Online research in philosophy

As brightly shown by Mainzer [24], the science of complexity has many distinct origins in many disciplines. Those various origins has led to “an interdisciplinary methodology to explain the emergence of certain macroscopic phenomena via the nonlinear interactions of microscopic elements” (ibid.). This paper suggests that the parallel and strong expansion of modeling and simulation - especially after the Second World War and the subsequent development of computers - is a rationale which also can be counted as an explanation (...) of this emergence. With the benefit of hindsight, one can find three periods in the methodologies of modeling in the empirical sciences: 1st the simple modeling of the simple, 2nd the simple modeling of the complex, 3rd the complex modeling and simulation of the complex. Our main thesis is that the current spreading (since the 90’s) of complex computer simulations of systems of models (where a simulation is no more a step by step calculus of a unique logico-mathematical model) is another promising dimension of the science of complexity. Following this claim, we propose to distinguish three different types of computer simulations in the context of complex systems’ modeling. Finally, we show that these types of simulations lead to three different types of weak emergence, too. (shrink)

In modern, Western societies the purpose of schooling is to ensure that school-goers acquire knowledge of pre-existing practices, events, entities and so on. The knowledge that is learned is then tested to see if the learner has acquired a correct or adequate understanding of it. For this reason, it can be argued that schooling is organised around a representational epistemology: one which holds that knowledge is an accurate representation of something that is separate from knowledge itself. Since the object of (...) knowledge is assumed to exist separately from the knowledge itself, this epistemology can also be considered ‘spatial.’ In this paper we show how ideas from complexity have challenged the spatial epistemology’ of representation and we explore possibilities for an alternative ‘temporal’ understanding of knowledge in its relationship to reality. In addition to complexity, our alternative takes its inspiration from Deweyan ‘transactional realism’ and deconstruction. We suggest that ‘knowledge’ and ‘reality’ should not be understood as separate systems which somehow have to be brought into alignment with each other, but that they are part of the same emerging complex system which is never fully ‘present’ in any (discrete) moment in time. This not only introduces the notion of time into our understanding of the relationship between knowledge and reality, but also points to the importance of acknowledging the role of the ‘unrepresentable’ or ‘incalculable’. With this understanding knowledge reaches us not as something we receive but as a response, which brings forth new worlds because it necessarily adds something (which was not present anywhere before it appeared) to what came before. This understanding of knowledge suggests that the acquisition of curricular content should not be considered an end in itself. Rather, curricular content should be used to bring forth that which is incalculable from the perspective of the present. The epistemology of emergence therefore calls for a switch in focus for curricular thinking, away from questions about presentation and representation and towards questions about engagement and response. (shrink)

Complexity and Postmodernism explores the notion of complexity in the light of contemporary perspectives from philosophy and science. The book integrates insights from complexity and computational theory with the philosophical position of thinkers including Derrida and Lyotard. Paul Cilliers takes a critical stance towards the use of the analytical method as a tool to cope with complexity, and he rejects Searle's superficial contribution to the debate.

Introduction to complexity and complex systems -- Introduction to large linear systems -- Introduction to biochemical oscillators and nonlinear biochemical systems -- Modularity, redundancy, degeneracy, pleiotropy and robustness in complex biological systems -- The evolution of biological complexity; invertebrate immune systems -- Irreducible and specified complexity in living systems -- The complex adaptive and innate human immune systems -- Complexity in quasispecies : microRNAs -- Introduction to complexity in economic systems -- Complexity in quasispecies (...) : micrornas -- Dealing with complexity. (shrink)

This book explains the relationship between intelligence and environmental complexity, and in so doing links philosophy of mind to more general issues about the relations between organisms and environments, and to the general pattern of 'externalist' explanations. The author provides a biological approach to the investigation of mind and cognition in nature. In particular he explores the idea that the function of cognition is to enable agents to deal with environmental complexity. The history of the idea in the (...) work of Dewey and Spencer is considered, as is the impact of recent evolutionary theory on our understanding of the place of mind in nature. (shrink)

Arguments about the evolutionary function of phenomenal consciousness are beset by the problem of epiphenomenalism. For if it is not clear whether phenomenal consciousness has a causal role, then it is difficult to begin an argument for the evolutionary role of phenomenal consciousness. We argue that complexity arguments offer a way around this problem. According to evolutionary biology, the structural complexity of a given organ can provide evidence that the organ is an adaptation, even if nothing is known (...) about the causal role of the organ. Evidence from cognitive neuropsychology suggests that phenomenal consciousness is structurally complex in the relevant way, and this provides prima facie evidence that phenomenal consciousness is an adaptation. Furthermore, we argue that the complexity of phenomenal consciousness might also provide clues about the causal role of phenomenal consciousness. (shrink)

Different proofs may be distinguished in terms of complexity. This paper reviews the inefficiency of current logical systems.

It is becoming rather monotonous continually reading articles that tell us how the concept of and the requirements for the modern organization are changing, how these are more complex than ever, and how a paradigm shift is necessary in order to facilitate our continued analysis, and management, of such entities. We are told that we must distribute decision making, encourage individual autonomy, and strive to innovate in the rapidly changing environment that characterizes the apparent New World Order. The list is (...) as far reaching as it is impressive. These concepts coincide with a new, or at least emerging, description of organizations. This “paradigm” appears, from particular presentations at least, wholly to reject the long-held prevailing paradigm of the mechanistic, efficiency-driven, hierarchical, command-and-control organization. (We would question the “whollyness” of this position.) Complexity science has emerged from the field of possible candidates as a prime contender for the top spot in the next era of management science. (shrink)

In the dissertation we study the complexity of generalized quantifiers in natural language. Our perspective is interdisciplinary: we combine philosophical insights with theoretical computer science, experimental cognitive science and linguistic theories. -/- In Chapter 1 we argue for identifying a part of meaning, the so-called referential meaning (model-checking), with algorithms. Moreover, we discuss the influence of computational complexity theory on cognitive tasks. We give some arguments to treat as cognitively tractable only those problems which can be computed in (...) polynomial time. Additionally, we suggest that plausible semantic theories of the everyday fragment of natural language can be formulated in the existential fragment of second-order logic. -/- In Chapter 2 we give an overview of the basic notions of generalized quantifier theory, computability theory, and descriptive complexity theory. -/- In Chapter 3 we prove that PTIME quantifiers are closed under iteration, cumulation and resumption. Next, we discuss the NP-completeness of branching quantifiers. Finally, we show that some Ramsey quantifiers define NP-complete classes of finite models while others stay in PTIME. We also give a sufficient condition for a Ramsey quantifier to be computable in polynomial time. -/- In Chapter 4 we investigate the computational complexity of polyadic lifts expressing various readings of reciprocal sentences with quantified antecedents. We show a dichotomy between these readings: the strong reciprocal reading can create NP-complete constructions, while the weak and the intermediate reciprocal readings do not. Additionally, we argue that this difference should be acknowledged in the Strong Meaning hypothesis. -/- In Chapter 5 we study the definability and complexity of the type-shifting approach to collective quantification in natural language. We show that under reasonable complexity assumptions it is not general enough to cover the semantics of all collective quantifiers in natural language. The type-shifting approach cannot lead outside second-order logic and arguably some collective quantifiers are not expressible in second-order logic. As a result, we argue that algebraic (many-sorted) formalisms dealing with collectivity are more plausible than the type-shifting approach. Moreover, we suggest that some collective quantifiers might not be realized in everyday language due to their high computational complexity. Additionally, we introduce the so-called second-order generalized quantifiers to the study of collective semantics. -/- In Chapter 6 we study the statement known as Hintikka's thesis: that the semantics of sentences like ``Most boys and most girls hate each other'' is not expressible by linear formulae and one needs to use branching quantification. We discuss possible readings of such sentences and come to the conclusion that they are expressible by linear formulae, as opposed to what Hintikka states. Next, we propose empirical evidence confirming our theoretical predictions that these sentences are sometimes interpreted by people as having the conjunctional reading. -/- In Chapter 7 we discuss a computational semantics for monadic quantifiers in natural language. We recall that it can be expressed in terms of finite-state and push-down automata. Then we present and criticize the neurological research building on this model. The discussion leads to a new experimental set-up which provides empirical evidence confirming the complexity predictions of the computational model. We show that the differences in reaction time needed for comprehension of sentences with monadic quantifiers are consistent with the complexity differences predicted by the model. -/- In Chapter 8 we discuss some general open questions and possible directions for future research, e.g., using different measures of complexity, involving game-theory and so on. -/- In general, our research explores, from different perspectives, the advantages of identifying meaning with algorithms and applying computational complexity analysis to semantic issues. It shows the fruitfulness of such an abstract computational approach for linguistics and cognitive science. (shrink)

In a world of ever growing specialization, the issue of complexity attracts a good amount of attention from cross-disciplinary points of view as this Congress provides evidence. Charles S. Peirce's thought may help us not only to shoulder once again philosophical responsibility which has been largely abdicated by much of 20th century philosophy, but also to tackle some of the most stubborn contemporary problems. The founder of pragmatism identified one century ago most of these problems, and he also mapped (...) out some paths that we could follow to overcome the poverty of contemporary scientistic reductionism. One of these paths is related with the issue of complexity, that lies at the heart of all his conception. -/- Along this line, the aim of my paper is to describe what Peirce can teach about complexity to semioticians coming from very different scientific backgrounds. The lecture will be divided in three sections: 1) a presentation of Peirce, stressing his personal authority as a scientist philosopher, providing also some biographical details; 2) the theory of categories as the heart of complexity according to Peirce and, finally, 3) some consequences of Peirce's notion of complexity in relation with abduction and creativity, semiosis, cross-disciplinarity and communication. -/- . (shrink)

This volume provides an accessible theoretical introduction to the topic of complexity theory while considering its broader implications for educational change.

We study the computational complexity of polyadic quantifiers in natural language. This type of quantification is widely used in formal semantics to model the meaning of multi-quantifier sentences. First, we show that the standard constructions that turn simple determiners into complex quantifiers, namely Boolean operations, iteration, cumulation, and resumption, are tractable. Then, we provide an insight into branching operation yielding intractable natural language multi-quantifier expressions. Next, we focus on a linguistic case study. We use computational complexity results to (...) investigate semantic distinctions between quantified reciprocal sentences. We show a computational dichotomy<br>between different readings of reciprocity. Finally, we go more into philosophical speculation on meaning, ambiguity and computational complexity. In particular, we investigate a possibility to<br>revise the Strong Meaning Hypothesis with complexity aspects to better account for meaning shifts in the domain of multi-quantifier sentences. The paper not only contributes to the field of the formal<br>semantics but also illustrates how the tools of computational complexity theory might be successfully used in linguistics and philosophy with an eye towards cognitive science. (shrink)

What enables individually simple insects like ants to act with such precision and purpose as a group? How do trillions of individual neurons produce something as extraordinarily complex as consciousness? What is it that guides self-organizing structures like the immune system, the World Wide Web, the global economy, and the human genome? These are just a few of the fascinating and elusive questions that the science of complexity seeks to answer. In this remarkably accessible and companionable book, leading complex (...) systems scientist Melanie Mitchell provides an intimate, detailed tour of the sciences of complexity, a broad set of efforts that seek to explain how large-scale complex, organized, and adaptive behavior can emerge from simple interactions among myriad individuals. Comprehending such systems requires a wholly new approach, one that goes beyond traditional scientific reductionism and that re-maps long-standing disciplinary boundaries. Based on her work at the Santa Fe Institute and drawing on its interdisciplinary strategies, Mitchell brings clarity to the workings of complexity across a broad range of biological, technological, and social phenomena, seeking out the general principles or laws that apply to all of them. She explores as well the relationship between complexity and evolution, artificial intelligence, computation, genetics, information processing, and many other fields. Richly illustrated and vividly written, Complexity: A Guided Tour offers a comprehensive and eminently comprehensible overview of the ideas underlying complex systems science, the current research at the forefront of this field, and the prospects for the field's contribution to solving some of the most important scientific questions of our time. (shrink)

The problem of computational complexity of semantics for some natural language constructions – considered in [M. Mostowski, D. Wojtyniak 2004] – motivates an interest in complexity of Ramsey quantifiers in finite models. In general a sentence with a Ramsey quantifier R of the following form Rx, yH(x, y) is interpreted as ∃A(A is big relatively to the universe ∧A2 ⊆ H). In the paper cited the problem of the complexity of the Hintikka sentence is reduced to the (...) problem of computational complexity of the Ramsey quantifier for which the phrase “A is big relatively to the universe” is interpreted as containing at least one representative of each equivalence class, for some given equvalence relation. In this work we consider quantifiers Rf, for which “A is big relatively to the universe” means “card(A) > f (n), where n is the size of the universe”. Following [Blass, Gurevich 1986] we call R mighty if Rx, yH(x, y) defines N P – complete class of finite models. Similarly we say that Rf is N P –hard if the corresponding class is N P –hard. We prove the following theorems. (shrink)

We study the computational complexity of reciprocal sentences with quantified antecedents. We observe a computational dichotomy between different interpretations of reciprocity, and shed some light on the status of the so-called Strong Meaning Hypothesis.

The main claim of this paper is that notions of implementation based on an isomorphic correspondence between physical and computational states are not tenable. Rather, ``implementation'' has to be based on the notion of ``bisimulation'' in order to be able to block unwanted implementation results and incorporate intuitions from computational practice. A formal definition of implementation is suggested, which satisfies theoretical and practical requirements and may also be used to make the functionalist notion of ``physical realization'' precise. The upshot of (...) this new definition of implementation is that implementation cannot distinguish isomorphic bisimilar from non-isomporphic bisimilar systems anymore, thus driving a wedge between the notions of causal and computational complexity. While computationalism does not seem to be affected by this result, the consequences for functionalism are not clear and need further investigations. (shrink)

" The Moment of Complexity is a profoundly original work. In remarkable and insightful ways, Mark Taylor traces an entirely new way to view the evolution of our culture, detailing how information theory and the scientific concept of complexity can be used to understand recent developments in the arts and humanities. This book will ultimately be seen as a classic."-John L. Casti, Santa Fe Institute, author of Godel: A Life of Logic, the Mind, and Mathematics The science of (...) complexity accounts for that inscrutable mix of chaos and order that governs our natural world. Complexity explains how networks emerge and function, how species organize into ecosystems, how stars form into galaxies, and how just a few sequences of DNA can account for so many different life forms. Recently, the idea of complexity has taken the worlds of business and politics by storm. The concept is used to account for phenomena as varied as the behavior of the stock market, the response of voting populations, and the effects of risk management. Even Disney has used complexity theory to manage crowd control at its theme parks. Given the startling development of new information technologies, we now live in a moment of unprecedented complexity, an era in which change occurs faster than our ability to comprehend it. With The Moment of Complexity , Mark C. Taylor offers a timely map for this unfamiliar terrain opening in our midst, unfolding an original philosophy through a remarkable synthesis of science and culture. According to Taylor, complexity is not just a breakthrough scientific concept, but the defining quality of the post-Cold War era. The flux of digital currents swirling around us, he argues, has created a new network culture with its own distinctive logic and dynamic. Drawing on resources from information theory and evolutionary biology, Taylor explains the operation of complex adaptive systems in social and cultural processes and captures a whole new zeitgeist in the making. To appreciate the significance of our emerging network culture, he claims, we need not only to understand contemporary scientific and technological transformations, but also to explore the subtle influences of art, architecture, philosophy, religion, and higher education. The Moment of Complexity , then, is a remarkable work of cultural analysis on a scale rarely seen today. To follow its trajectory is to learn how we arrived at this critical moment in our culture, and to know where we might head in the twenty-first century. (shrink)

The aim of this book is to show how supramolecular complexity of cell organization can dramatically alter the functions of individual macromolecules within a cell. The emergence of new functions which appear as a consequence of supramolecular complexity, is explained in terms of physical chemistry. The book is interdisciplinary, at the border between cell biochemistry, physics and physical chemistry. This interdisciplinarity does not result in the use of physical techniques but from the use of physical concepts to study (...) biological problems. In the domain of complexity studies, most works are purely theoretical or based on computer simulation. The present book is partly theoretical, partly experimental and theory is always based on experimental results. Moreover, the book encompasses in a unified manner the dynamic aspects of many different biological fields ranging from dynamics to pattern emergence in a young embryo. The volume puts emphasis on dynamic physical studies of biological events. It also develops, in a unified perspective, this new interdisciplinary approach of various important problems of cell biology and chemistry, ranging from enzyme dynamics to pattern formation during embryo development, thus paving the way to what may become a central issue of future biology. (shrink)

The theme of this book is formed by a pair of concepts: the concept of formal language as carrier of the precise expression of meaning, facts and problems, and the concept of algorithm or calculus, i.e. a formally operating procedure for the solution of precisely described questions and problems. The book is a unified introduction to the modern theory of these concepts, to the way in which they developed first in mathematical logic and computability theory and later in automata theory, (...) and to the theory of formal languages and complexity theory. Apart from considering the fundamental themes and classical aspects of these areas, the subject matter has been selected to give priority throughout to the new aspects of traditional questions, results and methods which have developed from the needs or knowledge of computer science and particularly of complexity theory. It is both a textbook for introductory courses in the above-mentioned disciplines as well as a monograph in which further results of new research are systematically presented and where an attempt is made to make explicit the connections and analogies between a variety of concepts and constructions. (shrink)

Mental state reasoning or theory-of-mind has been the subject of a rich body of imaging research. Although such investigations routinely tap a common set of regions, the precise function of each area remains a contentious matter. With the help of functional magnetic resonance imaging (fMRI), we sought to determine which areas are involved when processing mental state or intentional metarepresentations by focusing on the relational aspect of such representations. Using non-intentional relational representations such as spatial relations between persons and between (...) objects as a contrast, the results ascertained the involvement of the precuneus, the temporal poles, and the medial prefrontal cortex in the processing of intentional representations. In contrast, the anterior superior temporal sulcus and the left temporo-parietal junction were implicated when processing representations that refer to the presence of persons in relational contexts in general. The right temporo-parietal junction, however, was specifically activated for persons entering spatial relations. The level of representational complexity, a previously unexplored factor, was also found to modulate the neural response in some brain regions, such as the medial prefrontal cortex and the right temporo-parietal junction. These findings highlight the need to take into account the critical roles played by an extensive network of neural regions during mental state reasoning. (shrink)

We observe that the classification problem for countable models of arithmetic is Borel complete. On the other hand, the classification problems for finitely generated models of arithmetic and for recursively saturated models of arithmetic are Borel; we investigate the precise complexity of each of these. Finally, we show that the classification problem for pairs of recursively saturated models and for automorphisms of a fixed recursively saturated model are Borel complete.

The present article constitutes an attempt at a review of a few selected questions related to the complexity paradigm and its implications for research on cognition, especially within the so-called ecological approach framework. I propose several theses, among others concerning the two contrary tendencies within the dominant methodology (the propensity to search for simplicity and the growing emphasis on recognizing complexity), as well as the ontological consequences of the phenomenon under discussion (ontological emergence and processual emergentism).

The SAGE Handbook of Complexity and Management will be the first substantive scholarly work to provide a map of the state of art research in the growing field ...

Learning theory has frequently been applied to language acquisition, but discussion has largely focused on information theoretic problems—in particular on the absence of direct negative evidence. Such arguments typically neglect the probabilistic nature of cognition and learning in general. We argue first that these arguments, and analyses based on them, suffer from a major flaw: they systematically conflate the hypothesis class and the learnable concept class. As a result, they do not allow one to draw significant conclusions about the learner. (...) Second, we claim that the real problem for language learning is the computational complexity of constructing a hypothesis from input data. Studying this problem allows for a more direct approach to the object of study—the language acquisition device—rather than the learnable class of languages, which is epiphenomenal and possibly hard to characterize. The learnability results informed by complexity studies are much more insightful. They strongly suggest that target grammars need to be objective, in the sense that the primitive elements of these grammars are based on objectively definable properties of the language itself. These considerations support the view that language acquisition proceeds primarily through data-driven learning of some form. (shrink)

Book review of Bechtel and Richardson, Discovering Complexity (1993). Review suggests that one theme of the book -- that scientific reason is "constituted" in part by a cognitive strategy of finding complexity -- is not fully supported.

Self-organized complexity in the physical, biological, and social sciences Donald L Turcotte*f and John B. Rundle* *Department of Earth and Atmospheric ...

Introduction -- Elucidating complexity theories -- Complexity in the natural sciences -- Complexity in social theory -- Towards transdisciplinarity -- Complexity in philosophy: complexification and the limits to knowledge -- Complexity in ethics -- Earth in the anthropocene -- Complexity and climate change -- American dreams, ecological nightmares and new visions -- Complexity and sustainability: wicked problems, gordian knots and synergistic solutions -- Conclusion.

This book presents an up-to-date, unified treatment of research in bounded arithmetic and complexity of propositional logic, with emphasis on independence proofs and lower bound proofs. The author discusses the deep connections between logic and complexity theory and lists a number of intriguing open problems. An introduction to the basics of logic and complexity theory is followed by discussion of important results in propositional proof systems and systems of bounded arithmetic. More advanced topics are then treated, including (...) polynomial simulations and conservativity results, various witnessing theorems, the translation of bounded formulas (and their proofs) into propositional ones, the method of random partial restrictions and its applications, direct independence proofs, complete systems of partial relations, lower bounds to the size of constant-depth propositional proofs, the method of Boolean valuations, the issue of hard tautologies and optimal proof systems, combinatorics and complexity theory within bounded arithmetic, and relations to complexity issues of predicate calculus. Students and researchers in mathematical logic and complexity theory will find this comprehensive treatment an excellent guide to this expanding interdisciplinary area. (shrink)

Introduction -- The legal aspects -- Corporate governance and corporate performance -- Complexity and corporate governance -- Conclusion.

This is a comprehensive discussion of complexity as it arises in physical, chemical, and biological systems, as well as in mathematical models of nature. Common features of these apparently unrelated fields are emphasised and incorporated into a uniform mathematical description, with the support of a large number of detailed examples and illustrations. The quantitative study of complexity is a rapidly developing subject with special impact in the fields of physics, mathematics, information science, and biology. Because of the variety (...) of the approaches, no comprehensive discussion has previously been attempted. This book will be of interest to graduate students and researchers in physics (nonlinear dynamics, fluid dynamics, solid-state, cellular automata, stochastic processes, statistical mechanics and thermodynamics), mathematics (dynamical systems, ergodic and probability theory), information and computer science (coding, information theory and algorithmic complexity), electrical engineering and theoretical biology. (shrink)

JPVA Journal of Philosophy and the Visual Arts No 6 Complexity Architecture / Art / Philosophy 'Beginning with complexity will involve working with the recognition that there has always been more than one. Here however this insistent "more than one" will be positioned beyond the scope of semantics; rather than complexity occurring within the range of meaning and taking the form of a generalised polysemy, it will be linked to the nature of the object and to its (...) production. Complexity, therefore, will be inextricably connected to the ontology of the object. What this means is that complexity, in resisting the hold of a semantic idealism on the one hand, and the attempt to give to it the position of being the basis of a new foundationalism on the other, becomes a way of thinking both the presence and the production of objects.' Andrew Benjamin The Journal of Philosophy and the Visual Arts has set new standards in its exploration of themes central to philosophy's relation to the visual arts, illuminating areas of art criticism, architecture, feminism as well as philosophy itself. Rather than simply reflecting current trends it provides a forum in which the real developments in the analysis of the visual arts and its larger cultural and political context can be presented. Articles by well known philosophers and theorists, as well as some lesser known, together with writings by artists and architects allow a strong interdisciplinary approach reflecting the Journal's roots in post-structural theory. Previous issues include: Philosophy & the Visual Arts (No 1) Philosophy & Architecture (No 2) Architecture, Space, Painting (No 3) The Body (No 4) Abstraction (No 5). (shrink)

Richard Dawkins has popularized an argument which, according to him, proves that there is almost certainly no God. It rests on the assumption that complex and statistically improbable things are more difficult to explain than those that are not, and that any explanatory mechanism that is called on to do the explaining must show how this complexity can be built up from simpler means as it would be useless otherwise. In this paper, I first question what justifies the consideration (...) of the designer’s own complexity. I suggest a different understanding of both order and simplicity inevitable when one considers the psychological counterpart of information. I then assess what seems to be the inference engine of the proposal, the metaphor of biological organisms as either self-programmed machines or algorithms. I show how self-generated organized complexity would not sit well with our knowledge of both abduction and the theorems of information theory applied to genetics. I then turn to the positive side of Dawkins’ challenge, and I review some philosophers and their proposals for how the complexity of the world could be controlled from outside if one wanted to uphold a traditional understanding of God’s simplicity. (shrink)

What exactly is complexity science? Two's company, three is complexity ; Disorder rules, OK? ; Chaos and all that jazz ; Mob mentality ; Getting connected -- What can complexity science do for me? Forecasting financial markets ; Tackling traffic networks and climbing the corporate ladder ; Looking for Mr./Mrs. Right ; Coping with conflict : next-generation wars and global terrorism -- Catching a cold, avoiding super-flu and curing cancer ; The mother of all complexities : our (...) nanoscale quantum world ; To infinity and beyond. (shrink)

We study the computational complexity of the model checking problem for quantifier-free dependence logic ${(\mathcal{D})}$ formulas. We characterize three thresholds in the complexity: logarithmic space (LOGSPACE), non-deterministic logarithmic space (NL) and non-deterministic polynomial time (NP).

Machine generated contents note: -- Miracles and Nasty Surprises -- The Failure of Models & Labels; the Success of Experience & Emergence -- Two Kinds of Coherence - Ascribed and Emergent -- Models, Homologies & Simulacra -- The Ascribed Coherence of Thagard and Weick -- Coherence and Business Success -- Emergence, Coherence & Narrative -- Affordances and Organization -- Homology: Sense-Making revisited -- But Experience is Different -- Complexity tools: the Semiotic Square & Homology -- Steps to Implementation.

Modal dependence logic was introduced recently by Väänänen. It enhances the basic modal language by an operator = (). For propositional variables p 1, . . . , p n , = (p 1, . . . , p n-1, p n ) intuitively states that the value of p n is determined by those of p 1, . . . , p n-1. Sevenster (J. Logic and Computation, 2009) showed that satisfiability for modal dependence logic is complete for nondeterministic (...) exponential time.In this paper we consider fragments of modal dependence logic obtained by restricting the set of allowed propositional connectives. We show that satisfiability for poor man’s dependence logic, the language consisting of formulas built from literals and dependence atoms using ${\wedge, \square, \lozenge}$ (i. e., disallowing disjunction), remains NEXPTIME-complete. If we only allow monotone formulas (without negation, but with disjunction), the complexity drops to PSPACE-completeness.We also extend Väänänen’s language by allowing classical disjunction besides dependence disjunction and show that the satisfiability problem remains NEXPTIME-complete. If we then disallow both negation and dependence disjunction, satisfiability is complete for the second level of the polynomial hierarchy. Additionally we consider the restriction of modal dependence logic where the length of each single dependence atom is bounded by a number that is fixed for the whole logic. We show that the satisfiability problem for this bounded arity dependence logic is PSPACE-complete and that the complexity drops to the third level of the polynomial hierarchy if we then disallow disjunction.In this way we completely classify the computational complexity of the satisfiability problem for all restrictions of propositional and dependence operators considered by Väänänen and Sevenster. (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)

v. 1. Origins of order-creation science : complexity science from basic disciplines -- v. 2. Self-organization, emergence and self-organized criticality -- v. 3. Organization and management complexity dynamics -- v. 4. Agent-based socio-economic simulation -- v. 5. Power-law distributions in society and business.

Psychoneural reduction is under attack again, only this time from a former ally: cognitive neuroscience. It has become popular to think of the brain as a complex system whose theoretically important properties emerge from dynamic, non-linear interactions between its component parts. ``Emergence'' is supposed to replace reduction: the latter is thought to be incapable of explaining the brain qua complex system. Rather than engage this issue at the level of theories of reduction versus theories of emergence, I here emphasize a (...) role that reductionism plays – and will continue to play – even if neuroscience adopts this ``complex systems'' view. In detailed investigations into the function of complex neural circuits, certain questions can only be addressed by moving down levels and scales. This role for reduction also finds a place for approaches that dominate mainstream neuroscience, like the widespread use of experimental techniques and theories from molecular biology and biochemistry. These are difficult to reconcile with the anti-reductionist sentiments of the ``complex systems'' branch of cognitive neuroscience. (shrink)

Recent work in biology and cognitive science depicts a variety of target phenomena as the products of a tangled web of causal influences. Such influences may include both internal and external factors as well as complex patterns of reciprocal causal interaction. Such twisted tales are sometimes seen as a threat to explanatory strategies that invoke notions such as inner programs, genes for and sometimes even internal representations. But the threat, I shall argue, is more apparent than real. Complex causal influence, (...) in and of itself, provides no good reason to reject these familiar explanatory notions. To believe otherwise, I suggest, is generally to commit (at least) one of two seductive errors. The first error is to think that the general notion of a state x coding for an outcome y involves the state's constituting a full description of y. This is what I call the myth of the self-contained code. The second error is to think that the practice of treating certain factors as special (e.g., seeing genes as coding for outcomes in a way environmental factors do not) depends on the (often mistaken) belief that the singled out factor is somehow doing the most real work. Where the amounts of causal influence are evenly spread, it is assumed there can be no reason to treat one factor in a special way. This is what I term the Myth of Explanatory Equality. Avoiding these errors involves reminding ourselves of (1) the rich context-dependence of even standard, unproblematic uses of the notions of code, program and information content (all three make sense only relative to an assumed ecological backdrop) and (2) the difference between explaining why an event occurred and displaying the full workings of a complex causal system. (shrink)

In Unsimple Truths, Sandra Mitchell argues that the long-standing scientific and philosophical deference to reductive explanations founded on simple universal ...

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Extended Abstract This book introduces and develops a new, universal method of the scientific comprehension of reality providing the objective, ...

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PART ONE The Evolutionary Metaphors in the Reconstruction of Economics The indiscriminate application of the term 'evolution' however, has led to some ...

Almost before the mourning, the search for the explanation begins. When a public disaster like Saturday's space shuttle crash takes place, it's our natural impulse to find out why - an impulse motivated largely by a desire to avoid such tragedies in the future and to learn from our mistakes. Was it tiles damaged at takeoff? The wrong angle at rollover? A fuel leak? Insufficient funding?

This paper continues the study of the metric topology on $2^{\mathbb {N}}$ that was introduced by S. Binns. This topology is induced by a directional metric where the distance from $Y\in2^{\mathbb {N}}$ to $X\in2^{\mathbb {N}}$ is given by \[\limsup_{n}\frac{C(X\upharpoonright n|Y\upharpoonright n)}{n}.\] This definition is closely related to the notions of effective Hausdorff and packing dimensions. Here we establish that this is a path-connected topology on $2^{\mathbb {N}}$ and that under it the functions $X\mapsto\operatorname{dim}_{\mathcal{H}}X$ and $X\mapsto\operatorname{dim}_{p}X$ are continuous. We also investigate (...) the scalar multiplication operation that was introduced by Binns. The multiplication of a real $X\in2^{\mathbb {N}}$ by an element $\alpha\in[0,1]$ represents a dilution of the information in $X$ by a factor of $\alpha$ . Our main result is to show that every regular real is the dilution of a real of Hausdorff dimension 1. That is, that the information in every regular real can be maximally compressed. (shrink)

Slobodkin proposes that the best intellectual work is done as if it were a game on a simplified playing field.

An important distinction between phonology and syntax has been overlooked. All phonological patterns belong to the regular region of the Chomsky Hierarchy, but not all syntactic patterns do. We argue that the hypothesis that humans employ distinct learning mechanisms for phonology and syntax currently offers the best explanation for this difference.

This article contributes to the revision of the concept of system in social theory using complexity theory. The old concept of social system is widely discredited; a new concept of social system can more adequately constitute an explanatory framework. Complexity theory offers the toolkit needed for this paradigm shift in social theory. The route taken is not via Luhmann, but rather the insights of complexity theorists in the sciences are applied to the tradition of social theory inspired (...) by Marx, Weber, and Simmel. The article contributes to the theorization of intersectionality in social theory as well as to the philosophy of social science. It addresses the challenge of theorizing the intersection of multiple complex social inequalities, exploring the various alternative approaches, before rethinking the concept of social system. It investigates and applies, for the first time, the implications of complexity theory for the analysis of multiple intersecting social inequalities. Key Words: complexity theory • inequality • intersectionality • social theory. (shrink)

Adopting a materialist approach to the mind has far reaching implications for many presuppositions regarding the properties of the brain, including those that have traditionally been consigned to “the mental” aspect of human being. One such presupposition is the conception of the disembodied self. In this article we aim to account for the self as a material entity, in that it is wholly the result of the physiological functioning of the embodied brain. Furthermore, we attempt to account for the structure (...) of the self by invoking the logic of the narrative. While our conception of narrative selfhood incorporates the work of both Freud and Dennett, we offer a critique of these two theorists and then proceed to amend their theories by means of complexity theory. We argue that the self can be characterised as a complex system, which allows us to account for the structure of the wholly material self. (shrink)

The terms ``objectivity'''' and ``objective'''' are among the mostused yet ill-defined terms in the philosophy of science and epistemology. Common to all thevarious usages is the rhetorical force of ``I endorse this and you should too'''', orto put it more mildly, that one should trust the outcome of the objectivity-producing process.The persuasive endorsement and call to trust provide some conceptual coherenceto objectivity, but the reference to objectivity is hopefully not merely an attemptat persuasive endorsement. What, in addition to epistemological endorsement,does (...) objectivity carry with it? Drawing on recent historical and philosophical work,I articulate eight operationally accessible and distinct senses of objectivity.While there are links among these senses, providing cohesion to the concept, I argue thatnone of the eight senses is strictly reducible to the others, giving objectivity itsirreducible complexity. (shrink)

The science of complexity is based on a new way of thinking that stands in sharp contrast to the philosophy underlying Newtonian science, which is based on reductionism, determinism, and objective knowledge. This paper reviews the historical development of this new world view, focusing on its philosophical foundations. Determinism was challenged by quantum mechanics and chaos theory. Systems theory replaced reductionism by a scientifically based holism. Cybernetics and postmodern social science showed that knowledge is intrinsically subjective. These developments are (...) being integrated under the header of “complexity science”. Its central paradigm is the multi-agent system. Agents are intrinsically subjective and uncertain about their environment and future, but out of their local interactions, a global organization emerges. Although different philosophers, and in particular the postmodernists, have voiced similar ideas, the paradigm of complexity still needs to be fully assimilated by philosophy. This will throw a new light on old philosophical issues such as relativism, ethics and the role of the subject. (shrink)

My paper draws on examples from molecular biology, the details of which I have developed elsewhere (Rheinberger 1992, 1993, 1995, 1997). Here, I can give only a brief outline of my argument. Reduction of complexity is a prerequisite for experimental research. To make sense of the universe of living beings, the modern biologist is bound to divide his world into fragments in which parameters can be defined, quantities measured, qualities identified. Such is the nature of any "experimental system." Ontic (...) complexity has to be reduced in order to make experimental research possible. The complexity of the research object, however, is epistemically retained in the rich context of an experimental landscape, where the eruption of "volcanic systems" can change the scenery dramatically as the result of particular, unprecedented findings. (shrink)

A novel conceptual framework is introduced for the Complexity Levels Theory in a Categorical Ontology of Space and Time. This conceptual and formal construction is intended for ontological studies of Emergent Biosystems, Super-complex Dynamics, Evolution and Human Consciousness. A claim is defended concerning the universal representation of an item’s essence in categorical terms. As an essential example, relational structures of living organisms are well represented by applying the important categorical concept of natural transformations to biomolecular reactions and relational structures (...) that emerge from the latter in living systems. Thus, several relational theories of living systems can be represented by natural transformations of organismic, relational structures. The ascent of man and other living organisms through adaptation, is viewed in novel categorical terms, such as variable biogroupoid representations of evolving species. Such precise but flexible evolutionary concepts will allow the further development of the unifying theme of local-to-global approaches to highly complex systems in order to represent novel patterns of relations that emerge in super- and ultra-complex systems in terms of compositions of local procedures. Solutions to such local-to-global problems in highly complex systems with ‘broken symmetry’ might be possible to be reached with the help of higher homotopy theorems in algebraic topology such as the generalized van Kampen theorems (HHvKT). Categories of many-valued, Łukasiewicz-Moisil (LM) logic algebras provide useful concepts for representing the intrinsic dynamic ‘asymmetry’ of genetic networks in organismic development and evolution, as well as to derive novel results for (non-commutative) Quantum Logics. Furthermore, as recently pointed out by Baianu and Poli (Theory and applications of ontology, vol 1. Springer, Berlin, in press), LM-logic algebras may also provide the appropriate framework for future developments of the ontological theory of levels with its complex/entangled/intertwined ramifications in psychology, sociology and ecology. As shown in the preceding two papers in this issue, a paradigm shift towards non-commutative, or non-Abelian, theories of highly complex dynamics—which is presently unfolding in physics, mathematics, life and cognitive sciences—may be implemented through realizations of higher dimensional algebras in neurosciences and psychology, as well as in human genomics, bioinformatics and interactomics. (shrink)

This article shows that in two respects, Gödel's incompleteness theorem strongly supports the arguments of Edgar Morin's complexity paradigm. First, from the viewpoint of the content of Gödel's theorem, the latter justifies the basic view of complexity paradigm according to which knowledge is a dynamic, unfinished process, and develops by way of self-criticism and self-transcendence. Second, from the viewpoint of the proof procedure of Gödel's theorem, the latter confirms the complexity paradigm's circular line of inference through which (...) is formed the all-round knowledge of a concrete object. (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)

The paradigm of Laplacean determinism combines three regulative principles: determinism, predictability, and the explanatory adequacy of universal laws together with purely local conditions. Historically, it applied to celestial mechanics, but it has been expanded into an ideal for scientific theories whose cogency is often not questioned. Laplace's demon is an idealization of mechanistic scientific method. Its principles together assumes imply reducibility, and rule out holism and emergence. I will argue that Laplacean determinism fails even in the realm of planetary dynamics, (...) and that it does not give suitable criteria for explanatory success except within very well defined and rather exceptional domains. Ironically, the very successes of Laplacean method in the Solar System were made possible only by processes that are not themselves tractable to Laplacean methodology. The results of some of these processes were first observed in 1964, but despite the falsification of Laplacean methodology, the explanatory resources of holism and emergence remain in scientific limbo. (shrink)

This paper is a critique of Richard Dawkins’ “argument from improbability” against the existence of God. This argument, which forms the core of Dawkins’ book The God Delusion, provides an interesting example of the use of scientific ideas in arguments about religion. Here I raise three objections: (1) The argument is inapplicable to philosophical conceptions of God that reduce most of God’s complexity to that of the physical universe. (2) The argument depends on a way of estimating probabilities that (...) fails for the probability of an entity that creates natural laws. (3) The argument supposes that complexity arises from past physical causes; however, some forms of complexity known to mathematics and logic do not arise in this way. After stating these three criticisms, I show that some of these same considerations undermine Dawkins’ critique of agnosticism. I close the paper with some remarks on Dawkins’ conception of God. (shrink)

In this article, the author argues that complexity theories have limited use in the study of society, and that social processes are too complex and particular to be rigorously modeled in complexity terms. Theories of social complexity are shown to be inadequately developed, and typical weaknesses in the literature on social complexity are discussed. Two stronger analyses, of Luhmann and of Harvey and Reed, are also critically considered. New considerations regarding social complexity are advanced, on (...) the lines that simplicity, complexity that can be modeled, and incondensible complexity permeate society simultaneously. The difficulty of establishing complexity models for processes involving ongoing interpretation is discussed. It is argued that the notions of system and environment need recasting in social studies. Existing social studies and literature, it is argued, reflect a polymorphous, contextual, contingent, labyrinthine, dramatic and political face to social complexity. Students of social complexity must be literate in such studies. (shrink)

Biological systems exhibit complexity at all levels of organization. It has recently been argued by Michael Behe that at the biochemical level a type of complexity exists--irreducible complexity--that cannot possibly have arisen as the result of natural, evolutionary processes and must instead be the product of (supernatural) intelligent design. Recent work on self-organizing chemical reactions calls into question Behe's analysis of the origins of biochemical complexity. His central interpretative metaphor for biochemical complexity, that of the (...) well-designed mousetrap that ceases to function if critical parts are absent, is undermined by the observation that typical biochemical systems exhibit considerable redundancy and overlap of function. Real biochemical systems, we argue, manifest redundant complexity--a characteristic result of evolutionary processes. (shrink)

In Unsimple truths, Sandra D. Mitchell examines the historical context of current scientific practices and elaborates the challenges complexity has since posed to status quo science and policymaking. Mitchell criticizes models of science inspired by Newtonian physics and argues for a pragmatistic, anti-universalist approach to science. In this review, I focus on what I find to be the most important point of the book, Mitchell’s argument for the conceptual independence of compositional materialism and descriptive fundamentalism. Along the way, I (...) provide a description of Mitchell’s overall project and a road map of the book. (shrink)

Some theorists who emphasize the complexity of biological and cognitive systems and who advocate the employment of the tools of dynamical systems theory in explaining them construe complexity and reduction as exclusive alternatives. This paper argues that reduction, an approach to explanation that decomposes complex activities and localizes the components within the complex system, is not only compatible with an emphasis on complexity, but provides the foundation for dynamical analysis. Explanation via decomposition and localization is nonetheless extremely (...) challenging, and an analysis of recent cognitive neuroscience research on memory is used to illustrate what is involved. Memory researchers split between advocating memory systems and advocating memory processes, and I argue that it is the latter approach that provides the critical sort of decomposition and localization for explaining memory. The challenges of linking distinguishable functions with brain processes is illustrated by two examples: competing hypotheses about the contribution of the hippocampus and competing attempts to link areas in frontal cortex with memory processing. (shrink)

Current conceptions of the nature of human reasoning make it no longer tenable to assess children's inference by reference to the norms of logical inference. Alternatively, the complexity of the mental models employed in children's inferences can be analysed. This approach is applied to transitive inference, class inclusion, categorical induction, theory of mind, oddity, categorical syllogisms, analogy, and reasoning deficits. It is argued that a coherent account of children's reasoning emerges in that there is correspondence between tasks at the (...) same level of complexity across different domains, and that the inferences of younger children, while impressive and important, are consistently simpler than those of older children. (shrink)

In the past two or three decades, complexity not only has been a hot research topic but has caught the popular imagination. Terms such as chaos and bifurcation become so common they find their way into Hollywood movies. What is complexity? What is the theory of complexity or the science of complexity? I do not think there is such a thing as the theory of complexity. Not even a rigid definition of complexity exists in (...) the natural sciences. There are many theories trying to address various complex systems. What I try to do is to extract some general ideas that are implicit in these theories, and more generally, in the way that scientists face and think about complicated situations. (shrink)

A short review of complexity research from the perspective of history and philosophy of biology is presented. Complexity and its emergence has scientific and metaphysical meanings. From its beginning, biology was a science of complex systems, but with the advent of electronic computing and the possibility of simulating mathematical models of complicated systems, new intuitions of complexity emerged, together with attempts to devise quantitative measures of complexity. But can we quantify the complex?

\Complexity" is a catchword of certain extremely popular and rapidly developing interdisciplinary new sciences, often called accordingly the sciences of complexity1. It is often closely associated with another notably popular but ambiguous word, \information" information, in turn, may be justly called the central new concept in the whole 20th century science. Moreover, the notion of information is regularly coupled with a key concept of thermodynamics, viz. entropy. And like this was not enough, it is quite usual to add one (...) more, at present extraordinarily popular notion, namely chaos, and wed it with the above-mentioned concepts. (shrink)

bruce@edmonds.name http://bruce.edmonds.name Abstract. Two kinds of problem are distinguished: the first of finding processes which produce complex outcomes from the interaction of simple parts, and the second of finding which process resulted in an observed complex outcome. The former I call the easy complexity problem and the later the hard complexity problem. It is often assumed that progress with the easy problem will aid process with the hard problem. However this assumes that the “reverse engineering” problem, of determining (...) the process from the outcomes is feasible. Taking a couple of simple models of reverse engineering, I show that this task is infeasible in the general case. Hence it cannot be assumed that reverse engineering is possible, and hence that most of the time progress on the easy problem will not help with the hard problem unless there are special properties of a particular set of processes that make it feasible. Assuming that complexity science is not merely an academic “game” and given the analysis of this paper, some criteria for the kinds of paper that have a reasonable chance of being eventually useful for understanding observed complex systems are outlined. Many complexity papers do not fare well against these critieria. (shrink)

Communism may be dead, but a quasi?Marxist critique of liberal democracy survives in the writings of a number of thinkers ? most notably, David Miller and John Dryzek ? who deplore the self?centered apathy of their fellow citizens and defend the radical ideal of deliberative democracy. Inspired mainly by Rousseau and Habermas, this emergent school of thought argues for a more participatory system where the public interest takes precedence over private interest, and where rational argument replaces cynical manipulation. The paper (...) questions whether the deliberative model can cope with the incalculable complexity of modern society. Deliberative democracy, it is contended, rests on doubtful metaphysical assumptions, a blinkered approach to empirical evidence, and a common misapprehension about the nature of political argument. (shrink)

Given any simply consistent formal theory F of the state complexity L(S) of finite binary sequences S as computed by 3-tape-symbol Turing machines, there exists a natural number L(F ) such that L(S) > n is provable in F only if n < L(F ). On the other hand, almost all finite binary sequences S satisfy L(S) > L(F ). The proof resembles Berry’s..

In this chapter we consider economic systems, and in particular financial systems, from the perspective of the physics of complex systems (i.e. statistical physics, the theory of critical phenomena, and their cognates). This field of research is known as econophysics—alternative names are ‘financial physics’ and ‘statistical phynance.’ This title was coined in 1995 by Eugene Stanley, and since then its researchers have attempted to forge it as an independent and important field, one that stands in opposition to standard (‘Neo-Classical’) economic (...) theory. Econophysicists argue that the empirical data is best explained in terms flowing out of statistical physics, according to which the (stylized) facts of economics are best understood as emergent properties of a complex system. However, some economists argue that the methods used by econophysics are not sufficient to prove the existence of underlying complexity in economic systems. The complexity claim can nonetheless be defended as a good example of an inference to the best explanation rather than a definitive deduction. (shrink)

This paper explores consequences of the claim that phenomenal experiences are physical events of great descriptive complexity. This claim is attractive both because it can explain our most perplexing intuitions about the quality of consciousness and also because it is suggestive of very productive research opportunities. I illustrate the former by showing that two of the most compelling anti-physicalist arguments about phenomenal experience – the modal argument of Kripke and the conceivability argument of Chalmers – are not sound if (...) this claim is true. I illustrate the latter by showing that significant empirical predictions are a consequence of this claim. (shrink)

When the whole is greater than the sum of the parts--indeed, so great that the sum far transcends the parts and represents something utterly new and different--we call that phenomenon emergence. When the chemicals diffusing in the primordial waters came together to form the first living cell, that was emergence. When the activities of the neurons in the brain result in mind, that too is emergence. In The Emergence of Everything, one of the leading scientists involved in the study of (...) complexity, Harold J. Morowitz, takes us on a sweeping tour of the universe, a tour with 28 stops, each one highlighting a particularly important moment of emergence. For instance, Morowitz illuminates the emergence of the stars, the birth of the elements and of the periodic table, and the appearance of solar systems and planets. We look at the emergence of living cells, animals, vertebrates, reptiles, and mammals, leading to the great apes and the appearance of humanity. He also examines tool making, the evolution of language, the invention of agriculture and technology, and the birth of cities. And as he offers these insights into the evolutionary unfolding of our universe, our solar system, and life itself, Morowitz also seeks out the nature of God in the emergent universe, the God posited by Spinoza, Bruno, and Einstein, a God Morowitz argues we can know through a study of the laws of nature. Written by one of our wisest scientists, The Emergence of Everything offers a fascinating new way to look at the universe and the natural world, and it makes an important contribution to the dialogue between science and religion. (shrink)

In this paper, we focus attention on the role of computer system complexity in ascribing responsibility. We begin by introducing the notion of technological moral action (TMA). TMA is carried out by the combination of a computer system user, a system designer (developers, programmers, and testers), and a computer system (hardware and software). We discuss three sometimes overlapping types of responsibility: causal responsibility, moral responsibility, and role responsibility. Our analysis is informed by the well-known accounts provided by Hart and (...) Hart and Honoré. While these accounts are helpful, they have misled philosophers and others by presupposing that responsibility can be ascribed in all cases of action simply by paying attention to the free and intended actions of human beings. Such accounts neglect the part played by technology in ascriptions of responsibility in cases of moral action with technology. For both moral and role responsibility, we argue that ascriptions of both causal and role responsibility depend on seeing action as complex in the sense described by TMA. We conclude by showing how our analysis enriches moral discourse about responsibility for TMA. (shrink)

Murray Gell-Mann has proposed the concept of effective complexity as a measure of information content. The effective complexity of a string of digits is defined as the algorithmic complexity of the regular component of the string. This paper argues that the effective complexity of a given string is not uniquely determined. The effective complexity of a string admitting a physical interpretation, such as an empirical data set, depends on the cognitive and practical interests of investigators. (...) The effective complexity of a string as a purely formal construct, lacking a physical interpretation, is either close to zero, or equal to the string's algorithmic complexity, or arbitrary, depending on the auxiliary criterion chosen to pick out the regular component of the string. Because of this flaw, the concept of effective complexity is unsuitable as a measure of information content. (shrink)

Complexity theories are on the way to establish a new worldview—processes instead of objects, history and uniqueness of everything instead of repetition and lawlikeness are the elements. These theories from deterministic chaos via the dissipative structures, the theory of catastrophes, self organization and synergetics are mathematical models, connected with a new understanding of science. They are characterized by new fundamental commitments of sciences. But at the same time, they are characterized by epistemic boundaries.

The European Corporate Sustainability Framework (ECSF) is a new generation management framework, aimed to meet increased corporate complexity and support corporate transformation towards more sustainable ways of doing business. It is a multi-layer, integral business framework with an analytical, contextual, situational and dynamic dimension.Analytically, the framework is structured according to four focus points – the constitutional, conceptual, behavioural and evaluative perspective – providing integrative designs of complex and dynamic phenomena. The framework includes coherent sets of business philosophies, approaches, concepts (...) and tools that structures corporate realities and generates sequences of steps in order to obtain adequate institutional structures, a road to corporate transformation and higher performance levels. (shrink)

A central theme in F. A. Hayek’s work is the contrast between principles and expediency, and the insistence that governments follow abstract general principles rather than pursue apparently expedient social and economic policies that seek to make us better off.2 This is a radical and striking thesis, especially from an economist: governments should abjure the pursuit of social and economic policies that aim to improve welfare and, instead, adhere to moral principles. In this chapter I defend this radical claim. I (...) begin by explicating and defending Hayek’s argument against the pursuit of expediency based on his analysis of economic and social complexity. I then turn to a rather more critical examination of his evolutionary account of moral principles. (shrink)

The formal theory of the Model of Hierarchical Complexity is presented. Complexity theories generally exclude the concept of hierarchical complexity; Developmental Psychology has included it for over 20 years. It also applies to social systems and non-human systems. Formal axioms for the Model are outlined. The model assigns an order of hierarchical complexity to every task, using natural numbers, establishing a quantal notion of stage and stages of performance. This formalizes properties of stage theories in psychology. (...) The formal theory of the model enables extending the concept of hierarchical complexity to any field where tasks and their performances exist. (shrink)

The consensus among evolutionists seems to be (and has been for at least a century) that the morphological complexity of organisms increases in evolution, although almost no empirical evidence for such a trend exists. Most studies of complexity have been theoretical, and the few empirical studies have not, with the exception of certain recent ones, been especially rigorous; reviews are presented of both the theoretical and empirical literature. The paucity of evidence raises the question of what sustains the (...) consensus, and a number of suggestions are offered, including the possibility that certain cultural and/or perceptual biases are at work. In addition, a shift in emphasis from theoretical to empirical inquiry is recommended for the study of complexity, and guidelines for future empirical studies are proposed. (shrink)

The theories of complexity comprise a system of great breadth. But what is included under this umbrella? Here we attempt a portrait of complexity theory, seen through the lens of complexity theory itself. That is, we portray the subject as an evolving complex dynamical system, or social network, with bifurcations, emergent properties, and so on. This is a capsule history covering the twentieth century. Extensive background data may be seen at www.visual-chaos.org/complexity.

The common opinion has been that evolution results in the continuing development of more complex forms of life, generally understood as more complex organisms. The arguments supporting that opinion have recently come under scrutiny and been found wanting. Nevertheless, the appearance of increasing complexity remains. So, is there some sense in which evolution does grow complexity? Artificial life simulations have consistently failed to reproduce even the appearance of increasing complexity, which poses a challenge. Simulations, as much as (...) scientific theories, are obligated at least to save the appearances! We suggest a relation between these two problems, understanding biological complexity growth and the failure to model even its appearances. We present a different understanding of that complexity which evolution grows, one that genuinely runs counter to entropy and has thus far eluded proper analysis in information-theoretic terms. This complexity is reflected best in the increase in niches within the biosystem as a whole. Past and current artificial life simulations lack the resources with which to grow niches and so to reproduce evolution’s complexity. We propose a more suitable simulation design integrating environments and organisms, allowing old niches to change and new ones to emerge. (shrink)

We present a minimum message length (MML) framework for trajectory partitioning by point selection, and use it to automatically select the tolerance parameter ε for Douglas-Peucker partitioning, adapting to local trajectory complexity. By examining a range of ε for synthetic and real trajectories, it is easy to see that the best ε does vary by trajectory, and that the MML encoding makes sensible choices and is robust against Gaussian noise. We use it to explore the identification of micro-activities within (...) a longer trajectory. This MML metric is comparable to the TRACLUS metric – and shares the constraint of abstracting only by omission of points – but is a true lossless encoding. Such encoding has several theoretical advantages – particularly with very small segments (high frame rates) – but actual performance interacts strongly with the search algorithm. Both differ from unconstrained piecewise linear approximations, including other MML formulations. (shrink)

Does biology have general laws that apply to all levels of biological organisation, across all evolutionary time? In their book “Biology’s first law: the tendency for diversity and complexity to increase in evolutionary systems” (2010), Daniel McShea and Robert Brandon propose that the most fundamental law of biology is that all levels of biological organisation have an underlying tendency to become more complex and diverse over time. A range of processes, most notably selection, can prevent the expression of this (...) tendency, but they predict that, on average, we should see that lineages tend toward greater diversity and complexity, driven by fundamentally neutral processes. Their hypothesis can be summarised as “diversity is easy, stasis is hard”. Here, I consider evidence for this “zero force evolutionary law”. It provides a fair description of evolutionary change at the genomic level, but the predictions of the proposed law are not met for broad scale patterns in the evolution of the animal kingdom. (shrink)

In the present paper I develop a model of the evolutionary process associated to the widespread although controversial notion of a prevailing trend of increasing complexity over time. The model builds on a coupling of evolution to individual developmental programs and introduces an integrated view of evolution implying that human culture and science form a continuous extension of organic evolution. It is formed as a mathematical model that has made possible a quantitative estimation in relative terms of the growth (...) of complexity. This estimation is accomplished by means of computer simulations the result of which indicates a strong acceleration of complexity all the way from the appearance of multicellular organisms up to modern man. (shrink)

Evidence can be complex in various ways: e.g., it may exhibit structural complexity, containing information about causal, hierarchical or logical structure as well as empirical data, or it may exhibit combinatorial complexity, containing a complex combination of kinds of information. This paper examines evidential complexity from the point of view of Bayesian epistemology, asking: how should complex evidence impact on an agent’s degrees of belief? The paper presents a high-level overview of an objective Bayesian answer: it presents (...) the objective Bayesian norms concerning the relation between evidence and degrees of belief, and goes on to show how evidence of causal, hierarchical and logical structure lead to natural constraints on degrees of belief. The objective Bayesian network formalism is presented, and it is shown how this formalism can be used to handle both kinds of evidential complexity—structural complexity and combinatorial complexity. (shrink)

Edgar Morin took an early lead within the French intellectual community, but also in comparison with parallel reflections in the English-speaking world, as far as critical discussion of the epistemology of the new sciences of complexity is concerned. His "complex thought" raises many intriguing questions and offers a dazzling synthesis of a wide range of fields, from physics to biology to psychology and the social sciences. However, Morin's road to complexity bypasses some crucial issues in philosophy and political (...) economy. Therefore, although Morin's insights remain invaluable, one has reasons to be a little skeptical about the exact nature of the reform of thought he has sketched out. (shrink)

This article considers the implications of complex systems models for the study of economics and the evaluation of public policies. I argue that complexity can enhance current approaches to formal economic analysis, but does so in ways that complement current approaches. I further argue that while complexity can influence how public policy analysis is conducted, it does not delimit the use of consequentialist approaches to policy comparison to the degree initially suggested by Hayek and most recently defended by (...) Gaus. (shrink)

Larmore aims to recover three forms of moral complexity that have often been neglected by moral and political philosophers. First, he argues that virtue is not simply the conscientious adherence to principle. Rather, the exercise of virtue apply. He argues - and this is the second pattern of complexity - that recognizing the value of constitutive ties with shared forms of life does not undermine the liberal ideal of political neutrality toward differing ideals of the good life. Finally (...) Larmore agrues for what he calls the heterogeneity of morality. Moral thinking need not be exclusively deontological or consequentialist, and we should recognize that the ultimate sources of moral value are diverse. The arguments presented here do not attack the possibility of moral theory. But in addressing some of the central issues of moral and political thinking today thay attempt to restore to that thinking greater flexibility and a necessary sensitivity to our common experience. (shrink)

I argue that considerations about computational complexity show that all finite agents need characteristics like those that have been called epistemic virtues. The necessity of these virtues follows in part from the nonexistence of shortcuts, or efficient ways of finding shortcuts, to cognitively expensive routines. It follows that agents must possess the capacities – metavirtues –of developing in advance the cognitive virtues they will need when time and memory are at a premium.

Some problems rarely discussed in traditional philosophy of science are mentioned: The empirical sciences using mathematico-quantitative theoretical models are frequently confronted with several types of computational problems posing primarily methodological limitations on explanatory and prognostic matters. Such limitations may arise from the appearances of deterministic chaos and (too) high computational complexity in general. In many cases, however, scientists circumvent such limitations by utilizing reductional approximations or complexity reductions for intractable problem formulations, thus constructing new models which are (...) computationally tractable. Such activities are compared with reduction types (more) established in philosophy of science. (shrink)