The complex systems approach (CSA) to characterizing cognitive function is purported to underlie a conceptual and methodological revolution by its proponents. I examine one central claim from each of the contributed papers and argue that the provided examples do not justify calls for radical change in how we do cognitive science. Instead, I note how currently available approaches in ‘‘standard’’ cognitive science are adequate (or even more appropriate) for understanding the CSA provided examples.

Despite the close connection between the central limit theorem and renormalization group (RG) methods, the latter should be considered fundamentally distinct from the kind of probabilistic framework associated with statistical mechanics, especially the notion of averaging. The mathematics of RG is grounded in dynamical systems theory rather than probability, which raises important issues with respect to the way RG generates explanations of physical phenomena. I explore these differences and show why RG methods should be considered not just calculational tools but (...) the basis for a physical understanding of complex systems in terms of structural properties and relations. (shrink)

Using the concept of adjunction, for the comprehension of the structure of a complex system, developed in Part I, we introduce the notion of covering systems consisting of partially or locally defined adequately understood objects. This notion incorporates the necessary and sufficient conditions for a sheaf theoretical representation of the informational content included in the structure of a complex system in terms of localization systems. Furthermore, it accommodates a formulation of an invariance property of information communication (...) concerning the analysis of a complex system. (shrink)

"The question of complex systems is relatively new for critics today. Analyzing complex systems in Renaissance texts shows that the Christian kabbalistical concept of harmonia mundi led to an aesthetical development, reflecting the worldview of harmonious parallel worlds. Failure to perceive the esoteric text uniting apparently contradicting themes has often led Renaissance scholars to elaborate a theory of the instability of atmospheres characterizing the English Baroque. This article gives an example of a complex system in Shakespeare's (...) Antony and Cleopatra revealing that the Christian kabbalistical Hercules unifies contradicting metaphors and intertexts of the play, and reintegrates this work in a form of semantic stability more in harmony with the aesthetics of its time. In the case of postmodern literature, contradictory intertexts appear as a device to deconstruct all values and logic previously established. With the study of a text structured by the optical questions around the refraction of light, this paper shows that in the case of Nabokov, the unifying system of apparently contradicting semantic systems is the underlying logic of scientific theory. In Nabokov's text, failing to perceive the fundamental intertext of scientific debate leads one to assimilate Nabokov's writing to a form of postmodern deconstruction. Both cases studied tend to show a continuity of a form of aesthetics through different epistemological eras.". (shrink)

Containing selected papers on the fundamentals and applications of Complexity Science, this multi-disciplinary book presents new approaches for resolving complex issues that cannot be resolved using conventional mathematical or software models. Complex Systems problems can occur in a variety of areas such as physical sciences and engineering, the economy, the environment, humanities and social and political sciences. Complexity Science problems, the science of open systems consisting of large numbers of diverse components engaged in rich interaction, can occur in (...) a variety of areas such as physical sciences and engineering, the economy, the environment, humanities and social and political sciences. The global behaviour of these systems emerges from the interaction of constituent components and is unpredictable but not random. The key attribute of Complex Systems is the ability to self-organise and adapt to unpredictable changes in their environment. (shrink)

The arts are one of the most complex of human endeavours, and so it is fitting that a special issue on Complex Systems in Aesthetics and Arts is being published. As the editors of this special issue, we would like to thank the reviewers of the submitted papers for their hard work in making this issue possible, as well as the authors who submitted their work and were very responsive to the comments of the reviewers and editors.

Regularities in natural language systems, despite their cognitive advantages in terms of storage and learnability, often coexist with exceptions, raising the question of whether and why irregularities survive. We offer a complex system perspective on this issue, focusing on the irregular past tense forms in English. Two separate processes affect the overall regularity: new verbs constantly entering the vocabulary in the regular form at low frequency, and transitions in both directions (from irregular to regular and vice-versa) occurring in (...) a narrow frequency range. The introduction of new verbs leads to an increase in regular types, that, entering at low frequencies, have a small impact on the perceived irregularity in terms of tokens. The frequency of usage acts as a control parameter, the majority of verbs types being fully-regular(irregular) at low(high) frequencies, with no evidence of irregularity facing extinction. Very few verbs types in an intermediate frequency region exhibit both regular and irregular forms at the same time, suggesting that the coexistence is unstable. The observed pattern of usage showing an abrupt change in response to small variations of the control parameter only appears in agent-based models provided that the word state is non-binary. By introducing this key ingredient, high-frequency irregular past-tense can survive the tendency to regularize over time, as observed in natural languages. (shrink)

Complex systems are dynamic and may show high levels of variability in both space and time. It is often difficult to decide on what constitutes a given complex system, i.e., where system boundaries should be set, and what amounts to substantial change within the system. We discuss two central themes: the nature of system definitions and their ability to cope with change, and the importance of system definitions for the mental metamodels that we (...) use to describe and order ideas about system change. Systems can only be considered as single study units if they retain their identity. Previous system definitions have largely ignored the need for both spatial and temporal continuity as essential attributes of identity. After considering the philosophical issues surrounding identity and system definitions, we examine their application to modeling studies. We outline a set of five alternative metamodels that capture a range of the basic dynamics of complex systems. Although Holling’s adaptive cycle is a compelling and widely applicable metamodel that fits many complex systems, there are systems that do not necessarily follow the adaptive cycle. We propose that more careful consideration of system definitions and alternative metamodels for complex systems will lead to greater conceptual clarity in the field and, ultimately, to more rigorous research. (shrink)

The idea that democracy is under threat, after being largely dormant for at least 40 years, is looming increasingly large in public discourse. Complex systems theory offers a range of powerful new tools to analyse the stability of social institutions in general, and democracy in particular. What makes a democracy stable? And which processes potentially lead to instability of a democratic system? This paper offers a complex systems perspective on this question, informed by areas of the mathematical, (...) natural, and social sciences. We explain the meaning of the term 'stability' in different disciplines and discuss how laws, rules, and regulations, but also norms, conventions, and expectations are decisive for the stability of a social institution such as democracy. (shrink)

Traditional modes of system representation as dynamical systems, involving fixed sets of states together with imposed dynamical laws, pertain only to a meagre subclass of natural systems. This reductionistic paradigm leaves no room for final causes; constrained thus are the simple systems. Members of their complementary collection, natural systems having mathematical models that are not dynamical systems, are the complex systems. Complex systems, containing hierarchical cycles in their entailment networks, can only be approximated and simulated, locally and (...) temporarily, by simple ones. Anticipatory systems are, in this specific sense, complex, hence this introductory chapter on Complex Systems in the Handbook of Anticipation. (shrink)

Complex systems are pervasive in the world around us. Making sense of a complex system should require that a person construct a network of concepts and principles about some domain that represents key (often dynamic) phenomena and their interrelationships. This raises the question of how expert understanding of complex systems differs from novice understanding. In this study we examined individuals' representations of an aquatic system from the perspective of structural (elements of a system), behavioral (...) (mechanisms), and functional aspects of a system. Structure–Behavior–Function (SBF) theory was used as a framework for analysis. The study included participants from middle school children to preservice teachers to aquarium experts. Individual interviews were conducted to elicit participants' mental models of aquaria. Their verbal responses and pictorial representations were analyzed using an SBF‐based coding scheme. The results indicated that representations ranged from focusing on structures with minimal understanding of behaviors and functions to representations that included behaviors and functions. Novices' representations focused on perceptually available, static components of the system, whereas experts integrated structural, functional, and behavioral elements. This study suggests that the SBF framework can be one useful formalism for understanding complex systems. (shrink)

In this article we analyze in a new way the epistemological concept of mythical explanation. It is shown, within the framework of the theory of dynamic and complex systems, that this kind of explanation is grounded on the substitution of distributed causation by lineal and single causes. Considering four examples, we show which mechanism is operating in that substitution. The first one concerns a computational implementation of a racial segregation model. The second one will be the analysis of an (...) imaginary panic. The third one starts with the theory, developed by René Girard, concerning sacrifice rituals and the emergence of scapegoats. Finally, the fourth one is based on the introduction of the imitation mechanism as an explanation for the financial markets behavior. (shrink)

Nowadays, the development of big data is getting faster and faster, and the related research on motion sensing recognition and complex systems under the background of big data is gradually being valued. At present, there are relatively few related researches on vertical Baduanjin in the academic circles; research in this direction can make further breakthroughs in motion sensor recognition. In order to carry out related action recognition research on the lifting action of vertical Baduanjin, this paper uses sensor technology (...) to collect the motion video image of vertical Baduanjin based on the background of big data and uses action recognition technology and related algorithms to obtain the action. Recognize the video image to obtain the data, get the acceleration, angular velocity, and EMG data, and count the end time and duration according to the change of the action. According to the data table and graph change trend compiled at the end of the experiment, we can see the following: after the data is preprocessed, the acceleration signal change range is limited to [−1, 1], and the acceleration change has a clear directionality; and, after 15 lifts of the detected object, its angular velocity in X-axis direction is basically negative. However, when the ninth lift is performed, the angular velocity of the movement in X-axis direction is 36.09, the largest of all angular velocities. When performing the 15th lifting action, the angular velocity of this action in Z-axis direction is −26.05, which is the smallest of all angular velocities. The longest duration of the left muscle discharge during the lifting action of the subject is 15.24 for the tibial anterior muscle and 8.91 for the external oblique muscle with the shortest duration. The longest discharge duration of the right muscle is also the tibial anterior muscle with 12.15, and the shortest duration is the erector spinae with 8.79. (shrink)

We develop a category theoretical scheme for the comprehension of the information structure associated with a complex system, in terms of families of partial or local information carriers. The scheme is based on the existence of a categorical adjunction, that provides a theoretical platform for the descriptive analysis of the complex system as a process of functorial information communication.

So far, the sciences of complexity have received less attention from philosophers than from scientists. Responding to Salthe’s (Found Sci 15, 4(6):357–367, 2010a ) model of evolution, I focus on its metaphysical implications, asking whether the implications of his canonical developmental trajectory (CDT) must be materialistic as his reading proposes.

Complex systems research is becoming ever more important in both the natural and social sciences. It is commonly implied that there is such a thing as a complex system, different examples of which are studied across many disciplines. However, there is no concise definition of a complex system, let alone a definition on which all scientists agree. We review various attempts to characterize a complex system, and consider a core set of features that (...) are widely associated with complex systems in the literature and by those in the field. We argue that some of these features are neither necessary nor sufficient for complexity, and that some of them are too vague or confused to be of any analytical use. In order to bring mathematical rigour to the issue we then review some standard measures of complexity from the scientific literature, and offer a taxonomy for them, before arguing that the one that best captures the qualitative notion of the order produced by complex systems is that of the Statistical Complexity. Finally, we offer our own list of necessary conditions as a characterization of complexity. These conditions are qualitative and may not be jointly sufficient for complexity. We close with some suggestions for future work. (shrink)

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)

Complex systems are used, studied and instantiated in science, with what con-sequences? To be clear and systematic in response it is necessary to distin-guish the consequences, for science, of science using and studying complex systems, for philosophy of science, of science using and studying complex systems, for philosophy of science, of philosophy of science modelling sci-ence as a complex system. Each of these is explored in turn, especially. While has been least studied, it will be (...) shown how modelling science as a complex process may change our conception of science and thereby query what a philosophy of science adequate to this complexity might look like. (shrink)

Can we understand important social issues by studying individual personalities and decisions? Or are societies somehow more than the people in them? Sociologists have long believed that psychology can't explain what happens when people work together in complex modern societies. In contrast, most psychologists and economists believe that if we have an accurate theory of how individuals make choices and act on them, we can explain pretty much everything about social life. Social Emergence takes a new approach to these (...) longstanding questions. Sawyer argues that societies are complex dynamical systems, and that the best way to resolve these debates is by developing the concept of emergence, focusing on multiple levels of analysis - individuals, interactions, and groups - and with a dynamic focus on how social group phenomena emerge from communication processes among individual members. This book makes a unique contribution not only to complex systems research but also to social theory. (shrink)

How might we usefully apply concepts and procedures derived from the study of other complex dynamical systems to analyzing systemic change in education? In this article we begin to define possible agendas for research toward developing systematic frameworks and shared terminology for such a project. We illustrate the plausibility of defining such frameworks and raise the question of the relation between such frameworks and the crucial task of aggregating data across ‘systemic experiments’, such as those conducted under the Urban (...) Systemic Initiative sponsored by the US National Science Foundation. Our discussion includes a review of key issues identified by groups of researchers regarding Defining the System, Structural Analysis, Relationships Among Subsystems and Levels, Drivers for Change, and Modeling Methods. (shrink)

Part I [sections 2–4] draws out the conceptual links between modern conceptions of teleology and their Aristotelian predecessor, briefly outlines the mode of functional analysis employed to explicate teleology, and develops the notion of cybernetic organisation in order to distinguish teleonomic and teleomatic systems. Part II is concerned with arriving at a coherent notion of intentional control. Section 5 argues that intentionality is to be understood in terms of the representational properties of cybernetic systems. Following from this, section 6 argues (...) that intentional control needs to be seen as a particular type of relationship between the system and its environment. (shrink)

This is the first work to apply complex systems science to the psychological interplay of order and chaos. The author draws on thought from a wide range of disciplines-both conventional and unorthodox-to address such questions as the nature of consciousness, the relation between mind and reality, and the justification of belief systems. The material should provoke thought among systems scientists, theoretical psychologists, artificial intelligence researchers, and philosophers.

Foster (economics, University of Queensland, Australia) and H lzl (economics, Vienna University of Economics and Business Administration, Austria) develop an empirically based foundation for evolutionary economics built on complex systems theory. Arguing that modern evolutionary economics is at a crossroads on both the theoretical and applied level.

Ecologist Richard Levins argues population biologists must trade‐off the generality, realism, and precision of their models since biological systems are complex and our limitations are severe. Steven Orzack and Elliott Sober argue that there are cases where these model properties cannot be varied independently of one another. If this is correct, then Levins's thesis that there is a necessary trade‐off between generality, precision, and realism in mathematical models in biology is false. I argue that Orzack and Sober's arguments fail (...) since Levins's thesis concerns the pragmatic features of model building not just the formal properties of models. (shrink)

A key topic in the work of Burghard Rieger is the notion of meaning. To explore this notion, he and his collaborators developed a most sophisticated approach combining theoretical ideas and concepts of semiotics with empirical and numerical tools of computational linguistics. In the present contribution, relations of Rieger’s achievements to some issues of interest in the physics and philosophy of complex systems will be addressed.

We develop a general covariant categorical modeling theory of natural systems’ behavior based on the fundamental functorial processes of representation and localization-globalization. In the first part of this study we analyze the process of representation. Representation constitutes a categorical modeling relation that signifies the semantic bidirectional process of correspondence between natural systems and formal symbolic systems. The notion of formal systems is substantiated by algebraic rings of observable attributes of natural systems. In this perspective, the distinction between simple and (...) class='Hi'>complex systems is reflected in the appropriate qualification of their corresponding rings of observables. The crucial distinguishing requirement with respect to the coordinatizing rings of observables has to do with the property of global commutativity. The global information structure representing the behavior of a complex system is modeled functorially in terms of its Spectrum functor. Due to the fact that, the fundamental process of representation is bidirectional by construction, it admits a precise categorical formulation in terms of the syntactic language of adjoint functors, constituting thus, a categorical adjunction. The left adjoint functor of this adjunction signifies the process of encoding the information related with phenomena of natural systems in terms of coordinatizing rings of observables, whereas, the right adjoint functor signifies the inverse process of information decoding, which, can be used for making predictions about the behavior of natural systems. (shrink)

In recent debates mechanisms are often discussed in the context of ‘complex systems’ which are understood as having a complicated compositional structure. I want to draw the attention to another, radically different kind of complex system, in fact one that many scientists regard as the only genuine kind of complex system. Instead of being compositionally complex these systems rather exhibit highly non-trivial dynamical patterns on the basis of structurally simple arrangements of large numbers of (...) non-linearly interacting constituents. The characteristic dynamical patterns in what I call “dynamically complex systems” arise from the interaction of the system’s parts largely irrespective of many properties of these parts. Dynamically complex systems can exhibit surprising statistical characteristics, the robustness of which calls for an explanation in terms of underlying generating mechanisms. However, I want to argue, dynamically complex systems are not sufficiently covered by the available conceptions of mechanisms. I will explore how the notion of a mechanism has to be modified to accommodate this case. Moreover, I will show under which conditions the widespread, if not inflationary talk about mechanisms in complex systems stretches the notion of mechanisms beyond its reasonable limits and is no longer legitimate. (shrink)

In agreement with the target article, we would like to point out a few aspects related to embodiment which further support the position of biorobotics. We argue that, especially when complex systems are considered, modeling through a physical implementation can provide hints to comprehend the whole picture behind the specific set of experimental data.

We develop a general covariant categorical modeling theory of natural systems' behavior based on the fundamental functorial processes of representation and localization-globalization. In the second part of this study we analyze the semantic bidirectional process of localization-globalization. The notion of a localization system of a complex information structure bears a dual role: Firstly, it determines the appropriate categorical environment of base reference contexts for considering the operational modeling of a complex system's behavior, and secondly, it specifies (...) the global compatibility conditions of local contextual information. A localization system acts on the global information structure of a complex system, partitions it into sorts, and eventually, forces the consistent sheaf-theoretic fibering of the latter over the base category of commutative reference contexts. In this manner, the sheafification of the Spectrum functor of a complex information structure takes place by imposing on the uniform and homologous fibered structure of elements of the Spectrum presheaf the following two requirements of coherence in relation to the localization-globalization process: [i]. Compatibility of information under restriction from the global to the local level, and [ii]. Compatibility of information under extension from the local to the global level. Correspondingly, the options of local and global receive a concrete mathematical meaning with respect to a suitable notion of topology (categorical Grothendieck topology) defined on the base category of commutative reference contexts. Finally, the accurate functorial process modeling of a complex system's behavior, respecting the processes of representation and localization-globalization, is being effectuated by means of establishing a categorical dual equivalence between the category of complex information structures, and the topes of sheaves over the base category of partial or local information carriers, equipped with the categorical topology of epimorphosis families. (shrink)

Here, for the first time, development studies encounters the set of ideas popularly known as 'Chaos Theory'. Samir Rihani applies to the processes of economic development, ideas from complex adaptive systems like uncertainty, complexity, and unpredictability. Rihani examines various aspects of the development process - including the World Bank, debt, and the struggle against poverty - and demonstrates the limitations of fundamentally linear thinking in an essentially non-linear world.

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)