to matter or perceptual structure to a product of mind. I show that this strategy relies on hitherto overlooked idealist commitments. Forms are perceived objects whose intentional structure is intelligibly organized. Having denied that forms are constituted by mind or emergent from matter, Merleau-Ponty likens form-constitution to an ideal process (...) of intentional self-organization. Despite recognizing that Gestalt psychology develops fruitful models of perceptual self-organization, and adopting the transcendental view that form is significant for consciousness, his revisionary interpretation of form outstrips these accounts’ ontologies of mind and nature, and is better understood in light of a post-Kantian philosophical heritage. These results cast Merleau-Ponty’s relation to the Gestalt, post-Kantian, and phenomenological traditions in new light, challenge naturalizing interpretations of Structure, and motivate a rethinking of the status of metaphysics in his early thought. (shrink)

We set forth what one may call a “constitutional reading” in opposition to the traditional widespread “constructivist reading” of the object of cognition. In the light of the so-called one-object view reading of Transcendental idealism, the object of cognition is nothing but the object that exists in itself insofar as it appears to our cognitive apparatus. The object exists mind-independently, while our cognition of the same object must be mind-independent. The constructivist reading mistakes the epistemological problem of how we come (...) to cognize mind-dependently that what we represent (“the constitutional view”) are mind-independent objects with the Berkelian ontological problem of how we construct objects out of an undifferentiated, unstructured manifold (the constructivist view) My diagnosis is as follows. The first reason is the traditional “two-worlds view” reading of Kant’s idealism. If we take what exists in itself and the object of cognition as distinct things, then we must conclude that the object of cognition is a mind-dependent construction. Constructivist readers mistake the mind-dependent nature of our human cognition of objects for the putative mind-dependent nature of the known object. The second reason is overintellectualization. Constructivist readers mistake the objectifying syntheses of the imagination, below the threshold of self-consciousness, for cognitive conceptual operations by means of which we cognize (erkennen) the objects of cognition, or so I shall argue. (shrink)

I want to suggest that the major influence of classical arguments for embodiment like "The Embodied Mind" by Varela, Thomson & Rosch (1991) has been a changing of positions rather than a refutation: Cognitivism has found ways to retreat and regroup at positions that have better fortification, especially when it concerns theses about artificial intelligence or artificial cognitive systems. For example: a) Agent-based cognitivism' that understands humans as taking in representations of the world, doing rule-based processing and then acting on (...) them (sense-plan-act) is often limited to conscious decision processes; and b) Purely syntactic cognition is compatible with embodiment, or supplemented by embodiment (e.g. for 'grounding'). While the empirical thesis of embodied cognition ('embodied cognitive science') is true and the practical engineering thesis ('morphological computation', 'cheap design') is often true, the conceptual thesis ('embodiment is necessary for cognition') is likely false - syntax is often enough for cognition, unless grounding is really necessary. I conclude that it has become more sensible to integrate embodiment with traditional approaches rather than "fight for embodiment" or "against cognitivism". (shrink)

This thesis explores and discusses foundational issues concerning the relationship between phenomenological philosophy and the enactive approach to cognitive science, with the aim of clarifying, developing, and promoting the project of enactive phenomenology. This project is framed by three general ideas: 1) that the sciences of mind need a phenomenological grounding, 2) that the enactive approach is the currently most promising attempt to provide mind science with such a grounding, and 3) that this attempt involves both a naturalization of phenomenology (...) and a phenomenologization of the concept of nature. More specifically, enactive phenomenology is the project of pursuing mutually illuminative exchanges between, on the one hand, phenomenological investigations of the structures of lived experience and embodied existence and, on the other, scientific accounts of mind and life – in particular those framed by theories of biological self-organization. The thesis consists of two parts. Part one is an introductory essay that seeks to clarify some of enactive phenomenology’s overarching philosophical commitments by tracing some of its historical roots. Part two is a compilation of four articles, each of which intervenes in a different contemporary debate relevant to the dissertation’s project. (shrink)