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

Seven problems that occur in attempts to measure complexity are pointed out as they occur in four proposed measurement techniques. Each example method is an improvement over the previous examples. It turns out, however, that none are up to the challenge of complexity. Apparently, there is no currently available method that truly gets the measure of complexity. There are two reasons. First, the most natural approach, quantitative analysis, is rendered inadequate by the very nature of complexity. (...) Second, the intrinsic magnitude of complexity is still holding at bay attempts to use both quantitative and qualitative methods combined. Further progress in complexity science and in systems science is required. Any method that simplifies will fail because it ignores what complexity is. Techniques of understanding that do not simplify, but rather provide ways for the mind to grasp and work with complexity are more effective in getting its measure. (shrink)

Can an agent's intelligence level be negative? We extend the Legg-Hutter agent-environment framework to include punishments and argue for an affirmative answer to that question. We show that if the background encodings and Universal Turing Machine (UTM) admit certain Kolmogorov complexity symmetries, then the resulting Legg-Hutter intelligence measure is symmetric about the origin. In particular, this implies reward-ignoring agents have Legg-Hutter intelligence 0 according to such UTMs.

The aim of this paper is to comprehensively question the validity of the standard way of interpreting Chaitin's famous incompleteness theorem, which says that for every formalized theory of arithmetic there is a finite constant c such that the theory in question cannot prove any particular number to have Kolmogorov complexity larger than c. The received interpretation of theorem claims that the limiting constant is determined by the complexity of the theory itself, which is assumed to be (...) good measure of the strength of the theory. I exhibit certain strong counterexamples and establish conclusively that the received view is false. Moreover, I show that the limiting constants provided by the theorem do not in any way reflect the power of formalized theories, but that the values of these constants are actually determined by the chosen coding of Turing machines, and are thus quite accidental. (shrink)

The framework of Solomonoff prediction assigns prior probability to hypotheses inversely proportional to their Kolmogorov complexity. There are two well-known problems. First, the Solomonoff prior is relative to a choice of Universal Turing machine. Second, the Solomonoff prior is not computable. However, there are responses to both problems. Different Solomonoff priors converge with more and more data. Further, there are computable approximations to the Solomonoff prior. I argue that there is a tension between these two responses. This is (...) because computable approximations to Solomonoff prediction do not always converge. (shrink)

Early work on the frequency theory of probability made extensive use of the notion of randomness, conceived of as a property possessed by disorderly collections of outcomes. Growing out of this work, a rich mathematical literature on algorithmic randomness and Kolmogorov complexity developed through the twentieth century, but largely lost contact with the philosophical literature on physical probability. The present chapter begins with a clarification of the notions of randomness and probability, conceiving of the former as a property (...) of a sequence of outcomes, and the latter as a property of the process generating those outcomes. A discussion follows of the nature and limits of the relationship between the two notions, with largely negative verdicts on the prospects for any reduction of one to the other, although the existence of an apparently random sequence of outcomes is good evidence for the involvement of a genuinely chancy process. (shrink)

There are (at least) three approaches to quantifying information. The first, algorithmic information or Kolmogorov complexity, takes events as strings and, given a universal Turing machine, quantifies the information content of a string as the length of the shortest program producing it [1]. The second, Shannon information, takes events as belonging to ensembles and quantifies the information resulting from observing the given event in terms of the number of alternate events that have been ruled out [2]. The third, (...) statistical learning theory, has introduced measures of capacity that control (in part) the expected risk of classifiers [3]. These capacities quantify the expectations regarding future data that learning algorithms embed into classifiers. Solomonoff and Hutter have applied algorithmic information to prove remarkable results on universal induction. Shannon information provides the mathematical foundation for communication and coding theory. However, both approaches have shortcomings. Algorithmic information is not computable, severely limiting its practical usefulness. Shannon information refers to ensembles rather than actual events: it makes no sense to compute the Shannon information of a single string – or rather, there are many answers to this question depending on how a related ensemble is constructed. Although there are asymptotic results linking algorithmic and Shannon information, it is unsatisfying that there is such a large gap – a difference in kind – between the two measures. This note describes a new method of quantifying information, effective information, that links algorithmic information to Shannon information, and also links both to capacities arising in statistical learning theory [4, 5]. After introducing the measure, we show that it provides a non-universal analog of Kolmogorov complexity. We then apply it to derive basic capacities in statistical learning theory: empirical VC-entropy and empirical Rademacher complexity. A nice byproduct of our approach is an interpretation of the explanatory power of a learning algorithm in terms of the number of hypotheses it falsifies [6], counted in two different ways for the two capacities. We also discuss how effective information relates to information gain, Shannon and mutual information. (shrink)

While scientific inquiry crucially relies on the extraction of patterns from data, we still have a far from perfect understanding of the metaphysics of patterns—and, in particular, of what makes a pattern real. In this paper we derive a criterion of real-patternhood from the notion of conditional Kolmogorov complexity. The resulting account belongs to the philosophical tradition, initiated by Dennett :27–51, 1991), that links real-patternhood to data compressibility, but is simpler and formally more perspicuous than other proposals previously (...) defended in the literature. It also successfully enforces a non-redundancy principle, suggested by Ladyman and Ross, that aims to exclude from real-patternhood those patterns that can be ignored without loss of information about the target dataset, and which their own account fails to enforce. (shrink)

I argue for patternism, a new answer to the question of when some objects compose a whole. None of the standard principles of composition comfortably capture our natural judgments, such as that my cat exists and my table exists, but there is nothing wholly composed of them. Patternism holds, very roughly, that some things compose a whole whenever together they form a “real pattern”. Plausibly we are inclined to acknowledge the existence of my cat and my table but not of (...) their fusion, because the first two have a kind of internal organizational coherence that their putative fusion lacks. Kolmogorov complexity theory supplies the needed rigorous sense of “internal organizational coherence”. (shrink)

Ontic Structural Realism, as pivotal position in philosophy of science and metaphysics, defends the idea that the world is ultimately constituted of real physical structures. French (2014) regards physical symmetries as the foundational structure of a world without objects. On the other hand, Ladyman and Ross (2007) hold that the world is essentially made of non-redundant informational structure. I argue in this paper that these two positions are by no means incompatible, for instance by interpreting French’s physical symmetries as real (...) structures both encompassing and compressing every piece of information (Kolmogorov complexity) within the world. (shrink)

Applying the concepts of Kolmogorov-Chaitin complexity and Turing’s uncomputability from the computability and algorithmic information theories to the irreducible and incomputable randomness of quantum mechanics, a novel argument for the existence of God is presented. Concepts of ‘transintelligence’ and ‘transcausality’ are introduced, and from them, it is posited that our universe must be epistemologically and ontologically an open universe. The proposed idea also proffers a new perspective on the nonlocal nature and the infamous wave-function-collapse problem of quantum mechanics.

Review of "Exploring Randomness" (200) and "The Unknowable" (1999) by Gregory Chaitin.

Given functionally equivalent minds, how does the expected quantity of their conscious experience differ across different substrates and how could we calculate this? We argue that a realistic digital brain emulation would be orders of magnitude less conscious than a real biological brain. On the other hand, a mind running on neuromorphic hardware or a quantum computer could in principle be more conscious than than a biological brain.

The Kolmogorov-Sinai entropy is a fairly exotic mathematical concept which has recently aroused some interest on the philosophers’ part. The most salient trait of this concept is its working as a junction between such diverse ambits as statistical mechanics, information theory and algorithm theory. In this paper I argue that, in order to understand this very special feature of the Kolmogorov-Sinai entropy, is essential to reconstruct its genealogy. Somewhat surprisingly, this story takes us as far back as the (...) beginning of celestial mechanics and through some of the most exciting developments of mathematical physics of the 19th century. (shrink)

In the present paper we discuss: a) how Hilbert’s unification program failed completely, and b) we outline a new electron model based on Helmholtz’s electron vortex and Kolmogorov theory of turbulence. Novelty aspect: we discuss among other things, electron capture event, and von Karman vortex street. We also discuss a new model of origination of charge and matter. This paper is a sequel to a preceding paper on similar theme.

Standard semantic theories predict that non-deictic readings for complex demonstratives should be much more widely available than they in fact are. If such readings are the result of a lexical ambiguity, as Kaplan (1977) and others suggest, we should expect them to be available wherever a definite description can be used. The same prediction follows from ‘hidden argument’ theories like the ones described by King (2001) and Elbourne (2005). Wolter (2006), however, has shown that complex demonstratives admit non-deictic interpretations only (...) when a precise set of structural constrains are met. In this paper, I argue that Wolter’s results, properly understood, upend the philosophical status quo. They fatally undermine the ambiguity theory and demand a fundamental rethinking of the hidden argument approach. (shrink)

I have two aims for the present paper, one narrow and one broad. The narrow aim is to show that a class of data originally described by Lynsey Wolter empirically undermine the leading treatments of complex demonstratives that have been described in the literature. The broader aim of the paper is to show that Wolter demonstratives, as I will call the constructions I focus on, are a threat not just to existing treatments, but to any possible theory that retains the (...) uncontroversial assumptions that relative clauses always form a constituent with the nouns they modify, and that semantic composition proceeds sequentially and locally, with the inputs to interpretation having the structure syntax tells us they do. (shrink)

This article introduces and defends the “pathological complexity thesis” as a hypothesis about the evolutionary origins of minimal consciousness, or sentience, that connects the study of animal consciousness closely with work in behavioral ecology and evolutionary biology. I argue that consciousness is an adaptive solution to a design problem that led to the extinction of complex multicellular animal life following the Avalon explosion and that was subsequently solved during the Cambrian explosion. This is the economic trade-off problem of having (...) to deal with a complex body with high degrees of freedom, what I call “pathological complexity.” By modeling the explosion of this computational complexity using the resources of state-based behavioral and life history theory we will be able to provide an evolutionary bottom-up framework to make sense of subjective experience and its function in nature by paying close attention to the ecological lifestyles of different animals. (shrink)

An accurate classification of bacteria is essential for the proper identification of patient infections and subsequent treatment decisions. Multi-Locus Sequence Typing (MLST) is a genetic technique for bacterial classification. MLST classifications are used to cluster bacteria into clonal complexes. Importantly, clonal complexes can serve as a biological species concept for bacteria, facilitating an otherwise difficult taxonomic classification. In this paper, we argue for the inclusion of terms relating to clonal complexes in biomedical ontologies.

According to Leibniz’s infinite-analysis account of contingency, any derivative truth is contingent if and only if it does not admit of a finite proof. Following a tradition that goes back at least as far as Bertrand Russell, several interpreters have been tempted to explain this biconditional in terms of two other principles: first, that a derivative truth is contingent if and only if it contains infinitely complex concepts and, second, that a derivative truth contains infinitely complex concepts if and only (...) if it does not admit of a finite proof. A consequence of this interpretation is that Leibniz’s infinite-analysis account of contingency falls prey to Robert Adams’s Problem of Lucky Proof. I will argue that this interpretation is mistaken and that, once it is properly understood how the idea of an infinite proof fits into Leibniz’s circle of modal notions, the problem of lucky proof simply disappears. (shrink)

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

Throughout most of the 20th century, analytical and reductionist approaches have dominated in biological, social, and humanistic sciences, including linguistics and communication. We generally believed we could account for fundamental phenomena in invoking basic elemental units. Although the amount of knowledge generated was certainly impressive, we have also seen limitations of this approach. Discovering the sound formants of human languages, for example, has allowed us to know vital aspects of the ‘material’ plane of verbal codes, but it tells us little (...) about significant aspects of their social functions. I firmly believe, therefore, that alongside a linguistics that looks ‘inward’ there should also be a linguistics that looks ‘outward’, or one even that is constructed ‘from the outside’, a linguistics that I refer to elsewhere as ‘holistic’ though it could be identified by a different name. My current vision is to promote simultaneously the perspective that goes from the part to the whole and that which goes from the whole to the parts, i.e., both from the top down and from the bottom up. This goal is shared with other disciplines which recognize that many phenomena related to life are interwoven, self-organising, emergent and processual. Thus, we need to re-examine how we have conceived of reality, both the way we have looked at it and the images we have used to talk about it. Several approaches now grouped under the label of complexity have been elaborated towards this objective of finding new concepts and ways of thinking that better fit the complex organisation of facts and events. (shrink)

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

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

Some things look more complex than others. For example, a crenulate and richly organized leaf may seem more complex than a plain stone. What is the nature of this experience—and why do we have it in the first place? Here, we explore how object complexity serves as an efficiently extracted visual signal that the object merits further exploration. We algorithmically generated a library of geometric shapes and determined their complexity by computing the cumulative surprisal of their internal skeletons—essentially (...) quantifying the “amount of information” within each shape—and then used this approach to ask new questions about the perception of complexity. Experiments 1–3 asked what kind of mental process extracts visual complexity: a slow, deliberate, reflective process (as when we decide that an object is expensive or popular) or a fast, effortless, and automatic process (as when we see that an object is big or blue)? We placed simple and complex objects in visual search arrays and discovered that complex objects were easier to find among simple distractors than simple objects are among complex distractors—a classic search asymmetry indicating that complexity is prioritized in visual processing. Next, we explored the function of complexity: Why do we represent object complexity in the first place? Experiments 4–5 asked subjects to study serially presented objects in a self‐paced manner (for a later memory test); subjects dwelled longer on complex objects than simple objects—even when object shape was completely task‐irrelevant—suggesting a connection between visual complexity and exploratory engagement. Finally, Experiment 6 connected these implicit measures of complexity to explicit judgments. Collectively, these findings suggest that visual complexity is extracted efficiently and automatically, and even arouses a kind of “perceptual curiosity” about objects that encourages subsequent attentional engagement. (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)

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

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

Is a social complex identical to many united people or is it a group entity in addition to the people? For specificity, I will assume that a social complex is a plural subject in Margaret Gilbert’s sense. By appeal to my theory of Aspects, according to which there can be qualitative difference without numerical difference, I give an answer that is a middle way between metaphysical individualism and metaphysical holism. This answer will enable answers to two additional metaphysical questions: (i) (...) how can two social complexes have all the same members and (ii) how can there be a social complex of social complexes? (shrink)

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

Is native speaker variation in understanding complex sentences due to individual differences in working memory capacity or in syntactic competence? The answer to this question has very important consequences for both theoretical and applied concerns in linguistics and education. This book is distinctive in giving an historical and interdisciplinary perspective on the rule- based and experience-based debate and in supporting an integrated account. In the study reported here, variation was found to be due to differences in syntactic competence and the (...) author argues that sentence comprehension is a learned skill, displaying many of the general characteristics of cognitive skills. The book will be stimulating reading for psycholinguists, theoretical linguists, applied linguists and educators. (shrink)

Ellie Anderson had always known that she wanted to have children. Her mother, Louise, was aware of this wish. Ellie was designated male at birth, but according to news sources, identified as a girl from the age of three. She was hoping to undergo gender reassignment surgery at 18, but died unexpectedly at only 16, leaving Louise grappling not only with the grief of losing her daughter, but with a complex legal problem. Ellie had had her sperm frozen before starting (...) hormone treatment, specifically so that she would retain the chance of becoming a parent after her gender reassignment. Ellie had considered what might happen to the sperm if she died and was adamant that her children should be brought into the world. She made her mother promise to ensure that this would happen. But according to UK law, Ellie’s mother has no legal right to retain her sperm, or to use it to fulfil Ellie’s wishes. In this paper, we raise several key ethical questions on this case, namely: does a refusal to bring Ellie’s children into the world wrong her posthumously? Is Ellie’s mother morally entitled to use her daughter’s sperm as Ellie wished? Should the fact that Ellie was a minor at the time of her death or the fact that she was transgendered undermine her wish to have children? Can Ellie become a parent posthumously? We consider how these complex ethical questions could be approached. (shrink)

We review the recently proposed universal concept of dynamic complexity and its new mathematics based on the unreduced interaction problem solution. We then consider its progress-bringing applications at various levels of complex world dynamics, including complex-dynamical nanometal physics and living condensed matter, unreduced nanobiosystem dynamics and the integral medicine concept, causally complete management of complex economical and social dynamics, and the ensuing concept of truly sustainable world governance.

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)

This paper articulates a case for supposing that all shades of colour are visually complex.

In response to my argument against Aristotle’s claim that humans are more political than other animals, Edward Jacobs counters that the evidence I use from cognitive ethology and my application of evolutionary principles fail to demonstrate that other animals are as political as humans. Jacobs furthermore suggests that humans are more political than other animals by pointing to the political variation in human communities. In this article, I defend my use of evolutionary principles and my interpretation of anecdotes from cognitive (...) ethology, while challenging Jacobs’s assertion that human political variation implies that humans are more political than other animals. (shrink)

We analyse the computational complexity of three problems in judgment aggregation: (1) computing a collective judgment from a profile of individual judgments (the winner determination problem); (2) deciding whether a given agent can influence the outcome of a judgment aggregation procedure in her favour by reporting insincere judgments (the strategic manipulation problem); and (3) deciding whether a given judgment aggregation scenario is guaranteed to result in a logically consistent outcome, independently from what the judgments supplied by the individuals are (...) (the problem of the safety of the agenda). We provide results both for specific aggregation procedures (the quota rules, the premisebased procedure, and a distance-based procedure) and for classes of aggregation procedures characterised in terms of fundamental axioms. (shrink)

This is a contribution to the encyclopedia of systems biology on complexity.

Research on future-oriented mental time travel (FMTT) is highly active yet somewhat unruly. I believe this is due, in large part, to the complexity of both the tasks used to test FMTT and the concepts involved. Extraordinary care is a necessity when grappling with such complex and perplexing metaphysical constructs as self and time and their co-instantiation in memory. In this review, I first discuss the relation between future mental time travel and types of memory (episodic and semantic). I (...) then examine the nature of both the types of self-knowledge assumed to be projected into the future and the types of temporalities that constitute projective temporal experience. Finally, I argue that a person lacking episodic memory should nonetheless be able to imagine a personal future by virtue of (a) the fact that semantic, as well as episodic, memory can be self-referential, (b) autonoetic awareness is not a prerequisite for FMTT, and (c) semantic memory does, in fact, enable certain forms of personally-oriented FMTT. (shrink)

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

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

Unified and causal complex-dynamic origin of standard (special and general) relativistic and quantum effects revealed previously at the lowest levels of world interaction dynamics is explicitly generalised to all higher levels of unreduced interaction processes, thus additionally confirming the causally complete character of complex-dynamical, naturally quantised relativity, which does not contain any artificially added, abstract postulates. We demonstrate some elementary applications of this generalised quantum relativity at higher levels of complex brain and social interaction dynamics.

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

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

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

Background: how mind functions is subject to continuing scientific discussion. A simplistic approach says that, since no convincing way has been found to model subjective experience, mind cannot exist. A second holds that, since mind cannot be described by classical physics, it must be described by quantum physics. Another perspective concerns mind's hypothesized ability to interact with the world of quanta: it should be responsible for reduction of quantum wave packets; physics producing 'Objective Reduction' is postulated to form the basis (...) for mind-matter interactions. This presentation describes results derived from a new approach to these problems. It is based on well-established biology involving physics not previously applied to the fields of mind, or consciousness studies, that of critical feedback instability. -/- Methods: 'self-organized criticality' in complexity biology places system loci of control at critical instabilities, physical properties of which, including information properties, are presented. Their elucidation shows that they can model hitherto unexplained properties of experience. -/- Results: All results depend on physical properties of critical instabilities. First, at least one feed-back or feed-forward loop must have feedback gain, g = 1: information flows round the loop impress perfect images of system states back on themselves: they represent processes of perfect self-observation. This annihilates system quanta: system excitations are instability fluctuations, which cannot be quantized. Major results follow: -/- 1. Information vectors representing criticality states must include at least one attached information loop denoting self-observation. -/- 2. Such loop structures are attributed a function, 'registering the state's own existence', explaining -/- a. Subjective 'awareness of one's own presence' -/- b. How content-free states of awareness can be remembered (Jon Shear) -/- c. Subjective experience of time duration (Immanuel Kant) -/- d. The 'witness' property of experience – often mentioned by athletes 'in the zone' -/- e. The natural association between consciousness and intelligence -/- This novel, physically and biologically sound approach seems to satisfactorily model subjectivity. -/- Further significant results follow: -/- 1. Registration of external information in excited states of systems at criticality reduces external wave-packets: the new model exhibits 'Objective Reduction' of wave packets. -/- 2. High internal coherence (postulated by Domash & Penrose) leading to a. Non-separable information vector bundles. b. Non-reductive states (Chalmers's criterion for experience). -/- 3. Information that is: a. encoded in coherence negentropy; b. non-digitizable, and therefore c. computationally without digital equivalent (posited by Penrose). -/- Discussion and Conclusions: instability physics implies anharmonic motion, preventing excitation quantization, and totally different from the quantum physics of simple harmonic motion at stability. Instability excitations are different from anything hitherto conceived in information science. They can model aspects of mind never previously treated, including genuine subjectivity, objective reduction of wave-packets, and inter alia all properties given above. (shrink)

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

Representing species-specific proteins and protein complexes in ontologies that are both human and machine-readable facilitates the retrieval, analysis, and interpretation of genome-scale data sets. Although existing protin-centric informatics resources provide the biomedical research community with well-curated compendia of protein sequence and structure, these resources lack formal ontological representations of the relationships among the proteins themselves. The Protein Ontology (PRO) Consortium is filling this informatics resource gap by developing ontological representations and relationships among proteins and their variants and modified forms. Because (...) proteins are often functional only as members of stable protein complexes, the PRO Consortium, in collaboration with existing protein and pathway databases, has launched a new initiative to implement logical and consistent representation of protein complexes. We describe here how the PRO Consortium is meeting the challenge of representing species-specific protein complexes, how protein complex representation in PRO supports annotation of protein complexes and comparative biology, and how PRO is being integrated into existing community bioinformatics resources. The PRO resource is accessible at http://pir.georgetown.edu/pro/. (shrink)