The complexity that we observe in nature can often be explained in terms of cooperative behavior. For example, the major transitions of evolution required the emergence of cooperation among the lower-level units of selection, which led to specialization through division-of-labor ultimately resulting in spontaneous order. There are two aspects to address explaining how such cooperation is sustained: how free-riders are prevented from free-riding on the benefits of cooperative tasks, and just as importantly, how those social benefits arise. We review (...) these problems from an economic perspective, and highlight how ideas from economics can help us to better understand how the benefits of social interactions arise, how they are sustained, and how they affect the underlying social dilemmas. (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)

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

This paper examines the adequacy of causal graph theory as a tool for modeling biological phenomena and formalizing biological explanations. I point out that the causal graph approach reaches it limits when it comes to modeling biological phenomena that involve complex spatial and structural relations. Using a case study from molecular biology, DNA-binding and -recognition of proteins, I argue that causal graph models fail to adequately represent and explain causal phenomena in this field. The inadequacy of these (...) models is due to their failure to include relevant spatial and structural information in a way that does not render the model non-explanatory, unmanageable, or inconsistent with basic assumptions of causal graph theory. (shrink)

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

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

The development of language can be considered the second major innovation of humankind after the use of fire. It significantly improves communication efficiency and enables abstract conceptual thinking among humans. Although human language is learned through socially mediated interactions, humans might not be capable of complex language usage without biological evolutions.

This selective review explores biologically inspired learning as a model for intelligent robot control and sensing technology on the basis of specific examples. Hebbian synaptic learning is discussed as a functionally relevant model for machine learning and intelligence, as explained on the basis of examples from the highly plastic biological neural networks of invertebrates and vertebrates. Its potential for adaptive learning and control without supervision, the generation of functional complexity, and control architectures based on self-organization is brought forward. (...) Learning without prior knowledge based on excitatory and inhibitory neural mechanisms accounts for the process through which survival-relevant or task-relevant representations are either reinforced or suppressed. The basic mechanisms of unsupervised biological learning drive synaptic plasticity and adaptation for behavioral success in living brains with different levels of complexity. The insights collected here point toward the Hebbian model as a choice solution for “intelligent” robotics and sensor systems. Keywords: Hebbian learning; synaptic plasticity; neural networks; self-organization; brain; reinforcement; sensory processing; robot control . (shrink)

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

The selected effects or ‘etiological’ theory of Proper function is a naturalistic and realist account of biological teleology. It is used to analyse normativity in philosophy of language, philosophy of mind, philosophy of medicine and elsewhere. The theory has been developed with a simple and intuitive view of natural selection. Traits are selected because of their positive effects on the fitness of the organisms that have them. These ‘selected effects’ are the Proper functions of the traits. Proponents argue that (...) this analysis of biological teleology has the unique advantage that the selected effect function of a trait is also a causal explanation of the trait: the trait exists because it performs this function. We show, however, that selected effect functions as currently defined explain the existence of traits only under highly restrictive assumptions about evolutionary dynamics. In many common scenarios in which traits evolve by natural selection, selected effect functions do not explain those traits. This is because definitions of selected effect function extract from any evolutionary scenario only the information that would be explanatorily relevant in the simple evolutionary scenario implicit in those definitions. When applied to more complex scenarios selected effect functions omit the key information that is explanatorily relevant in those scenarios. The assumptions required for selected effect functions to be explanatory are particularly unlikely to hold in the domain that its proponents care most about - the evolution of representation. A more adequate selected effects theory of Proper functions may be possible, but will require much greater attention to the structure of actual evolutionary explanations. (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)

The impressive variation amongst biological individuals generates many complexities in addressing the simple-sounding question what is a biological individual? A distinction between evolutionary and physiological individuals is useful in thinking about biological individuals, as is attention to the kinds of groups, such as superorganisms and species, that have sometimes been thought of as biological individuals. More fully understanding the conceptual space that biological individuals occupy also involves considering a range of other concepts, such as life, (...) reproduction, and agency. There has been a focus in some recent discussions by both philosophers and biologists on how evolutionary individuals are created and regulated, as well as continuing work on the evolution of individuality. (shrink)

The context for biological emergence is modular hierarchical structures; their existence is what enables functional complexity to arise. Because of the openness of organisms to their environment, complete initial data (position, momentum) of all particles making up their structure is insufficient to determine future outcomes, because unpredictable new matter, energy, and information impacts each organism from the exterior. Consequently, through Darwinian evolution, life has developed processes to handle this issue functionally on short time scales as well on longer (...) developmental timescales. Symbolism and technology are the transforming factors handling this issue at the social levels, which is where the most sophisticated outcomes of openness occur. Considering the cosmological context, the issue is, should the universe itself be regarded as an open system over time? I make the case that is indeed so, because radically new outcomes occur such as the existence of aircraft, iPads, and the internet, which could not plausibly have been encoded in some form of data on the Last Scattering Surface in the expanding universe. (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)

This article considers the novel approach for epistemological interpretation of biomimetics or bionics and biosimilarity in different abiogenetic works with the terminological correction for elimination of the reifications (concretisms, hypostatizations), simplified metaphors and the results of metonymy. In the last part of this article one can see the analysis of the mistakes and problems of complex abiogenetic or supramolecular evolution projects within the aspects of the Conway law and the social organization of science and publishing sphere in subjective postmodern capitalistic (...) conditions. (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)

This paper explores the evolution of consciousness and subjectivity through a biological framework for understanding the universe. It posits that functional patterns in biological systems mirror cosmic mathematical principles, defining our objective reality. Similar to wave and Fibonacci patterns in different physical phenomena, biological patterns are intrinsic to all things and can be quantified using Dedre Gentner’s approach to analogy. For example, Earth’s ocean currents and the melting and freezing of Antarctica resemble the circulatory system and heart, (...) while the production of music from instruments is analogous to ribosomal protein synthesis. Shelves, tables, and chairs function like cytosol by holding objects in space, and a coffee cup mirrors red blood cell distribution. These analogies reveal a universal order rooted in biology’s functional patterns, essential for shaping the emergence and evolution of life. The paper traces the development of consciousness from its rudimentary cellular states to complex human cognition, highlighting the role of biologically-patterned environments in driving evolutionary changes. Organisms that recognized and organized according to these patterns evolved as “pattern recognition engines” crucial for survival—thus, Life (survival) being the primary measure of consciousness. Humans advanced this capacity, gaining cognitive freedom by insulating themselves from environmental constraints, but faced increased subjectivity complexity. Consciousness, while inherently subjective and necessary for cognitive development, evolves in humans to understand objective reality and its biological nature, so to understand and navigate complexities of subjectivity. Ultimately, the paper asserts consciousness is the ability to recognize and adhere to life-sustaining patterns and principles. Successful adherence deems them worthy of continued existence, while failure leads to demise. Parallels with historical concepts like Atman and Brahman in Hinduism suggest ancient recognition of the correspondence between human and universal patterns. This ancient understanding, coupled with modern scientific evidence, reveals the interconnectedness and intrinsic biological nature of reality. (shrink)

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

The purpose of this paper is to discuss the biological plausibility of the pace of creation written in the genesis. A fascinating hypothesis is made on the central role of serotonin as a guide, as the director of the phenomena that enable the best use of light by the plant world, the growth, the regulation of mood in the complex molecular interactions that characterize the varying levels of consciousness. This hypothesis provides biological interpretations of the c.

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.

Biological sexes (male, female, hermaphrodite) are defined by different gametic strategies for reproduction. Sexes are regions of phenotypic space which implement those gametic reproductive strategies. Individual organisms pass in and out of these regions – sexes - one or more times during their lives. Importantly, sexes are life-history stages rather than applying to organisms over their entire lifespan. This fact has been obscured by concentrating on humans, and ignoring species which regularly change sex, as well as those with non-genetic (...) or facultatively genetic sex determination systems. But the general point applies equally to humans. Assigning sexes to pre-reproductive life history stages involves ‘prospective narration’ – classifying the present in terms of its anticipated future. Assigning sexes to adult stages of non-reproductive castes or non-reproductive individuals is a complex matter whose biological meaning differs from case to case. The chromosomal and phenotypic ‘definitions’ of biological sex that are contested in philosophical discussions of sex are actually operational definitions which track gametic sex more or less effectively in some species or group of species. Neither ‘definition’ can be stated for species in general except by defining them in terms of gametic sex. The gametic definition of sex also features in widely accepted models which explain why two biological sexes – either in separate individuals or combined in hermaphroditic individuals - are almost universal in multicellular species. Finally, the fact that a species has only two biological sexes does not imply that every member of the species is either male, female or hermaphroditic, or that the sex of every individual organism is clear and determinate. The idea of biological sex is critical for understanding the diversity of life, but ill-suited to the job of determining the social or legal status of human beings as men or women. (shrink)

Modern life sciences research is increasingly relying on artificial intelligence (AI) approaches to model biological systems, primarily centered around the use of machine learning (ML) models. Although ML is undeniably useful for identifying patterns in large, complex data sets, its widespread application in biological sciences represents a significant deviation from traditional methods of scientific inquiry. As such, the interplay between these models and scientific understanding in biology is a topic with important implications for the future of scientific research, (...) yet it is a subject that has received little attention. Here, we draw from an epistemological toolkit to contextualize recent applications of ML in biological sciences under modern philosophical theories of understanding, identifying general principles that can guide the design and application of ML systems to model biological phenomena and advance scientific knowledge. We propose that conceptions of scientific understanding as information compression, qualitative intelligibility, and dependency relation modelling provide a useful framework for interpreting ML-mediated understanding of biological systems. Through a detailed analysis of two key application areas of ML in modern biological research – protein structure prediction and single cell RNA-sequencing – we explore how these features have thus far enabled ML systems to advance scientific understanding of their target phenomena, how they may guide the development of future ML models, and the key obstacles that remain in preventing ML from achieving its potential as a tool for biological discovery. Consideration of the epistemological features of ML applications in biology will improve the prospects of these methods to solve important problems and advance scientific understanding of living systems. (shrink)

A strong motivation for the human genome project was to relate biological features to the structure and function of small sets of genes, and ideally to individual genes. However, it is now increasingly realized that many problems require a "systems" approach emphasizing the interplay of large numbers of genes, and the involvement of complex networks of gene regulation. This implies a new emphasis on integrative, systems theoretical approaches. It may be called 'holistic' if the term is used without irrational (...) overtones, in the general sense of directing attention to integrated features of organs and organisms. In the history of biology, seemingly conflicting reductionist and holistic notions have alternated, with bottom-up as well as top-down approaches eventually contributing to the solutions of basic problems. By now, there is no doubt that biological features and phenomena are rooted in physico-chemical processes of the molecules involved; and yet, integrated systems aspects are becoming more and more relevant in developmental biology, brain and behavioural science, and socio-biology. -/- . (shrink)

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

Over the last decades, network-based approaches have become highly popular in diverse fields of biology, including neuroscience, ecology, molecular biology and genetics. While these approaches continue to grow very rapidly, some of their conceptual and methodological aspects still require a programmatic foundation. This challenge particularly concerns the question of whether a generalized account of explanatory, organisational and descriptive levels of networks can be applied universally across biological sciences. To this end, this highly interdisciplinary theme issue focuses on the definition, (...) motivation and application of key concepts in biological network science, such as explanatory power of distinctively network explanations, network levels, and network hierarchies. (shrink)

Evolutionary adaptation has been suggested as the hallmark of life that best accounts for life’s creativity. However, current evolutionary approaches still fail to give an adequate account of it, even if they are able to explain both the origin of novelties and the proliferation of certain traits in a population. Although modern-synthesis Darwinism is today usually appraised as too narrow a position to cope with all the complexities of developmental and structural biology—not to say biosemiotic phenomena—, Darwinism need not be (...) if we separate metaphor from reality in natural selection in order to show the axiological complexity of this concept. This can shed light on the relationship between biosemiotics and biological evolution. (shrink)

Computer simulations constitute a significant scientific tool for promoting scientific understanding of natural phenomena and dynamic processes. Substantial leaps in computational force and software engineering methodologies now allow the design and development of large-scale biological models, which – when combined with advanced graphics tools – may produce realistic biological scenarios, that reveal new scientific explanations and knowledge about real life phenomena. A state-of-the-art simulation system termed Reactive Animation (RA) will serve as a study case to examine the contemporary (...) philosophical debate on the scientific value of simulations, as we demonstrate its ability to form a scientific explanation of natural phenomena and to generate new emergent behaviors, making possible a prediction or hypothesis about the equivalent real-life phenomena. (shrink)

We review some of the main implications of the free-energy principle (FEP) for the study of the self-organization of living systems – and how the FEP can help us to understand (and model) biotic self-organization across the many temporal and spatial scales over which life exists. In order to maintain its integrity as a bounded system, any biological system - from single cells to complex organisms and societies - has to limit the disorder or dispersion (i.e., the long-run entropy) (...) of its constituent states. We review how this can be achieved by living systems that minimize their variational free energy. Variational free energy is an information theoretic construct, originally introduced into theoretical neuroscience and biology to explain perception, action, and learning. It has since been extended to explain the evolution, development, form, and function of entire organisms, providing a principled model of biotic self-organization and autopoiesis. It has provided insights into biological systems across spatiotemporal scales, ranging from microscales (e.g., sub- and multicellular dynamics), to intermediate scales (e.g., groups of interacting animals and culture), through to macroscale phenomena (the evolution of entire species). A crucial corollary of the FEP is that an organism just is (i.e., embodies or entails) an implicit model of its environment. As such, organisms come to embody causal relationships of their ecological niche, which, in turn, is influenced by their resulting behaviors. Crucially, free-energy minimization can be shown to be equivalent to the maximization of Bayesian model evidence. This allows us to cast natural selection in terms of Bayesian model selection, providing a robust theoretical account of how organisms come to match or accommodate the spatiotemporal complexity of their surrounding niche. In line with the theme of this volume; namely, biological complexity and self-organization, this chapter will examine a variational approach to self-organization across multiple dynamical scales. (shrink)

Chapter 1st of the book. This chapter explores the fundamental ambiguity of the concept of plasticity – between openness and determination, change and stabilization of forms. This pluralism of meanings is used to unpack different instantiations of corporeal plasticity across various epochs, starting from ancient and early modern medicine, particularly humouralism. A genealogical approach displaces the notion that plasticity is a unitary phenomenon, coming in the abstract, and illuminates the unequal distribution of different forms of plasticities across social, gender, and (...) ethnic groups. Taking a longer view of the plastic body as a ubiquitous belief in traditions predating and coexisting with modern medicine will help contextualize the seeming radicalism of today’s turn to permeability and the exceptionalism of Western findings. By highlighting the complex biopolitical usages of plasticity in the past, the chapter warns against simplistic appropriations of the term in contemporary body/world configurations driven by findings in neuroscience, epigenetics and microbiomics. (shrink)

In the late 19th century Husserl studied our internal sense of time passing, maintaining that its deep connections into experience represent prima facie evidence for it as the basis for all investigations in the sciences: Phenomenology was born. Merleau-Ponty focused on perception pointing out that any theory of experience must in accord with established aspects of biology i.e. embodied. Recent analyses suggest that theories of experience require non-reductive, integrative information, together with a specific property connecting them to experience. Here we (...) elucidate a new class of information states with just such properties found at the loci of control of complex biological systems, including nervous systems. Complexity biology concerns states satisfying self-organized criticality. Such states are located at critical instabilities, commonly observed in biological systems, and thought to maximize information diversity and processing, and hence to optimize regulation. Major results for biology follow: why organisms have unusually low entropies; and why they are not merely mechanical. Criticality states form singular self-observing systems, which reduce wave packets by processes of perfect self-observation associated with feedback gain g=1. Analysis of their information properties leads to identification of a new kind of information state with high levels of internal coherence, and feedback loops integrated into their structure. The major idea presented here is that the integrated feedback loops are responsible for our ‘sense of self’, and also the feeling of continuity in our sense of time passing. Long-range internal correlations guarantee a unique kind of non-reductive, integrative information structure enabling such states to naturally support phenomenal experience. Being founded in complexity biology, they are ‘embodied’; they also fulfill the statement that ‘The self is a process’, a singular process. High internal correlations and RenéThom-style catastrophes support non-digital forms of information, gestalt cognition, and information transfer via quantum teleportation. Criticality in complexity biology can ‘embody’ cognitive states supporting gestalts, and phenomenology’s senses of ‘self,’ time passing, existence and being. (shrink)

It should now be recognized that codes are central to life and to understanding its more complex forms, including human culture. Recognizing the ‘conventional’ nature of codes provides solid grounds for rejecting efforts to reduce life to biochemistry and justifies according a place to semantics in life. The question I want to consider is whether this is enough. Focussing on Eigen’s paradox of how a complex code could originate, I will argue that along with Barbieri’s efforts to account for the (...) origins of life based on the ribosome and then to account for the refined codes through a process of ambiguity reduction, something more is required. Barbieri has not provided an adequate account of emergence, or the basis for providing such an account. I will argue that Stanley Salthe has clarified to some extent the nature of emergence by conceptualizing it as the interpolation of new enabling constraints. Clearly, codes can be seen as enabling constraints. How this actually happens, though, is still not explained. Stuart Kauffman has grappled with this issue and shown that it radically challenges the assumptions of mainstream science going back to Newton. He has attempted to reintroduce real possibilities or potentialities into his ontology, and argued that radically new developments in nature are associated with realizing adjacent possibles. This is still not adequate. What is also involved, I will suggest, utilizing concepts developed by the French natural philosopher Gilbert Simondon, is ‘transduction’ as part of ‘ontogenesis’ of individuals in a process of ‘individuation’, that is, the emergence of ‘individuals’ from preindividual fields or milieux. (shrink)

Molecular biology is a relatively new and very successful branch of science but currently it faces challenges posed by very complex issues that cannot be addressed by a traditional reductionist approach. However, despite its origins in the providential shift of some theoretical physicists to biology, currently molecular biology is immersed in a blind trend in which high-throughput technology, able to generate trillions of data, is becoming the leading edge of a discipline that has traded rational and critical thinking for computer (...) power, data crunching and statistics in hope of finding, once more, its way. However, the complex questions facing MB may only be solved by a paradigm shift in the causal way of thinking, so that complex, emergent, non-linear phenomena may be effectively understood in top-down and not in bottom-up terms. (shrink)

The file on this site provides the slides for a lecture given in Hangzhou in May 2018, and the lecture itself is available at the URL beginning 'sms' in the set of links provided in connection with this item. -/- It is commonly assumed that regular physics underpins biology. Here it is proposed, in a synthesis of ideas by various authors, that in reality structures and mechanisms of a biological character underpin the world studied by physicists, in principle supplying (...) detail in the domain that according to regular physics is of an indeterminate character. In regular physics mathematical equations are primary, but this constraint leads to problems with reconciling theory and reality. Biology on the other hand typically does not characterise nature in quantitative terms, instead investigating in detail important complex interrelationships between parts, leading to an understanding of the systems concerned that is in some respects beyond that which prevails in regular physics. It makes contact with quantum physics in various ways, for example in that both involve interactions between observer and observed, an insight that explains what is special about processes involving observation, justifying in the quantum physics context the replacement of the unphysical many-worlds picture by one involving collapse. The link with biology furthermore clarifies Wheeler’s suggestion that a multiplicity of observations can lead to the ‘fabrication of form’, including the insight that this process depends on very specific ‘structures with power’ related to the 'semiotic scaffolding' of the application of sign theory to biology known as biosemiotics. -/- The observer-observed 'circle' of Wheeler and Yardley is a special case of a more general phenomenon, oppositional dynamics, related to the 'intra-action' of Barad's Agential Realism, involving cooperating systems such as mind and matter, abstract and concrete, observer and observed, that preserve their identities while interacting with one another in such a way as to act as a unit. A third system may also be involved, the mediating system of Peirce linking the two together. Such a situation of changing connections and separations may plausibly lead in the future to an understanding of how complex systems are able to evolve to produce 'life, the universe and everything'. -/- (Added 1 July 2018) The general structure proposed here as an alternative to a mathematics-based physics can be usefully characterised by relating it to different disciplines and the specialised concepts utilised therein. In theoretical physics, the test for the correctness of a theory typically involves numerical predictions, corresponding to which theories are expressed in terms of equations, that is to say assertions that two quantities have identical values. Equations have a lesser significance in biology which typically talks in terms of functional mechanisms, dependent for example on details of chemistry and concepts such as genes, natural selection, signals and geometrical or topologically motivated concepts such as the interconnections between systems and the unfolding of DNA. Biosemiotics adds to this the concept of signs and their interpretation, implying novel concepts such as semiotic scaffolding and the semiosphere, code duality, and appreciation of the different types of signs, including symbols and their capacity for abstraction and use in language systems. Circular Theory adds to this picture, as do the ideas of Barad, considerations such as the idea of oppositional dynamics. The proposals in this lecture can be regarded as the idea that concepts such as those deriving from biosemiotics have more general applicability than just conventional biology and may apply, in some circumstances, to nonlinear systems generally, including the domain new to science hypothesised to underlie the phenomena of present-day physics. -/- The task then has to be to restore the mathematical aspect presumed, in this picture, not to be fundamental as it is in conventional theory. Deacon has invoked a complex sequence of evolutionary steps to account for the emergence over time of human language systems, and correspondingly mathematical behaviour can be subsumed under the general evolutionary mechanisms of biosemiotics (cf. also the proposals of Davis and Hersh regarding the nature of mathematics), so that the mathematical behaviour of physical systems is consistent with the proposed scheme. In conclusion, it is suggested that theoretical physicists should cease expecting to find some universal mathematical ‘theory of everything’, and focus instead on understanding in more detail complex systems exhibiting behaviour of a biological character, extending existing understanding. This may in time provide a more fruitful understanding of the natural world than does the regular approach. The essential concepts have an observational basis from both biology and the little-known discipline of cymatics (a discipline concerned with the remarkable patterns that specific waveforms can give rise to), while again computer simulations also offer promise in providing insight into the complex behaviours involved in the above proposals. -/- References -/- Jesper Hoffmeyer, Semiotic Scaffolding of Living Systems. Commens, a Digital Companion to C. S. Peirce (on Commens web site). Terrence Deacon, The Symbolic Species, W.W. Norton & Co. Karen Barad, Meeting the Universe Halfway: Quantum Physics and the Entanglement of Matter and Meaning, Duke University Press. Philip Davis and Reuben Hersh, The Mathematical Experience, Penguin. Ilexa Yardley, Circular Theory. (shrink)

Advances in biology, at least over the past two centuries, have mostly relied on theories that were subsequently revised, expanded or eventually refuted using experimental and other means. The field of theoretical biology used to primarily provide a basis, similar to theoretical physics in the physical sciences, to rationally examine the frameworks within which biological experiments were carried out and to shed light on overlooked gaps in understanding. Today, however, theoretical biology has generally become synonymous with computational and mathematical (...) biology. This could in part be explained by a relatively recent tendency in which a "data first", rather than a "theory first", approach is preferred. Moreover, generating hypotheses has at times become procedural rather than theoretical, therefore perhaps inadvertently leading some hypotheses to become perfunctory in nature. This situation leaves our understanding enmeshed in data, which should be disentangled from much noise. Given the many unresolved questions in biology and medicine, big and small, ranging from the problem of protein folding to unifying causative frameworks of complex non-Mendelian human diseases, it seems apt to revive the role of pure theory in the biological sciences. This paper, using the current biomedical literature and historical precedents, makes the case for a "philosophical biology" (philbiology), distinct from but quite complementary to philosophy of biology (philobiology), which would entail biological investigation through philosophical approaches. Philbiology would thus be a reincarnation of theoretical biology, adopting the true sense of the word "theory" and making use of a rich tradition of serious philosophical approaches in the natural sciences. A philbiological investigation, after clearly defining a given biological problem, would aim to propose a set of empirical questions, along with a class of possible solutions, about that problem. Importantly, whether or not the questions can be tested using current experimental paradigms would be secondary to whether the questions are inherently empirical or not. These issues will be illustrated using a range of specific examples. The final goal of a philbiological investigation would be to develop a theoretical framework that can lead observational and/or interventional experimental studies of the defined problem, a framework that is structured, generative and expandable, and, crucially, one that simplifies some aspect(s) of the said problem. (shrink)

Our intuition tells us that there is a general trend in the evolution of nature, a trend towards greater complexity. However, there are several definitions of complexity and hence it is difficult to argue for or against the validity of this intuition. Christoph Adami has recently introduced a novel measure called physical complexity that assigns low complexity to both ordered and random systems and high complexity to those in between. Physical complexity measures the amount (...) of information that an organism stores in its genome about the environment in which it evolves. The theory of physical complexity predicts that evolution increases the amount of ‘knowledge’ an organism accumulates about its niche. It might be fruitful to generalize Adami’s concept of complexity to the entire evolution (including the evolution of man). Physical complexity fits nicely into the philosophical framework of cognitive biology which considers biological evolution as a progressing process of accumulation of knowledge (as a gradual increase of epistemic complexity). According to this paradigm, evolution is a cognitive ‘ratchet’ that pushes the organisms unidirectionally towards higher complexity. Dynamic environment continually creates problems to be solved. To survive in the environment means to solve the problem, and the solution is an embodied knowledge. Cognitive biology (as well as the theory of physical complexity) uses the concepts of information and entropy and views the evolution from both the information-theoretical and thermodynamical perspective. Concerning humans as conscious beings, it seems necessary to postulate an emergence of a new kind of knowledge - a self-aware and self-referential knowledge. Appearence of selfreflection in evolution indicates that the human brain reached a new qualitative level in the epistemic complexity. (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)

As a sort of intellectual provocation and as a lateral thinking strategy for creativity, this chapter seeks to determine what the study of the dynamics of biodiversity can offer linguists. In recent years, the analogical equation "language = biological species" has become more widespread as a metaphorical source for conceptual renovation, and, at the same time, as a justification for the defense of language diversity. Language diversity would be protected in a way similar to the mobilization that has taken (...) place to protect endangered species. Nevertheless, one must be careful when uncritically transferring conceptualizations and theoretical frameworks from one field to another, since obviously, these two phenomena are quite different in the real world. The dialogue with bioecologists starts by asking about the formation of diversity, i.e., about specialization. Here, one can observe the similarity between the processes of linguistic and genetic fragmentation, in the sense that both phenomena have a (socio)geographical basis for dispersion and consequently, for the loss of their original compact nature and their intercommunication. The self-organizing and creative properties of human beings favor the development of specific varieties for each subset, varieties that continue to evolve constantly, through the unceasing "languaging" of humanity. Regarding the continuity of species or languages, one can also observe the decisive role of intragroup relations. The staying power of linguistic varieties will increase in direct proportion to the intensity of the relationship among the components of the subset. On the other hand, if exotic elements are introduced, especially if these elements are aggressive in nature, the alteration of the ecological niche may turn out to be fatal for the continuity of the previously existing forms. This suggests to us the need to make an in-depth study of what the minimal contextual conditions would be so that a set linguistic group could be assured a sustainable continuity within a framework of linguistic contact. What type of minimum (socio-)ecological niche would a language have to have if we wished to ensure its habitual reproduction? A proposal is made here to explore the ideas of "exclusive functions" and "non-hierarchical functional distribution" for codes in situations where there is high contact and a danger of disuse. As regards change, this phenomenon is seen as an inherent element in the tendency of life to create new developments, which may or not be accompanied by an adaptation to changing environmental conditions. It is pointed out that, similar to what happens in biology, much of linguistic innovation stems from a systemically reorganized mixture of solutions from different codes. An important research question would be, however, to determine why some of these innovations disappear and others survive and extend throughout the community. The big question mark, as Mufwene points out, is, then how to manage to understand how "the evolution of language proceeds by naturally selecting from among the competing alternatives available through the idiolects of individual speakers". Extinction, whether it be of languages or of species, is caused in most cases "by a combination of demographic processes and environmental changes", as Brown points out. Thus, the environment plays a fundamental role in the direction of evolution, since the "the survival of the fittest is, in actuality, the survival of those who fit into the context (Allen and Hoekstra)". This allows us to see the great degree of importance of political-economic contexts in the case of languages. In the same way, migratory movements are also one of the major variables determining the extinction of biodiversity and language diversity. Species and habitat form the basic unit of existence, and this is the major point of departure for understanding the problem of the preservation and recovery of species or languages. Given the increase in the degree of linguistic contact, the continuity of language diversity depends on determining, as exactly as possible, as Prigogine, the physicist, would say, what precise conditions of imbalance may prove to be stable. The great challenge is not so much avoiding contact but managing it. And "restorative ecology" can also be of help to us here. Being able to reach sustainable solutions for language diversity implies a profound knowledge of the dynamics for determining the ways in which language is used in contact situations. The general conclusion is that linguistics is still terminologically and conceptually ill prepared to deal with the dynamic character of human languages. The world and our objects must be conceived of as elements in a state of flux, as changing systems in an unstable equilibrium. With respect to language policy, it would be necessary to make an effort to manage to establish some general principles regarding the linguistic organization of the human species that would make it possible for local linguistic diversity and communication on a planetary scale, which must necessarily take place, to be compatible with each other. To succeed, it will be necessary to continue promoting an autonomous socio-ecological perspective devoted to the comprehension of language phenomena. Such a perspective would be based on a paradigm of complexity, and would, at the same time, place human beings at the center of its theoretical underpinnings. (shrink)

This chapter draws on insights from non-equilibrium thermodynamics to demonstrate the ontological inadequacy of the machine conception of the organism. The thermodynamic character of living systems underlies the importance of metabolism and calls for the adoption of a processual view, exemplified by the Heraclitean metaphor of the stream of life. This alternative conception is explored in its various historical formulations and the extent to which it captures the nature of living systems is examined. Following this, the chapter considers the metaphysical (...) implications of reconceptualizing the organism from complex machine to flowing stream. What do we learn when we reject the mechanical and embrace the processual? Three key lessons for biological ontology are identified. The first is that activity is a necessary condition for existence. The second is that persistence is grounded in the continuous self-maintenance of form. And the third is that order does not entail design. (shrink)

In spite of being ubiquitous in life sciences, the concept of information is harshly criticized. Uses of the concept other than those derived from Shannon's theory are denounced as pernicious metaphors. We perform a computational experiment to explore whether Shannon's information is adequate to describe the uses of said concept in commonplace scientific practice. Our results show that semantic sequences do not have unique complexity values different from the value of meaningless sequences. This result suggests that quantitative theoretical frameworks (...) do not account fully for the complex phenomenon that the term “information” refers to. We propose a restructuring of the concept into two related, but independent notions, and conclude that a complete theory of biological information must account completely not only for both notions, but also for the relationship between them. (shrink)

Research in ecology and evolutionary biology (evo-eco) often tries to emulate the “hard” sciences such as physics and chemistry, but to many of its practitioners feels more like the “soft” sciences of psychology and sociology. I argue that this schizophrenic attitude is the result of lack of appreciation of the full consequences of the peculiarity of the evo-eco sciences as lying in between a-historical disciplines such as physics and completely historical ones as like paleontology. Furthermore, evo-eco researchers have gotten stuck (...) on mathematically appealing but philosophi- cally simplistic concepts such as null hypotheses and p-values defined according to the frequentist approach in statistics, with the consequence of having been unable to fully embrace the complexity and subtlety of the problems with which ecologists and evolutionary biologists deal with. I review and discuss some literature in ecology, philosophy of science and psychology to show that a more critical methodological attitude can be liberating for the evo-eco scientist and can lead to a more fecund and enjoyable practice of ecology and evolutionary biology. With this aim, I briefly cover concepts such as the method of multiple hypotheses, Bayesian analysis, and strong inference. (shrink)

In the first part of this article we survey general similarities and differences between biological and social macroevolution. In the second (and main) part, we consider a concrete mathematical model capable of describing important features of both biological and social macroevolution. In mathematical models of historical macrodynamics, a hyperbolic pattern of world population growth arises from non-linear, second-order positive feedback between demographic growth and technological development. Based on diverse paleontological data and an analogy with macrosociological models, we suggest (...) that the hyperbolic character of biodiversity growth can be similarly accounted for by non-linear, second-order positive feedback between diversity growth and the complexity of community structure. We discuss how such positive feedback mechanisms can be modelled mathematically. (shrink)

In the first part of this article we survey general similarities and differences between biological and social macroevolution. In the second (and main) part, we consider a concrete mathematical model capable of describing important features of both biological and social macroevolution. In mathematical models of historical macrodynamics, a hyperbolic pattern of world population growth arises from non-linear, second-order positive feedback between demographic growth and technological development. Based on diverse paleontological data and an analogy with macrosociological models, we suggest (...) that the hyperbolic character of biodiversity growth can be similarly accounted for by non-linear, second-order positive feedback between diversity growth and the complexity of community structure. We discuss how such positive feedback mechanisms can be modelled mathematically. (shrink)

The scientific study of living organisms is permeated by machine and design metaphors. Genes are thought of as the ‘‘blueprint’’ of an organism, organisms are ‘‘reverse engineered’’ to discover their func- tionality, and living cells are compared to biochemical factories, complete with assembly lines, transport systems, messenger circuits, etc. Although the notion of design is indispensable to think about adapta- tions, and engineering analogies have considerable heuristic value (e.g., optimality assumptions), we argue they are limited in several important respects. In (...) particular, the analogy with human-made machines falters when we move down to the level of molecular biology and genetics. Living organisms are far more messy and less transparent than human-made machines. Notoriously, evolution is an oppor- tunistic tinkerer, blindly stumbling on ‘‘designs’’ that no sensible engineer would come up with. Despite impressive technological innovation, the prospect of artificially designing new life forms from scratch has proven more difficult than the superficial analogy with ‘‘programming’’ the right ‘‘software’’ would sug- gest. The idea of applying straightforward engineering approaches to living systems and their genomes— isolating functional components, designing new parts from scratch, recombining and assembling them into novel life forms—pushes the analogy with human artifacts beyond its limits. In the absence of a one-to-one correspondence between genotype and phenotype, there is no straightforward way to imple- ment novel biological functions and design new life forms. Both the developmental complexity of gene expression and the multifarious interactions of genes and environments are serious obstacles for ‘‘engi- neering’’ a particular phenotype. The problem of reverse-engineering a desired phenotype to its genetic ‘‘instructions’’ is probably intractable for any but the most simple phenotypes. Recent developments in the field of bio-engineering and synthetic biology reflect these limitations. Instead of genetically engi- neering a desired trait from scratch, as the machine/engineering metaphor promises, researchers are making greater strides by co-opting natural selection to ‘‘search’’ for a suitable genotype, or by borrowing and recombining genetic material from extant life forms. (shrink)

This essay explains Donald Hoffman's Interface Theory of Perception using The Biological Framework for a Mathematical Universe proposed by Ronald Williams. According to Hoffman, what we perceive is more like a “desktop interface with icons representing complex underlying processes, rather than a direct window into the true nature of the world." The theory of a biological framework for a mathematical universes suggests that these complex underlying processes of “the desktop interface with icons” contain correspondences to biological systems. (...) For example, “the desktop icons” of the ocean’s arctic currents and the melting and freezing of Antartica contain “complex underlying processes” which mirror the processes of a circulatory system and beating heart. While indeed we do interpret reality from a superficial perspective as Hoffman suggests, our cognitive abilities are enhanced enough to recognize correspondences to these patterns pertaining to these biological systems which reveal the true nature of the world/universe/reality and thus highlight our evolved perceptual systems which can enable us how to recognize these patterns and organize ourselves to them in order to best survive it. Another example of a “desktop icon” is human society—the order and properties of human society contain “complex underlying processes” that are analogous to the order and properties of cellular society—thus “socioeconomic problems” can be understood through cellular economic problems. Moreover, the solution to the cellular economic problem is the solution to the socioeconomic problem. (shrink)

The problem with reductionism in biology is not the reduction, but the implicit attitude of determinism that usually accompanies it. Methodological reductionism is supported by deterministic beliefs, but making such a connection is problematic when it is based on an idea of determinism as fixed predictability. Conflating determinism with predictability gives rise to inaccurate models that overlook the dynamic complexity of our world, as well as ignore our epistemic limitations when we try to model it. Furthermore, the assumption of (...) a strictly deterministic framework is unnecessarily hindering to biology. By removing the dogma of determinism, biological methods, including reductive methods, can be expanded to include stochastic models and probabilistic interpretations. Thus, the dogma of reductionism can be saved once its ties with determinism are severed. In this paper, I analyze two problems that have faced molecular biology for the last 50 years—protein folding and cancer. Both cases demonstrate the long influence of reductionism and determinism on molecular biology, as well as how abandoning determinism has opened the door to more probabilistic and unconstrained reductive methods in biology. (shrink)

As figurational sociologists and sociolinguists, we need to know that we currently find support from other fields in our efforts to construct a sociocultural science focused on interdependencies and processes, creating a multidimensional picture of human beings, one in which the brain and its mental and emotional processes are properly recognized. The paradigmatic revolutions in 20th-century physics, the contributions made by biology to our understanding of living beings, the conceptual constructions built around the theories of systems, self-organization and complexity, (...) all these implore that we reflect on social sciences paradigms in the light of the great changes in these other disciplines. The application of metaphors or theoretical images of complexity and figurational sociology in understanding language and socio-communication phenomena is of great use, since language is not an ‘object’, but a ‘complex’; it exists simultaneously in and among different domains. ‘Languaging’ and interaction are co-phenomena. The former exists within the latter, and the latter within the former. By visualizing, for instance, the different levels of linguistic structure not as separate entities but rather as united and integrated within the same theoretical frame, by seeing their functional interdependencies, by situating them in a greater multidimensionality that includes what for a long time was considered ‘external’ – the individual and his or her mind-brain, the sociocultural system, the physical world, etc. – and expanding in this way our classical view, we should be able to make important, if not essential, theoretical and practical advances. (shrink)

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

Are living organisms--as Descartes argued--just machines? Or is the nature of life such that it can never be fully explained by mechanistic models? In this thought-provoking and controversial book, eminent geophysicist Walter M. Elsasser argues that the behavior of living organisms cannot be reduced to physico-chemical causality. Suggesting that molecular biology today is at the same point as Newtonian physics on the eve of the quantum revolution, Elsasser lays the foundation for a theoretical biology that points the way toward a (...) natural philosophy of organic life. Explicitly repudiating "vitalism" (the notion that the laws of nature need to be modified when applied to living organisms), Elsasser argues instead that the structural complexity of even a single living cell is "transcomputational"--that is, beyond the power of any imaginable system to compute. Beginning from this insight, Elsasser leads the reader through a step-by-step process that ultimately arrives at the conclusion that living and non-living matter are separated by "a no-man's land of irrationality." Trained in Germany as a physicist, Elsasser first pondered the implications of quantum mechanics for biology as early as 1951. The more closely he studied the inherent complexity of life, the more skeptical he became of the reductionist view of organisms as tiny machines. "An organism," he concluded, "is a source of causal chains which cannot be traced beyond a terminal point because they are lost in the unfathomable complexity of the organism." Like the physicist who works within the bounds of an unfathomable universe, Elsasser argues, the biologist must seek answers within a system that is no less unfathomable. (shrink)

This paper critically revises the organisational account of teleology, which argues that living systems are first and foremost oriented towards a goal: maintaining their own conditions of existence. It points out some limitations of this account, mainly in the capability to account for the richness and complexity of biological systems and their purposeful behaviours. It identifies the reason of these limitations in the theoretical grounding of this account, specifically in the too narrow notion of closure of constraints, focused (...) on self-production. It proposes to ground an organisational account of biological teleology in the capability of living system not just to produce and replace their parts, but to control their own internal dynamics and behaviours in such a way as to maintain themselves. This theoretical framework has two advantages. It better captures the distinctive features of biological organisations and consequently the richness and active nature of their purposeful behaviours. By doing so, it makes it possible to apply this framework beyond minimal theoretical models to real biological cases. (shrink)

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