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

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

In this article, network science is discussed from a methodological perspective, and two central theses are defended. The first is that network science exploits the very properties that make a system complex. Rather than using idealization techniques to strip those properties away, as is standard practice in other areas of science, network science brings them to the fore, and uses them to furnish new forms of explanation. The second thesis is that network representations are particularly helpful in explaining (...) the properties of non-decomposable systems. Where part-whole decomposition is not possible, network science provides a much-needed alternative method of compressing information about the behavior of complex systems, and does so without succumbing to problems associated with combinatorial explosion. The article concludes with a comparison between the uses of network representation analyzed in the main discussion, and an entirely distinct use of network representation that has recently been discussed in connection with mechanistic modeling. (shrink)

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

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

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

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

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

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

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

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

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

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

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

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

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)

Several complexity theorists draw a sharp and ontologically robust distinction between (merely) complicated systems and (genuinely) complex systems. I argue that this distinction does not hold. Upon fine-grained analysis, ostensibly complicated systems turn out to be complex systems. The purported boundary between the complicated and the complex appears to be vague rather than sharp. Systems are complex by degrees.

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

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

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

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

‘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)

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)

Emergence is much discussed by both philosophers and scientists. But, as noted by Mitchell (2012), there is a significant gulf; philosophers and scientists talk past each other. We contend that this is because philosophers and scientists typically mean different things by emergence, leading us to distinguish being emergence and pattern emergence. While related to distinctions offered by others between, for example, strong/weak emergence or epistemic/ontological emergence (Clayton, 2004, pp. 9–11), we argue that the being vs. pattern distinction better captures what (...) the two groups are addressing. In identifying pattern emergence as the central concern of scientists, however, we do not mean that pattern emergence is of no interest to philosophers. Rather, we argue that philosophers should attend to, and even contribute to, discussions of pattern emergence. But it is important that this discussion be distinguished, not conflated, with discussions of being emergence. In the following section we explicate the notion of being emergence and show how it has been the focus of many philosophical discussions, historical and contemporary. In section 3 we turn to pattern emergence, briefly presenting a few of the ways it figures in the discussions of scientists (and philosophers of science who contribute to these discussions in science). Finally, in sections 4 and 5, we consider the relevance of pattern emergence to several central topics in philosophy of biology: the emergence of complexity, of control, and of goal-directedness in biological systems. (shrink)

The possibility of social and technological collapse has been the focus of science fiction tropes for decades, but more recent focus has been on specific sources of existential and global catastrophic risk. Because these scenarios are simple to understand and envision, they receive more attention than risks due to complex interplay of failures, or risks that cannot be clearly specified. In this paper, we discuss the possibility that complexity of a certain type leads to fragility which can function as (...) a source of catastrophic or even existential risk. The paper first reviews a hypothesis by Bostrom about inevitable technological risks, named the vulnerable world hypothesis. This paper next hypothesizes that fragility may not only be a possible risk, but could be inevitable,and would therefore be a subclass or example of Bostrom’s vulnerable worlds. After introducing the titular fragile world hypothesis, the paper details the conditions under which it would be correct, and presents arguments for why the conditions may in fact may apply. Finally, the assumptions and potential mitigations of the new hypothesis are contrasted with those Bostrom suggests. (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)

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)

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

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)

Achieving Coherence introduces a transformative framework for understanding and managing the complexities of dynamic systems. In a world where uncertainty and interconnected challenges define the landscape, the SPARC framework (Spectrum of Possibility and Recursive Choice) offers a unified model to address these issues, bridging the gap between theory and application across disciplines. This work explores the principles of coherence, constraint satisfaction, and recursive feedback, shedding light on how systems maintain stability and adapt in the face of evolving constraints and environmental (...) noise. By integrating dimensional transitions, multi-constraint optimization, and resilience to stochastic influences, SPARC provides a novel approach to modeling systems that are inherently complex and interdependent. Applications range from stabilizing critical infrastructure and managing ecosystems under stress to advancing artificial intelligence and optimizing multi-agent systems. By demonstrating how local behaviors interact with global dynamics and how systems can thrive under variability, Achieving Coherence presents a compelling vision for navigating complexity and driving innovation in an interconnected world. (shrink)

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

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)

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

We have recently started to understand that fundamental aspects of complex systems such as emergence, the measurement problem, inherent uncertainty, complex causality in connection with unpredictable determinism, time­irreversibility and non­locality all highlight the observer's participatory role in determining their workings. In addition, the principle of 'limited universality' in complex systems, which prompts us to search for the appropriate 'level of description in which unification and universality can be expected', looks like a version of Bohr's 'complementarity principle'. It (...) is more or less certain that the different levels of description possible of a complex whole ­­ actually partial objectifications ­­ are projected on to and even redefine its constituent parts. Thus it is interesting that these fundamental complexity issues don't just bear a formal resemblance to, but reveal a profound connection with, quantum mechanics. Indeed, they point to a common origin on a deeper level of description. (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 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)

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

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)

Against the concept that objects are defined by their self-contained essence – “thing-in-themselves” – Husserl and Foucault claim they are defined by intersubjectivity or social institutions. I argue that biological and even physical (complex) systems can constitute the unity and meaning of objects.

Landgrebe and Smith (2022) have recently offered an important mathematical argument against the possibility of Artificial General Intelligence (AGI): human intelligence is a complex system; complex systems have some properties that cannot be modelled mathematically; hence we have no viable way to build an AI that would be able to emulate human intelligence. The issue of complexity is thus at the heart of the Landgrebe and Smith approach, and they tackle this issue by postulating a set of (...) conditions, derived from mathematics, for a system to be complex. I argue that Hayek’s “Theory of Complex Phenomena” offers an interesting alternative path to understanding what makes complex systems complex. This offers an argument complementary to that of Landgrebe and Smith, which approaches the issue of complexity from an ontological angle, and which is designed to show that AI systems are not the right kind of entity that may interact meaningfully with complex systems. (shrink)

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

Complex logic is a novel logical framework, which formalizes the semantics of the categories of matter, space, and time in a system of logic that operates with complex logical objects. A complex logical object represents a superposition of a logical statement and its logical negation positioning any statement co-relatively to its logical negation. In the system of logical notations, where S is a logical statement and Not S is its logical negation, complex logic includes (...) co-relative logical positions of S and Not S with the probabilities of their truth within the scale 0... 1, excluding the boundary values of 0 and 1, into a single logical superposition. Thus, the logical superposition, summing up the probabilistic positions of S and Not S, has an invariant truth value equal to 1. In this context, complex logic proves to be invariant to reality, offering a unified logical interpretation of reality, spanning from quantum to cosmological scales. By defining the nature of matter and reality as a complex two-format processual essence with corresponding logical consequences, complex logic enhances our concept and understanding of reality. (shrink)

This article attempts to show why an experimental hypothesis is plausible and merits testing; in brief, the hypothesis is that autism begins with a failure in early learning and that changing the environment of early learning would dramatically change its incidence. Strong statistical evidence supporting this hypothesis has been published by Waldman et al., but to date this evidence has largely been ignored, perhaps because it challenges prevalent beliefs about autism. This article also suggests that the current epidemic of autism (...) is serious enough, and intellectually mysterious enough, to merit attention from a wider community of brain scientists: New ideas are needed. A confirmation of this article’s hypothesis would have interesting implications for brain science. (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)

Historical processes exert an influence on the current state and evolution of situations of language contact, brought to bear from different domains, the economic and the political, the ideological and group identities, geo-demographics, and the habits of inter-group use. Clearly, this kind of phenomenon requires study from a complexical and holistic perspective in order to accommodate the variety of factors that belong to different levels and that interrelate with one another in the evolving dynamic of human languaging. Therefore, there is (...) a need for the restricted and general complexity approaches to come to a meeting of the minds, and take steps toward a mutual integration based on the acceptance of the shortcomings of each approach, achieving progress through a non-contradictory complementarity of perspectives. It must be conceded that the practical and methodological applications of basic complexical ideas need to be developed much farther in order to apply them to specific research. At the same time, the limits of complex adaptive systems as computational strategies must be accepted in the pursuit of a better understanding of the dynamic and evolutionary processes typical of human beings. New tools for the conception, apprehension and treatment of the data will need to be devised to complement existing ones and to enable us to make headway toward practices that better fit complexical perspectives. It seems obvious that human complexics must be seen as multi-methodological, insofar as necessary combining quantitative-computation methodologies and more qualitative methodologies aimed at understanding the historical mental and emotional world of people. (shrink)

A general mathematical framework, based on countable partitions of Natural Numbers [1], is presented, that allows to provide a Semantics to propositional languages. It has the particularity of allowing both the valuations and the interpretation Sets for the connectives to discriminate complexity of the formulas. This allows different adequacy criteria to be used to assess formulas associated with the same connective, but that differ in their complexity. The presented method can be adapted potentially infinite number of connectives and truth values, (...) therefore, it can be considered a general framework to provide semantics to several of the known logic systems (eg, LC, L3 LP, FDE). The presented semantics allow to converge to different standard semantics if the separation complexity procedure is annulled. Therefore, it can be understood as a framework that allows greater precision (in complexity terms) with respect to formula satisfaction. Naturally, because of how it is built, it can be incorporated into non-deterministic semantics. The presented procedure also allows generating valuations that grant a different truth value to each formula of propositional language. As a positive side effect, our method allows a constructive proof of the equipotence between N and N^n for all Natural n. (shrink)

This article explores the relationship between deliberative democracy, the Internet, and systems theory’s thoughts on diversity. After introducing Habermas’s theory of deliberative democracy and how diversity fits into it, the article discusses various ideas about whether and how it could work on the Internet. Next, the article looks at research into diversity done in the field of complex adaptive systems, showing that diversity has both good and bad effects, but is clearly preferred for the purpose of survival. The article (...) concludes with an analysis of how the results of systems theory’s study of diversity can assist society in bringing democracy to the Web. (shrink)

Complex systems are difficult to manage, operate and maintain. This is why we see teams of highly specialised engineers in industries such as aerospace, nuclear and subsurface. Condition based monitoring is also employed to maximise the efficiency of extensive maintenance programmes instead of using periodic maintenance. A level of automation is often required in such complex engineering platforms in order to effectively and safely manage them. Advances in Artificial Intelligence related technologies have offered greater levels of automation but (...) this potentially pivots the weight of decision making away from the operator to the machine. Implementing AI or complex algorithms into a platform can mean that the Operators’ control over the system is diminished or removed altogether. For example, in the Boeing 737 Air Max Disaster, AI had been added to a platform and removed the operators’ control of the system. This meant that the operator could not then move outside the extremely reserved, algorithm defined, “envelope” of operation. This paper analyses the challenges of AI driven condition based monitoring where there is a potential to see similar consequences to those seen in control engineering. As the future of society becomes more about algorithm driven technology, it is prudent to ask, not only whether we should implement AI into complex systems, but how this can be achieved ethically and safely in order to reduce risk to life. (shrink)

This chapter introduces a research-based conceptual framework for the study of the inner psychosocial reality of business enterprises. It is called the Inner Organizational Ecosystem Approach (IOEA). This model is systemic in nature, and it defines the basic features of small and medium-size enterprises, such as elements, structures, borders, social actors, organizational climate, processes and resources. Further, it also covers the dynamics of psychosocial reality, processes, emergent qualities and the higher-order subsystems of the overall organizational ecosystem, including the global business (...) environment, which is understood as a macro-system where all the individual organizational ecosystems co-exist. In the applied part of the chapter, cognitive changes emerging within systemic leadership training are defined. Participation in systemic training causes changes in the cognitive processing of reality, more specifically improvements in layer-based framing, relativistic contextual orientation, temporality drift and meaning generation. All of these changes are components of the systemic mind, which is a concept newly proposed and defined by the present study. The systemic mind is a living matrix that is extremely open to acquiring new skills and new patterns of thinking, analyzing and meaning generation. It is processual and it can be considered as an ongoing process of continuous absorption of new cognitive patterns. Both the Inner Organizational Ecosystem Approach and the concept of the systemic mind provide a new theoretical background for empirical investigation in the fields of systemic and systems psychology, complexity psychology, organizational psychology, economic anthropology and the social anthropology of work. (shrink)

Complexity is a relatively new field of study that is still heavily influenced by philosophy. However, with the advent of modern computing, it has become easier to conduct thorough investigations of complex systems using computational simulations. Despite significant progress, there remain certain characteristics of complex systems that are difficult to comprehend. To better understand these features, information can be applied using simple models of complex systems. The concepts of Shannon's information theory, Kolgomorov complexity, and logical depth are (...) helpful in this regard. Computational mathematical models based on recursive processes, recursive functions, and information in functions offer new insights into complexity by exploring its connection with irreversibility and the loss of information. Through these models, it is possible to see complexity as a transformation of input information through algorithmic computation, with time, logical depth, and information loss serving as key factors in understanding the process. (shrink)