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

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

This volume brings together for the first time the diverse threads within the growing field of serendipity research, to reflect both on the origins of this emerging field within different disciplines as well as its increasing influence as its own field with foundational texts and emerging practices. The phenomenon of serendipity has been described in many ways since Horace Walpole initially coined the term in 1754 to categorize those discoveries that happen by “both accidents and sagacity”. This book offers a (...) sampling of perspectives from experts in serendipity research from organizational studies, management theory, information science and library studies, psychology, literature, computer science, social science, ethics, and the history and philosophy of science. Considerations about the importance and role of serendipity are being raised now across science (both empirical and theoretical) as well as practice (from art and innovation to leadership and governance), with ever more eyes looking closer at its significance in human history and the likelihood it will play a key, while unpredictable, role in forming our future. Serendipity Science represents an emerging, and also important and potentially necessary field of study, if we are to deal well as a society with our complex times and uncertain future. (shrink)

The increased complexity of health information management sows the seeds of inequalities between health care stakeholders involved in the production and use of health information. Patients may thus be more vulnerable to use of their data without their consent and breaches in confidentiality. Health care providers can also be the victims of a health information system that they do not fully master. Yet, despite its possible drawbacks, the management of health information is indispensable for advancing science, medical (...) care and public health. Therefore, the central question addressed by this paper is how to manage health information ethically? This article argues that Paul Ricœur’s ‘‘little ethics’’, based on his work on hermeneutics and narrative identity, provides a suitable ethical framework to this end. This ethical theory has the merit of helping to harmonise self-esteem and solicitude amongst patients and healthcare providers, and at the same time provides an ethics of justice in public health. A matrix, derived from Ricœur’s ethics, has been developed as a solution to overcoming possible conflicts between privacy interests and the common good in the management of health information. (shrink)

The challenges of the Anthropocene are growing ever more complex and uncertain, underpinned by the emergence of systemic risks. At the same time, the landscape of risk governance has become compartmentalised and siloed, characterized by non-overlapping activities, competing scientific discourses, and distinct responsibilities distributed across diverse public and private bodies. Operating across scales and disciplines, actors tend to work in silos which constitute critical gaps within the interface of science, policy, and practice. Yet, increasingly complex and ‘wicked’ problems require holistic (...) solutions, multi-scalar communication, coordination, collaboration, data interoperability, funding, and stakeholder engagement. To address these problems in a real-world context, we present the Risk-Tandem framework for bridging theory and practice; to guide and structure the integration of disaster risk management (DRM), climate change adaptation (CCA) and systemic risk management through a process of transdisciplinary knowledge co-production. Advancing the frontiers of knowledge in this regard, The Risk-Tandem framework combines risk management approaches and tools with iterative co-production processes as a cornerstone of its implementation, in efforts to promote the co-design of fit-for-purpose solutions, methods and approaches contributing toward strengthened risk governance alongside stakeholders. The paper outlines how the framework is developed, applied, and further refined within selected case study regions, including Denmark, Germany, Italy and the Danube Region. (shrink)

Software application ontologies have the potential to become the keystone in state-of-the-art information management techniques. It is expected that these ontologies will support the sort of reasoning power required to navigate large and complex terminologies correctly and efficiently. Yet, there is one problem in particular that continues to stand in our way. As these terminological structures increase in size and complexity, and the drive to integrate them inevitably swells, it is clear that the level of consistency required for (...) such navigation will become correspondingly difficult to maintain. While descriptive semantic representations are certainly a necessary component to any adequate ontology-based system, so long as ontology engineers rely solely on semantic information, without a sound ontological theory informing their modeling decisions, this goal will surely remain out of reach. In this paper we describe how Language and Computing nv (L&C), along with The Institute for Formal Ontology and Medical Information Sciences (IFOMIS), are working towards developing and implementing just such a theory, combining the open software architecture of L&C’s LinkSuiteTM with the philosophical rigor of IFOMIS’s Basic Formal Ontology. In this way we aim to move beyond the more or less simple controlled vocabularies that have dominated the industry to date. (shrink)

Humans are cognitive entities. Our ongoing interactions with the environment are threaded with creations and usages of meaningful information. Animal life is also populated with meaningful information related to survival constraints. Information managed by artificial agents can also be considered as having meanings, as derived from the designer. Such perspective brings us to propose an evolutionary approach to cognition based on meaningful information management. We use a systemic tool, the Meaning Generator System (MGS), and apply it consecutively to animals, (...) humans and artificial agents [1, 2]. The MGS receives information from its environment and compares it with its constraint. The generated meaning is the connection existing between the received information and the constraint. It triggers an action aimed at satisfying the constraint. The action modifies the environment and the generated meaning. Meaning generation links agents to their environments. The MGS is a system: a set of elements linked by a set of relations. Any system submitted to a constraint and capable of receiving information can lead to a MGS. Animals, humans and robots are agents containing MGSs dealing with different constraints. Similar MGSs carrying different constraints will generate different meanings. Cognition is system dependent. Contrary to approaches on meaning generation based on psychology or linguistics, the MGS approach is not based on human mind. We want to avoid the circularity of taking human mind as a starting point. Free will and self-consciousness participate to the management of human meanings. They do not exist for animals or robots. Staying alive is a constraint that we share with animals. Robots ignore that constraint. We first use the MGS for animals with “stay alive” and “group life” constraints. The analysis of meaning and cognition in animals is however limited by our un-complete understanding of the nature of life (the question of final causes). Extending the analysis of meaning generation and cognition to humans is complex and has some true limitations as the nature of human mind is a mystery for today science and philosophy. The natures of our feelings, free will or self-consciousness are unknown. Approaches to identify human constraints are however possible, where the MGS can highlight some openings [3, 4]. Modeling meaning management in artificial agents is rather straightforward with the MGS. We, the designers, know the agents and the constraints. The derived nature of constraints, meaning and cognition is however to be highlighted. We define a meaningful representation of an item for an agent as being the networks of meanings relative to the item for the agent, with the action scenarios involving the item. Such meaningful representations embed the agents in their environments and are far from the GOFAI type of representations. Cognition, meanings and representations exist by and for the agents. We finish by summarizing the points presented here and highlight possible continuations . [1] “Information and Meaning” [2] “Introduction to a systemic theory of meaning” [3] “Computation on Information, Meaning and Representations. An Evolutionary Approach” [4] “Proposal for a shared evolutionary nature of language and consciousness”. (shrink)

The nature of time is perceived by intellectuals variedly. An attempt is made in this paper to reconcile such varied views in the light of the Upanishads and related Indian spiritual and philosophical texts. The complex analysis of modern mathematics is used to represent the nature and presentation physical and psychological times so differentiated. Also the relation between time and energy is probed using uncertainty relations, forms of energy and phases of matter. Implications to time-dependent Schrodinger wave equation and uncertainty (...) principle are hinted. (shrink)

This collection of articles was written over the last 10 years and edited to bring them up to date (2017). The copyright page has the date of the edition and new editions will be noted there as I edit old articles or add new ones. All the articles are about human behavior (as are all articles by anyone about anything), and so about the limitations of having a recent monkey ancestry (8 million years or much less depending on viewpoint) and (...) manifest words and deeds within the framework of our innate psychology as presented in the table of intentionality. As famous evolutionist Richard Leakey says, it is critical to keep in mind not that we evolved from apes, but that in every important way, we are apes. If everyone was given a real understanding of this (i.e., of human ecology and psychology to actually give them some control over themselves), maybe civilization would have a chance. As things are however the leaders of society have no more grasp of things than their constituents and so collapse into anarchy is inevitable. The first group of articles attempts to give some insight into how we behave that is reasonably free of the theoretical delusions that are universal. In the next group, I show how these insights apply by reviewing some books in philosophy and psychology. Next I review books on science and religion and finally provide reviews and articles showing how understanding of both science and philosophy gives insight into the tragic delusions destroying the world. People believe that society can be saved by science, religion and politics, so I provide some suggestions as to why this is unlikely via short articles and reviews of recent books by well-known writers. It is critical to understand why we behave as we do and so the first section presents articles that try to describe (not explain as Wittgenstein insisted) behavior. I start with a brief review of the logical structure of rationality, which provides some heuristics for the description of language (mind, rationality, personality) and gives some suggestions as to how this relates to the evolution of social behavior. This centers around the two writers I have found the most important in this regard, Ludwig Wittgenstein and John Searle, whose ideas I combine and extend within the dual system (two ii systems of thought) framework that has proven so useful in recent thinking and reasoning research. As I note, there is in my view essentially complete overlap between philosophy, in the strict sense of the enduring questions that concern the academic discipline, and the descriptive psychology of higher order thought (behavior). Once one has grasped Wittgenstein’s insight that there is only the issue of how the language game is to be played, one determines the Conditions of Satisfaction (what makes a statement true or satisfied etc.) and that is the end of the discussion. Since philosophical problems are the result of our innate psychology, or as Wittgenstein put it, due to the lack of perspicuity of language, they run throughout human discourse and behavior, so there is endless need for philosophical analysis, not only in the ‘human sciences’ of philosophy, sociology, anthropology, political science, psychology, history, literature, religion, etc., but in the ‘hard sciences’ of physics, mathematics, and biology. It is universal to mix the language game questions with the real scientific ones as to what the empirical facts are. Scientism is ever present and the master has laid it before us long ago, i.e., Wittgenstein (hereafter W) beginning with the Blue and Brown Books in the early 1930’s. "Philosophers constantly see the method of science before their eyes and are irresistibly tempted to ask and answer questions in the way science does. This tendency is the real source of metaphysics and leads the philosopher into complete darkness." (BBB p18) The key to everything about us is biology, and it is obliviousness to it that leads millions of smart educated people like Obama, Chomsky, Clinton and the Pope to espouse suicidal utopian ideals that inexorably lead straight to Hell on Earth. As W noted, it is what is always before our eyes that is the hardest to see. We live in the world of conscious deliberative linguistic System 2, but it is unconscious, automatic reflexive System 1 that rules. This is the source of the universal blindness described by Searle’s The Phenomenological Illusion (TPI), Pinker’s Blank Slate and Tooby and Cosmides’ Standard Social Science Model. The astute may wonder why we cannot see System 1 at work, but it is clearly counterproductive for an animal to be thinking about or second guessing every action, and in any case, there is no time for the slow, massively integrated System 2 to be involved in the constant stream of split second ‘decisions’ we must make. As W noted, our ‘thoughts’ (T1 or the ‘thoughts’ of System 1) must lead directly to actions. iii It is my contention that the table of intentionality (rationality, mind, thought, language, personality etc.) that features prominently here describes more or less accurately, or at least serves as an heuristic for, how we think and behave, and so it encompasses not merely philosophy and psychology, but everything else (history, literature, mathematics, politics etc.). Note especially that intentionality and rationality as I (along with Searle, Wittgenstein and others) view it, includes both conscious deliberative System 2 and unconscious automated System 1 actions or reflexes. Thus, all the articles, like all behavior, are intimately connected if one knows how to look at them. As I note, The Phenomenological Illusion (oblivion to our automated System 1) is universal and extends not merely throughout philosophy but throughout life. I am sure that Chomsky, Obama, Zuckerberg and the Pope would be incredulous if told that they suffer from the same problem as Hegel, Husserl and Heidegger, (or that that they differ only in degree from drug and sex addicts in being motivated by stimulation of their frontal cortices by the delivery of dopamine (and over 100 other chemicals) via the ventral tegmentum and the nucleus accumbens), but it’s clearly true. While the phenomenologists only wasted a lot of people’s time, they are wasting the earth and their descendant’s futures. Many of the articles describe the ‘digital delusions’, which confuse the language games of System 2 with the automatisms of System 1, and so cannot distinguish biological machines (i.e., people) from other kinds of machines (i.e., computers). The ‘reductionist’ claim is that one can ‘explain’ behavior at a ‘lower’ level, but what actually happens is that one does not explain human behavior but a ‘stand in’ for it. Hence the title of Searle’s classic review of Dennett’s book (“Consciousness Explained”)— “Consciousness Explained Away”. In most contexts ‘reduction’ of higher level emergent behavior to brain functions, biochemistry, or physics is incoherent. Also for ‘reduction’ of chemistry or physics, the path is blocked by chaos and uncertainty. Anything can be ‘represented’ by equations, but when they ‘represent’ higher order behavior, it is not clear (and cannot be made clear) what the ‘results’ mean. Reductionist metaphysics is a joke, but most scientists and philosophers lack the appropriate sense of humor. Other digital delusions are that we will be saved from the pure evil (selfishness) of System 1 by computers/AI/robotics/ nanotech/genetic engineering created by System 2. The No Free Lunch principal tells us there will be serious and possibly fatal consequences. The adventurous may regard this principle as a higher order emergent expression of the Second Law of Thermodynamics. Hi-tech enthusiasts hugely underestimate the problems iv resulting from unrestrained motherhood, and of course it is neither profitable nor politically correct (and now with third world supremacism dominant, not even possible) to be honest about it. The last section describes various versions of the ‘altruism delusion’ that we are selected for cooperation, and that the euphonious ideals of Democracy, Diversity and Equality will lead us into utopia, if we just manage things correctly (the possibility of politics). Again, the No Free Lunch Principle ought to warn us it cannot be true, and we see throughout history and all over the contemporary world, that without strict controls, selfishness and stupidity gain the upper hand and soon destroy any nation that embraces it. In addition, the monkey mind steeply discounts the future, and so we cooperate in selling our descendant’s heritage for temporary comforts, greatly exacerbating the problems. I describe versions of this delusion (i.e., that we are basically ‘friendly’ if just given a chance) as it appears in some recent books on sociology/biology/economics. I end with an essay on the great tragedy playing out in America and the world, which can be seen as a direct result of our evolved psychology manifested as the inexorable machinations of System 1. Our psychology, eminently adaptive and eugenic on the plains of Africa from ca. 6 million years ago, when we split from chimpanzees, to ca. 50,000 years ago, when many of our ancestors left Africa (i.e., in the EEA or Environment of Evolutionary Adaptation), is now maladaptive and dysgenic and the source of our Suicidal Utopian Delusions. So, like all discussions of behavior (philosophy, psychology, sociology, biology, anthropology, politics, law, literature, history, economics, soccer strategies, business meetings, etc.), this book is about evolutionary strategies, selfish genes and inclusive fitness (kin selection). Many accept the delusion that we are selected for cooperation with people generally (group selection or altruism) and not just our immediate relatives (kin selection or inclusive fitness), so I spend some time in the essays of the last section demolishing this fantasy. One thing rarely mentioned by the group selectionists is the fact that, even were ‘group selection’ possible, selfishness is at least as likely (probably far more likely in most contexts) to be group selected for as altruism. Just try to find examples of true altruism in nature –the fact that we can’t (which we know is not possible if we understand evolution) tells us that its apparent presence in humans is an artefact of modern life concealing the facts, and that it can no more be selected for than the tendency to suicide (which in fact it is). One does not really need science or mathematics to grasp this – it is crushingly obvious that an v organism cannot be selected for behavior that decreases the frequency of its own genes in the next generation. One might also benefit from considering a phenomenon never (in my experience) mentioned by group selectionists -- cancer. No group has as much in common as the (originally) genetically identical cells in our own bodies-a 100 trillion cell clone-- but we are all born with thousands and perhaps millions of cells that have already taken the first step on the path to cancer and generate millions to billions of cancer cells in our life. If we did not die of other things first, we (and perhaps all multicellular organisms) would all die of cancer. Only a massive and hugely complex mechanism built into our genome that represses or derepresses trillions of genes in trillions of cells, and kills and creates billions of cells a second, keeps the majority of us alive long enough to reproduce. One might take this to imply that a just, democratic and enduring society for any kind of entity on any planet in any universe is only a dream, and that no being or power could make it otherwise. It is not only ‘the laws’ of physics that are universal and inescapable, or perhaps we should say that inclusive fitness is a law of physics. The great mystic Osho said that the separation of God and Heaven from Earth and Humankind was the most evil idea that ever entered the Human mind. In the 20th century an even more evil notion arose—that humans are born with rights, rather than having to earn privileges. Thus, every day the population increases by 200,000, who must be provided with resources to grow and space to live, and who soon produce another 200,000 etc. And one almost never hears it noted that what they receive must be taken from those already alive. Their lives diminish those already here in both major obvious and countless subtle ways. Every new baby destroys the earth from the moment of conception. There cannot be human rights without human wrongs. It cannot be more obvious, but one will never see the streets full of protesters against motherhood. America and the world are in the process of collapse from excessive population growth, most of it for the last century and now all of it due to 3rd world people. Consumption of resources and the addition of 4 billion more ca. 2100 will collapse industrial civilization and bring about starvation, disease, violence and war on a staggering scale. The earth loses about 2% of it’s topsoil every year, so as it nears 2100, most of it’s food growing capacity will be gone. Billions will die and nuclear war is all but certain. In America, this is being hugely accelerated by massive immigration and immigrant reproduction, combined with abuses made possible by democracy. Depraved human nature inexorably turns the dream of democracy and diversity into a vi nightmare of crime and poverty. China will continue to overwhelm America and the world, as long as it maintains the dictatorship which limits selfishness. The root cause of collapse is the inability of our innate psychology to adapt to the modern world, which leads people to treat unrelated persons as though they had common interests. This, plus ignorance of basic biology and psychology, leads to the social engineering delusions of the partially educated who control democratic societies. Few understand that if you help one person you harm someone else—there is no free lunch and every single item anyone consumes destroys the earth beyond repair. Consequently, social policies everywhere are unsustainable and one by one all societies without stringent controls on selfishness will collapse into anarchy or dictatorship. Without dramatic and immediate changes, there is no hope for preventing the collapse of America, or any country that follows a democratic system. The popular notions supported by the Democratic Party and Third World Supremacists are that Democracy, Diversity, Equality and Social Justice will produce a Utopia in America and the world, but it is clear as crystal that they unavoidably foster selfishness and divisiveness and are producing collapse. Hence my concluding essay “Suicide by Democracy”. The most basic facts, almost never mentioned, are that there are not enough resources in America or the world to lift a significant percentage of the poor out of poverty and keep them there. Even the attempt to do this is already bankrupting America and destroying the world. The earth’s capacity to produce food decreases daily, as does our genetic quality. And now, as always, by far the greatest enemy of the poor is other poor and not the rich. -/- My writings are available as paperbacks and Kindles on Amazon. -/- Talking Monkeys: Philosophy, Psychology, Science, Religion and Politics on a Doomed Planet - Articles and Reviews 2006-2017 (2017) ASIN B071HVC7YP. -/- The Logical Structure of Philosophy, Psychology, Mind and Language in Ludwig Wittgenstein and John Searle--Articles and Reviews 2006-2016 (2017) ASIN B071P1RP1B. -/- Suicidal Utopian Delusions in the 21st century: Philosophy, Human Nature and the Collapse of Civilization - Articles and Reviews 2006-2017 (2017) 2nd printing with corrections (Feb 2018) ASIN B0711R5LGX -/- Suicide by Democracy: an Obituary for America and the World (2018) ASIN B07CQVWV9C -/- . (shrink)

A huge amount of data is being generated everyday through different transactions in industries, social networking, communication systems etc. Big data is a term that represents vast volumes of high speed, complex and variable data that require advanced procedures and technologies to enable the capture, storage, management, and analysis of the data. Big data analysis is the capacity of representing useful information from these large datasets. Due to characteristics like volume, veracity, and velocity, big data analysis is becoming one (...) of the most challenging research problems. Semantic analysis is method to better understand the implied or practical meaning of the input dataset. It is mostly applied with ontology to analyze content mainly in web resources. This field of research combines text analysis and Semantic Web technologies. The use semantic knowledge is to aid sentiment analysis of queries like emotion mining, popularity analysis, recommendation systems, user profiling, etc. A new method has been proposed to extract semantic relationships between different data attributes of big data which can be applied to a decision system. (shrink)

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

There is a middle ground of imperfect knowledge in fields like medicine and the social sciences. It stands between our day-to-day relatively certain knowledge obtained from ordinary basic observation of regularities in our world and our knowledge from well-validated theories in the physical sciences. -/- The latter enable reliable prediction a great deal of the time of the happening of events never before experienced. The former enable prediction only of what has happened before and beyond that of educated guesses which (...) may sometimes prove right and others not when we test them. -/- The imperfection of our knowledge between those limits is a consequence of complexity. -/- Reductionist empiricism fails when faced with complexity but we all still have to live in a complex world where reductionist science cannot help us devise reliable theories from which we can predict the behaviour of the world around us. We cannot predict reliably. We can only monitor actively and manage actively the world around us if we want to try to control our environment. We have a limited prediction horizon. -/- Science and empiricism work well if we want to send Voyager I and II over 3 billion miles away but not for most other things. -/- In medicine how and when to apply probabilistic, conjectural, incomplete medical theories and explanations requires professional expertise, intuition and judgement (non scientific knowledge) which is essential. Medical diagnosis is a skill of predicting from knowledge of what has happened before that the past will recur in the current patient applying expertise and intuition from knowledge and experience of prior cases and probabilistic medical research and theory. The physician is left to an educated guess as to how the future will develop for the particular individual patient the focus of his or her current attention. (shrink)

The theory of science that Thomas Kuhn built in the Structure of Scientific Revolutions was considered as a hypothetical framework in this study. Since the publication of the work, many questions have arisen that call for a psychology of science. These questions are moved to another dimension through the knowledge of the decision made within Galileo Affair, which occupies an important place in modern science, fundamentally arising from an epistemic struggle and emerging out of an unscientific base rather than the (...) charge of unholiness. Abandoning the perspective which evaluates these questions within a historical process as a weak side of the Kuhnian theory of science, this study challenges the current approaches with an alternative approach. The epistemic complexity in the Kuhnian theory of science is an imperative complexity caused by human factors. From this perspective, there are potential questions for psychology of science as a field of study based on Kuhnian epistemology and it can be assumed that new problems may appear when the other epistemological questions which assumed as “answered” are reviewed in the scope of this study. The main thesis of this study is that psychology of science is possible as a valid and operationalizable research program based on Kuhnian theory of science. (shrink)

In this article, we discuss epistemological limitations relating to the use of ethnoracial categories in biomedical research as devised by the Office of Management and Budget’s institutional guidelines. We argue that the obligation to use ethnoracial categories in genomics research should be abandoned. First, we outline how conceptual imprecision in the definition of ethnoracial categories can generate epistemic uncertainty in medical research and practice. Second, we focus on the use of ethnoracial categories in medical genetics, particularly genomics-based precision medicine, (...) where ethnoracial identity is understood as a proxy for medically relevant differences among individuals. Notably, extensive criticisms have been made already against the genetic interpretation of races, but, nonetheless, the concept of race remains a key element of contemporary genomics. This motivates us to explore possible reasons why such criticisms may have been ineffective in redirecting attention to other (non-race-based) ways of controlling for human variability. We contend that popular arguments against the idea that human races have a genetic basis, though convincing in many respects, are not sufficient to exclude the pragmatic use of race and ethnicity as proxies for genetic variability related to complex phenotypes. Finally, we provide two further arguments to support the idea that ethnoracial categories are unlikely to provide meaningful insights into medical genetics, which implies that even the interpretation of race as a useful tool to stratify disease risk is unwarranted. (shrink)

The notions of information, representation and enaction entertain historical and complex relations with cognition. Historical relations because representational structures belong to the central hypothesis of cognitive sciences. Complex relations because cognitive sciences apply the notion of representation to animals, humans and robots, and also because the enactive approach tends to disregard the GOFAI type of representations. In this wide horizon of relations, we propose to look at a systemic approach that could bring up a common denominator for information and representations (...) in the build up of cognition, and also keep a link with the enactive approach. Our purpose is to show that systems submitted to constraints can generate meaningful information to maintain their natures, and consequently build up meaningful representations that have some compatibility with the enactive approach. Such a systemic approach to the notion of meaningful information could then make available a link between enaction and meaningful representations. The first part of the presentation is about reminding that cognition does not exist per se, but is related to the system that builds it. We look at cognition as constituted by dynamic meaningful representations built up by systems that have constraints to satisfy in their environments. Cognition is considered here at the level of the system that builds it and uses it in order to maintain its nature in its environment. Such a systemic approach fits with evolution. Organisms build representations to cope with survival constraints (frogs build representations of moving black dots in order to satisfy food constraints). Humans build representations and cognition to satisfy constraints that are conscious and unconscious. Artificial systems can use representations and cognition to run activities related to constraints implemented by the designers or coming from the environment (a goal to reach being considered as a constraint to satisfy). In the second part of the presentation we define what are a meaningful information and a representation for a system submitted to a constraint in its environment, and we link these to the enactive approach. We define a meaningful information (a meaning) as an information generated by a system submitted to a constraint when it receives an external information that has a connection with the constraint. The meaning is precisely that connection. The meaning belongs to the interactions that link the system to its environment. The function of the meaning is to participate to the determination of an action that will be implemented in order to satisfy the constraint. (Menant, 2003). The satisfaction of the constraint goes with maintaining the nature of the system in its environment. A Meaning Generator System (MGS) is defined correspondingly. It is a building block for higher level systems. We present some characteristics of the MGS (groundings of a meaning, domain of efficiency and transfer of meanings, networking of meanings, evolutionary usage). The MGS approach is close to a simplified version of the Peircean triadic theory of signs (Menant, 2003, 2005 ). We define the representation of an item for a system as being the dynamic set of meaningful information corresponding to the item for the system in its environments (an elementary representation being made of a single meaningful information). These representations link the system to its environment by their meaningful components related to the nature of the system. These representations are different from the GOFAI ones. The possibilities for linking these notions of meaning and representation with the enactive approach come from the structure of the MGS: the need for an action is the cause of the meaning generation by and for the system. The action on the environment is for the system to maintain its nature (its identity). The MGS links together the generation of meaningful representations, the nature of the system, and the interactions with the environment. This can be considered as close to enacting a world by meaning generation (Di Paolo and all 2007), and to the enactive concept of sense making (De Jaegher, Di Paolo 2007). We propose that basing the definition of a representation on the notion of meaningful information generated by a system submitted to a constraint can open a way for making the notion of representation compatible with the enactive approach. In the third part of the presentation, we consider some cases of meaningful information and representations for organisms and for robots. Regarding organisms, the MGS can be used in an evolutionary context by looking at the evolution of the systems and of the constraints. Purpose is to modelize the generation of meanings and of representations in order to make available a tool usable for different levels of evolution, as evolution has a place in cognitive sciences (Proust, 2007). Constraints for basic life are survival constraints (individual and species). Group life constraints are also to be considered. Reaching the level of humans in evolution brings in new constraints that cannot be clearly identified as they have to take into account human consciousness which is today a mystery (the “hard problem”). On an evolutionary standpoint, human constraints come in addition to the ones existing for non human organisms. We can make some hypothesis on the nature of human constraints (Maslow pyramid based constraints, anxiety limitation…). For robots, the MGS is initially based on the design of the robot. The meaning generated within a robot is initially derived from the constraints implemented by the designer and from the environment. But some non calculable or non predictable evolutions of the robot can introduce meanings that look proper to the robot. This last point can be linked to the notion of autonomy in robots. In such examples, the dynamic management of meanings thru the MGSs in their environments keeps the link with the enactive approach. We finish the presentation by summarising the points addressed and by proposing several continuations. (shrink)

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

In this article I present an alternative philosophy of science based on ideas drawn from the study of complex adaptive systems. As a result of the spectacular expansion in scientific disciplines, the number of scientists and scientific institutions in the twentieth century, I believe science can be characterised as a complex system. I want to interpret the processes of science through which scientists themselves determine what counts as good science. This characterisation of science as a complex system can give an (...) answer to the question why the sciences are so successful in solving growing numbers of problems and correcting their own mistakes. I utilise components of complexity theory to explain and interpret science as a complex system. I first explain the concept of complexity in ordinary language. The explanation of science as a complex system starts with a definition of the basic rules that guide the behaviour of science as a complex system. Next, I show how various sciences result through the implementation of these rules in the study of a specific aspect of reality. The explanation of the growth of science through evolutionary adaptation and learning forms the core of the article. (shrink)

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

What is the relation between science and ideology? Are they incompatible, complementary or the same thing? Should science avoid "contamination" from ideology? Is there an only way to do science? Does anyone of them lead to the same results and give us the same worldview? We will focus on the figure of Alexander Bogdanov, Russian physician and philosopher, in order to discuss these and other relevant topics. His theories gave birth to what may be called later "the theory of two (...) sciences", which entitles this work. (shrink)

This monograph focuses on two major themes of current interest---those of complexity and emergence. Neither of the two concepts is, in the very nature of things, precisely defined or easily comprehended. Complexity is all around us while the sciences often analyze entities and events by making simplifications. But the fault lines in the latter get exposed over larger spans of space and time. Complexity entails emergence that involves discontinuity and novelty in the evolution of complex systems, based (...) on the appearance of distinctive spatial and temporal structures. Underlying an occurrence of emergence is an instability involving the transition from one stable regime to another, where distinct space-time scales acquire relevance across the transition. The structures on the two sides of the transition are characterized by distinct sets of state variables where, on one side of the transition, collective variables make their appearance. -/- Complexity and emergence are viewed in this book in the context of the Kantian noumenal-phenomenal divide. The noumenal reality is the repository of all the complexity that there is, while the phenomenal reality emerges from the noumenal in the process of our perception, interpretation, and inference. The noumenal exists in itself as a self-determined whole, and reveals itself to us in fragmentary patches as skewed projections that we assemble to form our phenomenal world that keeps on emerging ceaselessly. Our own biological evolution and the evolution of our phenomenal world occurs in a mutually consistent process, that can be referred to as co-evolution. -/- We seek regularities in the phenomenal world by means of our scientific theories which are often successful, but that success is fragile as, over larger spans of space and time, anomalies make their appearance, giving a lie to the simplicity and regularity previously presumed to have been unearthed. Simplicity, regularity, and harmony are only fleetingly located in the phenomenal world by means of our theories as these keep on being revised endlessly and often radically, thereby generating a mosaic of theories that becomes ever more complex, indicating a pervasive complexity of the noumenal world from which the phenomenal emerges by way of strictly limited projections. The foundational metaphysics on which this book is based tells us that reality is fundamentally complex, while tiny islands of simplicity, regularity, and harmony are embedded in it in isolated space-time domains. (shrink)

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

Since the outbreak of the Coronavirus disease 2019 (Covid-19), tremendous efforts have been made by scientists, health professionals, business people, politicians, and laypeople around the world. Covid-19 vaccines are one of the most crucial innovations that help fight against the virus. This paper attempts to revisit the Covid-19 vaccines production process by employing the serendipity-mindsponge-3D creativity management theory. Vaccine production can be considered an information process and classified into three main stages. The first stage involved the processes of absorbing (...) information (e.g., digital data and open science) and rejecting unhelpful information (e.g., misinformation and fake news) for effectively acquiring useful insights. Useful insights were later employed by experts, enterprises, governments, and international organizations through interdisciplinary coordinated efforts for developing vaccines within a short period. Finally, the appearance of multiple types of vaccines enabled more strategic options for vaccine distribution and administration. Findings from this vaccine creativity management process could be used as critical lessons for further improvements of vaccination programs. (shrink)

Comprehensive when it was published in 1990, this guide brought together information on the broad spectrum of education and research opportunities then available in the sciences of complexity. Its purpose was to make these kinds of investigations more accessible by providing information on programs, institutions, organizations, and literature where one can learn about their principles, methods, and applications. The guide was intended to help interested students and educators locate the various academic fields, departments, institutes, and programs that offer education (...) in systems thinking and complex systems research. The guide also served professionals who are already involved in the systems sciences as a directory to general activity in what are otherwise widely separate fields. By browsing the guide one can get a snapshot of where education and research in systems science was happening at the time in both North America and Europe. The landscape has since massively changed so its value is mostly historical. (shrink)

In computational complexity theory, decision problems are divided into complexity classes based on the amount of computational resources it takes for algorithms to solve them. In theoretical computer science, it is commonly accepted that only functions for solving problems in the complexity class P, solvable by a deterministic Turing machine in polynomial time, are considered to be tractable. In cognitive science and philosophy, this tractability result has been used to argue that only functions in P can feasibly (...) work as computational models of human cognitive capacities. One interesting area of computational complexity theory is descriptive complexity, which connects the expressive strength of systems of logic with the computational complexity classes. In descriptive complexity theory, it is established that only first-order (classical) systems are connected to P, or one of its subclasses. Consequently, second-order systems of logic are considered to be computationally intractable, and may therefore seem to be unfit to model human cognitive capacities. This would be problematic when we think of the role of logic as the foundations of mathematics. In order to express many important mathematical concepts and systematically prove theorems involving them, we need to have a system of logic stronger than classical first-order logic. But if such a system is considered to be intractable, it means that the logical foundation of mathematics can be prohibitively complex for human cognition. In this paper I will argue, however, that this problem is the result of an unjustified direct use of computational complexity classes in cognitive modelling. Placing my account in the recent literature on the topic, I argue that the problem can be solved by considering computational complexity for humanly relevant problem solving algorithms and input sizes. (shrink)

What is the relation between science and ideology? Are they incompatible, complementary or the same thing? Should science avoid “contamination” from ideology? Is there an only way to do science? Does anyone of them lead to the same results and give us the same worldview? We will focus on the figure of Alexander Bogdanov, Russian physician and philosopher, in order to discuss these and other relevant topics. His theories gave birth to what may be called later “the theory of two (...) sciences”, which entitles this work. (shrink)

‘Complexics’ denotes the meta-transdisciplinary field specifically concerned with giving us suitable cognitive tools to understand the world’s complexity. Additionally, the use of the adjective ‘complexical’ would avoid the common confusion caused by the adjective ‘complex’, which belongs to everyday usage and already has its own connotations of complication and confusion. Thus, ‘complexical’ thinking and ‘complexical’ perspective would provide clearer terms, be freer of confusion, and refer more precisely to epistemic elements in contrast to the ‘complexity’ typical of many (...) phenomena of reality. In short, the world would be ‘complex’, but our way of looking at the world would be ‘complexical’. As a meta-transdiscipline, ‘complexics’ would carry on the perspective of cybernetics: “Cybernetics deals with all forms of behaviour insofar as they are regular, or determinate, or reproducible. The materiality is irrelevant… The truths of cybernetics are not conditional on their being derived from some other branch of science. Cybernetics has its own foundations” (Ashby, 1956:1). Thus, it has a distinctly transdisciplinary mission to provide concepts, schema and possibilities of thinking and representation able to express the multidimensional and systemic interwovenness and interdependence of the many, highly significant phenomena of reality that have these characteristics. On the level of theory, complexics needs to provide a set of principles, concepts and conceptual landscapes that can be applied transversally to distinct areas of knowledge and phenomena of reality, enabling us to gain a much firmer grasp of the complex aspects of their existence than we currently have. For this reason, our aim needs to be, as Morin says, not “to reduce complexity to simplicity, [but] to translate complexity into theory” (1994:315). (shrink)

The philosophy of science has traditionally assumed that knowledge should be organized in the form of theories. From theories propositions can be deduced that can be tested in experiments. Most propositions deduced from theories take the form of if-then statements. For example, if variable A increases, what happens to variable B, assuming that all other variables are held constant? However, an alternative way of organizing knowledge, in the form of producer-product relationships, was proposed by the philosopher E.A. Singer, Jr. and (...) advocated by two of his students, C. West Churchman and Russell L. Ackoff. Whether to structure knowledge in the form of theories or methods is related to the question of whether there is a fundamental difference between the natural and the social sciences. As opposed to Karl Popper’s doctrine of the unity of method, this paper argues that structuring knowledge in the form of methods is appropriate in applied fields, particularly in management where a large part of the task is to achieve agreement among a group of knowing subjects on an appropriate set of actions. (shrink)

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

In this paper I suggest that several problems in the study of emotion depend on a lack of adequate analytical tools, in particular on the tendency of viewing the organism as a modular and hierarchical system whose activity is mainly constituted by strictly sequential causal events. I argue that theories and models based on this view are inadequate to account for the complex reciprocal influences of the many ingredients that constitute emotions. Cognitive processes, feelings and bodily states are so subtly (...) intertwined that it is not possible to determine which one "comes first" in a causal chain. The dynamical systems approach in cognitive science, I suggest, provides a more appropriate framework for the study of emotion. In particular, the notion of circular causation and collective action help depict the organism as a self-organising system in which emotion emerges as a function of its global activity. Among others, this dynamical perspective allows revising the popular notion of appraisal in a way that can dissolve some of the questions that have taunted emotion theorists thus far. (shrink)

A familiar dialogue is taking place in the professional literature of records and information management. Since the early 1990s, the theoretical foundation of a records management theory has been constructed on convergence (Pemberton & Nugent, 1995; Walters, 1995; Zawiyah M Yusof & Robert W Chell, 2002). While certain concepts are shared across disciplines, arguably the most foundational definition is the most divergent: a record. Each discipline (Archival Science, Library Science, Computer Science) defines the term record in its own (...) way. Records managers at all levels have difficulty espousing a universal definition of the term, while claiming sole responsibility for the authority, organization, authenticity, and sustainability of records. (shrink)

The philosophy of science has traditionally assumed that knowledge should be organized in the form of theories. From theories propositions can be deduced that can be tested in experiments. Most propositions deduced from theories take the form of if-then statements. For example, if variable A increases, what happens to variable B, assuming that all other variables are held constant? However, an alternative way of organizing knowledge, in the form of producer-product relationships, was proposed by the philosopher E.A. Singer, Jr. and (...) advocated by two of his students, C. West Churchman and Russell L. Ackoff. Whether to structure knowledge in the form of theories or methods is related to the question of whether there is a fundamental difference between the natural and the social sciences. As opposed to Karl Popper’s doctrine of the unity of method, this paper argues that structuring knowledge in the form of methods is appropriate in applied fields, particularly in management where a large part of the task is to achieve agreement among a group of knowing subjects on an appropriate set of actions. (shrink)

The book Theory and Practice of Contrast completes, corrects and integrates the foundations of science and humanities, which include: theory of art, philosophy (aesthetics, epistemology, ontology, axiology), cognitive science, theory of information, theory of complexity and physics. Through the integration of these distant disciplines, many unresolved issues in contemporary science have been clarified or better understood, among others: defining impact (contrast) and using this definition in different fields of knowledge; understanding what beauty/art is and what our aesthetic preferences depend (...) on; deeper understanding of what complexity and information are in essence, and providing their general definitions. Complexity means integration, value and goodness - concepts that seem to be neglected today. The book also has a high degree of integration/complexity, although each chapter introduces a new issue. The last chapter: "Binary Model of the Universe" draws attention to the need for including in physics the analysis of our mind and the resulting new possibilities, which include the mentioned (digital) model of the universe. Despite the difficult issues raised here, this study is written in accessible language and may be interesting not only for scientists and academics. (shrink)

Sumantra Ghoshal’s condemnation of “bad management theories” that were “destroying good management practices” has not lost any of its salience, after a decade. Management theories anchored in agency theory (and neo-classical economics generally) continue to abet the financialization of society and undermine the functioning of business. An alternative approach (drawn from a more classic institutional, new ecological, and refocused ethical approaches) is reviewed.

Understanding causation in social-ecological systems (SES) is indispensable for promoting sustainable outcomes. However, the study of such causal relations is challenging because they are often complex and intertwined, and their analysis involves diverse disciplines. Although there is agreement that no single research approach (RA) can comprehensively explain SES phenomena, there is a lack of ability to deal with this diversity. Underlying this diversity and the challenge of dealing with it are different causal reasonings that are rarely explicit. Awareness of hidden (...) assumptions is essential for understanding how the causal reasoning of an RA is constituted, and for promoting the integration, translation, or juxtaposition of different RAs. We identify the following elements as particularly relevant for understanding causal reasoning: methods, frameworks and theories, accounts of causation, analytical focus, and causal notions. We begin with the idea that one of these elements typically figures as an entry point to an RA. This entry point is particularly important because it generates a path dependence that orients causal reasoning. In a subsequent step, when an approach is applied, causal reasoning concretizes as a result of a particular constellation of the remaining elements. We come to these insights by studying the application of four different RAs to the same social-ecological case (the collapse of Baltic cod stocks in the 1980s). On the basis of our findings we developed a guide for the analysis of causal reasoning by raising awareness of the assumptions, key elements, and the relations between these key elements for a given RA. The guide can be used to elicit the causal reasoning of RAs, facilitate interdisciplinary collaboration, and support disclosure of ethical/political dimensions that underlie management/governance interventions that are formulated on the basis of causal findings of research studies. (shrink)

Main principles of the complex nonlinear thinking which are based on the notions of the modern theory of evolution and self-organization of complex systems called also synergetics are under discussion in this article. The principles are transdisciplinary, holistic, and oriented to a human being. The notions of system complexity, nonlinearity of evolution, creative chaos, space-time definiteness of structure-attractors of evolution, resonant influences, nonlinear and soft management are here of great importance. In this connection, a prominent contribution made to (...) system analysis and to a necessary reform of education and thinking by Edgar Morin is considered. (shrink)

This is a book about evolution from a post-Darwinian perspective. It recounts the core ideas of French philosopher Henri Bergson and his rediscovery and legacy in the poststructuralist critical philosophies of the 1960s, and explores the confluences of these ideas with those of complexity theory in environmental biology. The failings in the development of systems theory, many of which complex systems theory overcomes, are retold; with Bergson, this book proposes, some of the rest may be overcome too. It asserts (...) that Bergson’s ideas can further our understanding of evolution, and of complex systems, and aid the work of scientists working in the field of ecological complexity. See http://www.creativeemergence.info/ for more detail and sample chapter. "The claim is that Bergson's notions of duration and élan vital resonate with and provide interesting metaphysical speculations complementing a process structuralist biology of the Goodwin and Kauffman stripe. This is certainly provocative and worth further thought. The key claim is that the openness and unpredictability of systems "at the edge of chaos" (where a system can be said to "choose" at bifurcation points in its state space) meets the Bergsonian desideratum of an open universe that takes irreversible time and evolutionary difference seriously. In particular, Kreps stresses the way Bergson's insistence on the differentiating force of élan vital (which Kreps successfully defends from the charge of a substantialist vitalism along the lines of Hans Dreisch) puts him in line with those who see natural selection as a secondary pruning of a primary differentiation." -John Protevi, Notre Dame Philosophical Reviews 2016.01.20 'This is no ordinary introduction to Henri Bergson. What David Kreps' excellent study gives us is Bergson, complexe: first, because there is no simple way to take (or leave) Bergson's ideas - his thought of durée, élan, and 'multiplicity' demands the most subtle and nuanced reading to give them their full justice; and second, because only by intertwining his ideas with the most up-to-date research in systems thinking, complexity theory, and poststructuralism can we begin to understand their absolute contemporaneity. Kreps' work does all this and more: it gives us the Bergson we need for today.' -John Mullarkey, author of Bergson and Philosophy 'Kreps' book is a thoroughly researched and well-written work that shows how Bergson's philosophy of evolution and time can also reinvigorate our ideas about complexity and organization in the natural and physical sciences.' -Stephen Crocker, author of Bergson and the Metaphysics of Media. (shrink)

In the current age there exists a widespread and extremely negative opinion of humankind held almost everywhere. The prevailing theory and application in all of science and religion holds that 'human perception is deeply flawed'. In all of the established religions of the world human kind is somehow seen as fallen and in need of a powerful intervention and 'saving' from our frail natures. In the scientific community our limitations require external proofs to substantiate our assertions about nature. There lived (...) a philosopher, 350 years ago, who understood us far better than this. His name was Baruch Spinoza. Under his own formidable power of understanding of life he recognized our actual merit and inestimable worth. He shared that understanding in a book titled, the "Ethics". In it he details; not only the nature of God as adequately conceived, but also the existence and structure and functions of the human mind. He also wrote a short treatise which has come to be called the 'Fragment'. From the outset of the piece he set out his goal for us; to recognize "...the knowledge of the union existing between the mind and the whole of nature". This 'Fragment' holds the key to unlocking the complexity of the "Ethics". This key consists in grasping the nature of reality which comes to each of us in the form of the 'idea'. The idea resides within each human mind and serves as the nexus where human understanding and the objects in our world co-habit. In this little pamphlet, first in a proposed series of six, together we will discuss and illustrate his discovery of the mind. Most important for you dear friend a challenge will be extended; to discover within you the existence and nature of your own mind and to learn to harness its near unlimited power to understand and to manage our environment and the future. It will prove a lifelong challenge to reach the point where we clearly understand and dutifully accept the responsibility which we owe to all of our ancestors and to those who will follow us to recognize the human mind as the most sublime of all the elements which have evolved on the planet. Sincerely, Charles M. Saunders. (shrink)

Scientific management introduced a novel way of organizing work and measuring productivity into the modern workplace. With a stopwatch and a clever method of analysis, Frederick Winslow Taylor is either acclaimed or reviled, depending on the audience, for giving industrial/organizational consultancy a groundbreaking tool: the efficiency study. What is less well known is that the American pragmatist John Dewey criticized scientific management for its dualistic assumptions, for treating workers as pure doers or “muscle” and management as pure (...) thinkers or “brains” in an efficient, though inhumane, work process. The first section of this paper examines the similarities and differences between Dewey’s and Taylor’s respective conceptions of science and management. In the second section, I consider Dewey’s critique of scientific management in his book Democracy and Education. The paper concludes with some thoughts about the implications of Dewey’s critique of Taylorism for organizational theory and industrial relations today. (shrink)

As humans, we use the power of thinking to make scientific discoveries, develop technologies, manage social interactions, and transmit knowledge to the next generations. With the ability to think, we can trace back and discover the origin of the universe, the natural world, and ourselves. The content of this book, Mindsponge Theory, is part of that discovery process. -/- Product Details -/- Publisher ‏ : ‎ Walter de Gruyter (December 6, 2022) Publication date ‏ : ‎ December 6, 2022 Language (...) ‏ : ‎ English Hardcover ‏ : ‎ 214 pages ISBN-10 ‏ : ‎ 8367405145 ISBN-13 ‏ : ‎ 978-8367405140 Dimensions ‏ : ‎ 6.3 x 0.71 x 9.29 inches -/- . (shrink)

It is widely recognized that accurately identifying and classifying competitors is a challenge for many companies and entrepreneurs. Nonetheless, it is a paramount activity which provide valuable insights that affect a wide range of strategic decisions. One of the main challenges in competitor identification lies in the complex nature of the competitive relationships that arise in business envi- ronments. These have been extensively investigate over the years, which lead to a plethora of competition theories and frameworks. Still, the concept of (...) competition remains conceptually complex, as none of these approaches properly formalized their assumptions. In this paper, we address this issue by means of an ontological analysis on the notion of competition in general, and of business competition, in particular, leveraging theories from various fields, including Marketing, Strategic Management, Ecology, Psychology and Cognitive Sciences. Our analysis, the first of its kind in the literature, is grounded on the Unified Foundational Ontology (UFO) and allows us to formally characterize why competition arises, as well as to distinguish between three types of business competitive relationships, namely market-level, firm-level and potential competition. (shrink)

"All-and-Nought" is the 2nd Edition of a series of books that study the nature of Reality and Being. The first edition of the book, "The Metaphysics of All-and-None", was published by Edwin Mellen Press in January 2022; since then the book has been under severe investigations and reviews by many scholars and pundits worldwide. The 2nd edition of the book contains the original text plus a foreword by Professor Richard Howells from King’s College London and some reports by Physicists, Biologists, (...) and others. Journal of Mind, one the most important journals in Philosophy, paid attention to the book and in its critical review article October 2022, encourages all philosophers and pundits to study the book. -/- The book's core concept is a Scientific/Mathematical, a-priori/synthetic theory called All'n'None Theory. It studies the Symmetry and Asymmetry in Reality and points to a state that indicates the deep Symmetry between "All Beings" and "Nothingness". It deeply studies the relationships between being and non-being from the perspective of Symmetry and symmetry failure and offers a measurement tool and a new method of studding phenomena. -/- It seems All`n`None Theory is a scientific breakthrough and discovery. The book methodologically applies Physics, Conceptual philosophy, Mathematics, and jurisprudence analysis both inductively and deductively to prove its theory. It is the first and the only Theory for Explaining the nature of Existence (being) and provides a technique to measure the amount of existence in each being. -/- Through understanding Existence, the book explains Creation, Cosmos, Consciousness, Mind, Spirit, and Soul scientifically. It is deep enough to initially encompass and then merge all modern Sciences (Physics, Philosophy, Complexity, System Theory, Information Theory, Psychology, Biology, Humanities, Economy, and Management) with Buddhism, Hinduism, Judaism, Christianity, and Islam (Theology) and makes the image of Reality clearer for readers. -/- The book proves chaos management in the depth of creation and shows how people can use the only law of creation for their own success and salvation. Therefore the book is an inspirational one too. Readers by reading the book and understanding it not only gain scientific knowledge but also find profound wisdom that heals them. They feel they are enlightened and motivated to construct their meaning of life. Readers can recognize their self/ego structure of being and enjoy the passion to improve. -/- The book tries to explain the mechanism of Thinking with respect to Symmetry and the failure of Symmetry as the foundation of Categorization, Mentalization, and Cognition. By understanding the mechanism of cognition it can study Epistemology and explain the Believing Mechanism and the nature of Beliefs as a kind of being. The theory claims no matter the beliefs originated from Science or Religion the mechanisms that govern both of them are the same. Based on the theory, “Knower or Observer”, “Known or Observed”, and “knowing or observation” are rooted in Symmetry and its failure. The tone, language, and structure of the book are unique to itself. It is not an entertainment or commercial book. The book is written for readers who plan to spend enough time to think about its content. (shrink)

Purpose – With the growing demand for ethical standards in the prevailing business environment, ethical leadership has been under increasingly more focus. Based on the social exchange theory and social learning theory, this study scrutinized the impact of ethical leadership on the presentation of ethical conduct by employees through the ethical climate. Notably, this study scrutinized the moderating function of the person organization fit (P-O fit) in relation to ethical climate and the ethical conduct of employees. -/- Design/methodology/approach – To (...) evaluate the research hypotheses, two-wave data were collected from 295 individuals who are currently employed in various Iraqi organizations (i.e. manufacturing, medical, and insurance industries). -/- Findings – In line with the hypotheses, the outcomes from a sample of 295 workers working in different Iraqi entities exhibited a positive relationship between the ethical behavior of leaders and the ethical conduct of employees in the ethical climate. Moreover, it was observed that the P-O fit of employees moderated the relationship between ethical climate and the ethical conduct of employees such that the relationship was more robust for those with a high P-O fit in comparison to those with a low P-O fit. -/- Research limitations/implications – The primary limitation of this study is in the data, which was obtained from a single source. Although the study conducted two surveys and utilized a mediation and moderation variables model that was less likely to be influenced by common method bias (CMB) (Podsakoff et al., 2012), one cannot completely rule out CMB. Apart from the potential effects of the CMB, the consistency of the empirical findings could have also been compromised since self-reported data were utilized in measuring ethical behavior, which can be a very complex and sensitive issue. For this reason, the social desirability response bias cannot be ruled out completely. When possible, future studies must gather data from multiple sources. Furthermore, supervisors must evaluate the ethical behavior of employees. Another limitation was that the findings of this study were based on a sample in a Middle Eastern cultural context such as in Iraq. Perhaps, the particular cultural features of this context, which encompassed, among other things, strong adherence to religious values (Moaddel, 2010), could have influenced the findings of this study. It is true that the effects of differences (P–O fit) are highly likely to replicate across cultural contexts (Triandis et al., 1988). However, it can be seen that further studies are needed to evaluate the context-sensitivity of these findings (Whetten, 2009) by analyzing other cultures, where the importance of religiosity is on the decline (i.e. in Western countries, Ribberink et al., 2018) or where the cultural features are very much different from those that apply to Iraq. Lastly, other external factors were not taken into account by this study as it tried to explain ethical behavior. Ethics is a highly complex subject and is influenced by numerous variables at the organizational, individual, and external environment levels. Thus, caution must be observed when making inferences from the present study which, to a certain degree, offered a simplified version of ethical behavior by concentrating on a few variables such as the Arab culture’s traditional ideology, which dominates even science (Abu Khalil, 1992). In addition, there are political conflicts in the Middle Eastern cultural context such as what is happening in Iraq (Harff, 2018). Thus, it is important to include such aspects in future researches. -/- Practical implications – In terms of management, the findings send a clear signal to those in the upper echelon that, without ignoring the issue of ethics in organizations, employees are a critical aspect to be taken into account to encourage ethical behavior at the workplace. This study has important practical implications. First, this study determined that ethical leadership (here, of the supervisors) positively influences the behavior of subordinates (refers to the supervisors here); this, in turn, further improves the ethical behavior of employees. It is vital that managers or supervisors are motivated to practice ethical leadership because they directly influence the employees. It has been suggested that top managers, especially chief executive officers, have the ability to shape the ethical climate, which also influences the ethical behavior of employees further. This study offered several feasible ways that managers can apply to achieve that. In particular, top managers may utilize the ethical climate as a way of communicating the ethical values that they have to their subordinates, thereby serving as a motivation for the subordinates to adopt ethical behavior. It was also suggested by this study that ethical climate and the P–O fit may, to some degree, substitute each other as they influence the ethical behavior of employees. Therefore, firms that were identified to have a low level of ethical standards, practices, and policies, at least from the employees’ perspective, are better poised to conduct ethical issues in order to construct the ethical behavior of their subordinates. More importantly, it is highly essential that the value congruence between an organization and its followers be considered. -/- Social implications – This study highlighted the notion of ethics and how it’s essential for society. Ethics refers to the norms, standards, and values that direct the behavior of an individual. Ethical behavior is vital in society because we need to be treated with respect as human beings. -/- Originality/value – This study responds to recent calls for more research to identify factors that may strengthen or mitigate the influence of ethical behavior in the workplace such as ethical leadership, ethical climate, and Person–Organization. (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 Nature of Free Will and Its Dependence on 100 Percent Correct Information -/- Free will has long been regarded as one of the most profound and complex concepts in philosophy, psychology, and science. It is the capacity of individuals to make choices that are not predetermined by prior causes or external constraints. While the debate surrounding the nature of free will continues, one fundamental truth stands out: free will cannot operate effectively without access to 100 percent correct information (...) or knowledge. Without accurate understanding, decision-making becomes flawed, leading to choices that are neither truly free nor aligned with natural laws. -/- The Role of Information in Free Will -/- Free will is intrinsically tied to decision-making. Every choice we make is shaped by the information we possess. This information serves as the foundation for our understanding of potential outcomes, risks, and benefits. If the information is incomplete, misleading, or outright false, our ability to make rational and informed decisions is compromised. In essence, the “freedom” of our will becomes restricted by ignorance or deception. -/- For instance, if an individual is unaware of the harmful effects of a particular behavior or decision, they may unknowingly choose a path that leads to suffering. Conversely, with complete and accurate knowledge, individuals can weigh their options effectively, leading to choices that are consistent with their values and the universal law of balance in nature. -/- The Universal Law of Balance and Knowledge -/- The concept of the universal law of balance emphasizes harmony within natural systems. This law suggests that every decision, whether individual or collective, must align with the inherent order of the universe. When decisions are made based on false beliefs, unproven theories, or misinformation, they disrupt this balance, creating disharmony and unintended consequences. -/- Correct knowledge serves as the compass that guides human actions within the framework of natural balance. It ensures that decisions are grounded in reality, minimizing the risks of imbalance. Without it, free will operates in a vacuum of uncertainty, where the likelihood of errors increases exponentially. -/- Ignorance: The Antithesis of Free Will -/- Ignorance is the greatest threat to free will. It shackles the mind, preventing individuals from recognizing the full spectrum of choices available to them. When people lack access to truthful information, they are vulnerable to manipulation, prejudice, and irrational fears. In such scenarios, their decisions are not truly free but are instead dictated by external forces or internal misconceptions. -/- A classic example of this can be seen in societal issues such as propaganda or misinformation campaigns. When individuals base their choices on fabricated narratives, their actions may perpetuate harm rather than promoting balance or well-being. Only by addressing the root cause—ignorance—can free will be restored to its rightful state. -/- The Role of AI and AGI Technology in Supporting Free Will -/- In the modern era, artificial intelligence (AI) and artificial general intelligence (AGI) technologies have emerged as critical tools for addressing the limitations of the human brain in processing and synthesizing vast amounts of information. The biological brain, while capable of remarkable feats, is inherently limited in its capacity to handle the exponential growth of data in today’s interconnected world. -/- AI and AGI can support the biological brain by efficiently analyzing, filtering, and synthesizing enormous volumes of data to identify accurate, relevant, and actionable knowledge. By doing so, these technologies can provide individuals with the correct information needed to make informed decisions that align with the universal law of balance. -/- For example, AI systems can help identify patterns and correlations within complex systems—whether in healthcare, environmental management, or societal governance—that may otherwise be overlooked by human cognition. AGI, with its advanced problem-solving capabilities, could go even further by integrating diverse sources of knowledge and offering insights that promote harmony within both internal (psychological) and external (societal and environmental) systems. -/- Moreover, AI and AGI can act as safeguards against misinformation and bias, which are major barriers to free will. By detecting and correcting false narratives or flawed reasoning, these technologies can ensure that individuals are empowered with the truth, thereby preserving their capacity for rational, autonomous decision-making. -/- The Educational Imperative -/- To safeguard the integrity of free will, education must play a pivotal role. A holistic educational system that emphasizes critical thinking and the pursuit of truth is essential. By teaching individuals to evaluate evidence, question assumptions, and seek out accurate information, society can empower its members to exercise their free will responsibly. -/- The integration of AI and AGI into the educational system further enhances this process. These technologies can personalize learning experiences, provide real-time feedback, and ensure that students have access to the most accurate and up-to-date information. Additionally, AI can help educators identify gaps in understanding and address them effectively, fostering a generation of critical thinkers who are equipped to maintain balance in their decisions. -/- Conclusion -/- Free will, while an inherent aspect of human existence, is not an absolute or isolated phenomenon. Its true nature is dependent on the quality of information available to individuals. Only with 100 percent correct knowledge can free will function as a tool for creating balance, harmony, and progress. Ignorance, by contrast, distorts this capacity, leading to decisions that undermine both individual well-being and societal stability. -/- The advent of AI and AGI technologies represents a transformative opportunity to support the biological brain in navigating the complexities of the modern world. By synthesizing vast amounts of correct information and providing actionable insights, these technologies ensure that free will operates within the framework of natural balance. -/- By prioritizing truth, fostering education, and leveraging advanced technologies, humanity can unlock the full potential of free will. In doing so, we move closer to a world where every choice is a reflection of both freedom and wisdom—an ideal state where the universal law of balance is preserved. -/- . (shrink)

The existential insecurity of human beings has induced them to create protective spheres of symbols: myths, religions, values, belief systems, theories, etc. Rationality is one of the key factors contributing to the construction of civilisation in technical and symbolic terms. As Hankiss (2001) has emphasised, protective spheres of symbols may collapse – thus causing a profound social crisis. Social and political transformations had a tremendous impact at the end of the 20th century. As a result, management theories have been (...) revised in order to deal with transition and uncertainty. Francis Fukuyama's (2000) approach is supportive of hierarchical organisation as the best solution when facing a 'disruption'. The notion of Homo Hierarchicus has been based on, allegedly, rational presumptions. This paper contributes to the discussion on hierarchy within contemporary organisations. It criticises so-called 'natural' and 'rational' necessities justifying hierarchy. A key issue identified by the paper is the formalisation of language in claiming value-free knowledge and 'detached' observation as the basis for neutral rationality and aspired efficiency. This should be seriously reconsidered as hindering rather than aiding understanding of social complexity. All in all, Homo Hierarchicus appears to be misleading rather than helping symbolic sphere or construct. (shrink)

F.A. Hayek argued that the sciences of complex phenomena, including (perhaps especially) economics, are limited to incomplete “explanations of the principle” and “pattern predictions.” According to Hayek, these disciplines suffer from (what I call) a data problem, i.e., the hopelessness of populating theoretical models with data adequate to full explanations and precise predictions. In Hayek’s terms, explanations in these fields are always a matter of “degree.” However, Hayek’s methodology implies a distinct theory problem: theoretical models of complex phenomena may be (...) underspecified so that, even were all the data available, a full explanation could not be inferred from the model. Where the sciences of complex phenomena are subject to both the data and theory problems, explanations and predictions will be of even lesser “predictive degree.” The paper also considers how to interpret Hayek’s claim that pattern predictions are falsifiable. (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)

"Complexity" is a catchword of certain extremely popular and rapidly developing interdisciplinary new sciences, often called accordingly the sciences of complexity. It is often closely associated with another notably popular but ambiguous word, "information"; information, in turn, may be justly called the central new concept in the whole 20th century science. Moreover, the notion of information is regularly coupled with a key concept of thermodynamics, viz. entropy. And like this was not enough it is quite usual to add (...) one more at present extraordinarily popular notion, namely chaos, and wed it with the abovementioned concepts. It is my aim in this paper to critically analyse this conceptual mess from a logical and philosophical point of view concentrating on the concepts of complexity and information and the question concerning the true relation between them. (shrink)

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

From his earliest work forward, phenomenologist Maurice Merleau-Ponty attempted to develop a new ontology of nature that would avoid the antinomies of realism and idealism by showing that nature has its own intrinsic sense which is prior to reflection. The key to this new ontology was the concept of form, which he appropriated from Gestalt psychology. However, Merleau-Ponty struggled to give a positive characterization of the phenomenon of form which would clarify its ontological status. Evan Thompson has recently taken up (...) Merleau-Ponty’s ontology as the basis for a new, “enactive” approach to cognitive science, synthesizing it with concepts from dynamic systems theory and Francisco Varela’s theory of autopoiesis. However, Thompson does not quite succeed in resolving the ambiguities in Merleau-Ponty’s account of form. This article builds on an indication from Thompson in order to propose a new account of form as asymmetry, and of the genesis of form in nature as symmetry-breaking. These concepts help us to escape the antinomies of Modern thought by showing how nature is the autoproduction of a sense which can only be known by an embodied perceiver. (shrink)