This study provides a basic introduction to agent-based modeling (ABM) as a powerful blend of classical and constructive mathematics, with a primary focus on its applicability for social science research. The typical goals of ABM social science researchers are discussed along with the culture-dish nature of their computer experiments. The applicability of ABM for science more generally is also considered, with special attention to physics. Finally, two distinct types of ABM applications are summarized in order to illustrate concretely the duality (...) of ABM: Real-world systems can not only be simulated with verisimilitude using ABM; they can also be efficiently and robustly designed and constructed on the basis of ABM principles. (shrink)

A promising recent approach for understanding complex phenomena is recognition of anticipatory behavior of living organisms and social organizations. The anticipatory, predictive action permits learning, novelty seeking, rich experiential existence. I argue that the established frameworks of anticipation, adaptation or learning imply overly passive roles of anticipatory agents, and that a fictionalist standpoint reflects the core of anticipatory behavior better than representational or future references. Cognizing beings enact not just their models of the world, but own make-believe existential agendas as (...) well. Anticipators embody plausible scripts of living, and effectively assume neo-Kantian or pragmatist perspectives of cognition and action. It is instructive to see that anticipatory behavior is not without mundane or loathsome deficiencies. Appreciation of ferally fictionalist anticipation suggests an equivalence of semiosis and anticipation. (shrink)

Complexity science has proliferated across academic domains in recent years. A question arises as to whether any useful sense of ‘generalized complexity ’ can be abstracted from the various versions of complexity to be found in the literature, and whether it could prove fruitful in a scientific sense. Most attempts at defining complexity center around two kinds of notions: Structural, and temporal or dynamic. Neither of these is able to provide a foundation for the intuitive or generalized notion when taken (...) separately; structure is often a derivative notion, dependent on prior notions of complexity, and dynamic notions such as entropy are often indefinable. The philosophical notion of process may throw light on the tensions and contradictions within complexity. Robustness, for instance, a key quality of complexity, is quite naturally understood within a process-theoretical framework. Understanding complexity as process also helps one align complexity science with holistically oriented predecessors such as General System Theory, while allowing for the reductionist perspective of complexity. These results, however, have the further implication that it may be futile to search for general laws of complexity, or to hope that investigations of complex objects in one domain may throw light on complexity in unrelated domains. (shrink)

Aesthetic chills occur in artistic, scientific and religious context. We introduce a theoretical framework relating them to humans’ vital need for cognition. We discuss the implications of such a framework and the plausibility of our hypothesis. Numerous references to chills are introduced.

The book addresses the broad issue of sustainability of our civilization and seeks to contribute to the ongoing discussion of what many see as its systemic crisis. There is a broad agreement that new creative ideas, initiatives, and solutions are essential for dealing with the current problems. However, despite this recognition, we still know very little about the process of creation and how it works. As a result, our civilization fails to harness the enormous creative potential of humanity. This failure, (...) the book argues, is the main source of our current problems—languishing economy, deteriorating environment, continued violence, the deficit of democracy, and the lack of new fundamental breakthroughs in science. It examines some of these problems and demonstrates the connection between them and our failure to embrace the process of creation. The book offers a perspective that sheds light on the process of creation. It pays special attention to the theoretical contributions of Jean Piaget and the ongoing discussions of knowledge production that help us understand better how the process of creation works. The central argument of the book is that in order to solve our current problems and ensure the sustainability of our civilization well into the future we must embrace the process of creation and make it the central organizing principle of our social practice. Finally, the book provides an outline of the principal changes that the adoption of the new social practice organized around the process of creation will involve. (shrink)

The traditional practice of establishing morphological types and investigating morphological organization has found new support from evolutionary developmental biology (evo-devo), especially with respect to the notion of body plans. Despite recurring claims that typology is at odds with evolutionary thinking, evo-devo offers mechanistic explanations of the evolutionary origin, transformation, and evolvability of morphological organization. In parallel, philosophers have developed non-essentialist conceptions of natural kinds that permit kinds to exhibit variation and undergo change. This not only facilitates a construal of species (...) and higher taxa as natural kinds, but also broadens our perspective on the diversity of kinds found in biology. There are many different natural kinds relevant to the investigative and explanatory aims of evo-devo, including homologues and developmental modules. (shrink)

Systems biologists often distance themselves from reductionist approaches and formulate their aim as understanding living systems “as a whole.” Yet, it is often unclear what kind of reductionism they have in mind, and in what sense their methodologies would offer a superior approach. To address these questions, we distinguish between two types of reductionism which we call “modular reductionism” and “bottom-up reductionism.” Much knowledge in molecular biology has been gained by decomposing living systems into functional modules or through detailed studies (...) of molecular processes. We ask whether systems biology provides novel ways to recompose these findings in the context of the system as a whole via computational simulations. As an example of computational integration of modules, we analyze the first whole-cell model of the bacterium M. genitalium. Secondly, we examine the attempt to recompose processes across different spatial scales via multi-scale cardiac models. Although these models rely on a number of idealizations and simplifying assumptions as well, we argue that they provide insight into the limitations of reductionist approaches. Whole-cell models can be used to discover properties arising at the interfaces of dynamically coupled processes within a biological system, thereby making more apparent what is lost through decomposition. Similarly, multi-scale modeling highlights the relevance of macroscale parameters and models and challenges the view that living systems can be understood “bottom-up.” Specifically, we point out that system-level properties constrain lower-scale processes. Thus, large-scale modeling reveals how living systems at the same time are more and less than the sum of the parts. (shrink)

Real-world economies are open-ended dynamic systems consisting of heterogeneous interacting participants. Human participants are decision-makers who strategically take into account the past actions and potential future actions of other participants. All participants are forced to be locally constructive, meaning their actions at any given time must be based on their local states; and participant actions at any given time affect future local states. Taken together, these essential properties imply real-world economies are locally-constructive sequential games. This paper discusses a modeling approach, (...) Agent-based Computational Economics, that permits researchers to study economic systems from this point of view. ACE modeling principles and objectives are first concisely presented and explained. The remainder of the paper then highlights challenging issues and edgier explorations that ACE researchers are currently pursuing. (shrink)

Abstract: Purpose – The purpose of this paper is to analyse the broad phases of web development: the read-only Web 1.0, the read-write Web 2.0, and the collaborative and Internet of Things Web 3.0, are examined for the theoretical lenses through which they have been understood and critiqued. Design/methodology/approach – This is a conceptual piece, in the tradition of drawing on theorising from outside the Information Systems field, to shed light on developments in information communication technologies (ICTs). Findings – Along (...) with a summary of approaches to Webs 1.0 and 2.0, the authors contend that a more complex and poststructuralist theoretical approach to the notion of, and the phenomenon of Web 3.0, offers a more interesting and appropriate theoretical grounding for understanding its particularities. Originality/value – The discussion presages five further papers engaged with ICTs in a changing society, each of which similarly addresses novel theoretical understandings. (shrink)

Mechanisms are said to consist of two kinds of components, entities and activities. In the first half of this chapter, I examine what entities and activities are, how they relate to well-known ontological categories, such as processes or dispositions, and how entities and activities relate to each other (e.g., can one be reduced to the other or are they mutually dependent?). The second part of this chapter analyzes different criteria for individuating the components of mechanisms and discusses how real the (...) boundaries of mechanisms are. (shrink)

Synthetic biology research is often described in terms of programming cells through the introduction of synthetic genes. Genetic material is seemingly attributed with a high level of causal responsibility. We discuss genetic causation in synthetic biology and distinguish three gene concepts differing in their assumptions of genetic control. We argue that synthetic biology generally employs a difference-making approach to establishing genetic causes, and that this approach does not commit to a specific notion of genetic program or genetic control. Still, we (...) suggest that a strong program concept of genetic material can be used as a successful heuristic in certain areas of synthetic biology. Its application requires control of causal context, and may stand in need of a modular decomposition of the target system. We relate different modularity concepts to the discussion of genetic causation and point to possible advantages of and important limitations to seeking modularity in synthetic biology systems. (shrink)

Wants, preferences, and cares are physical things or events, not ideas or propositions, and therefore no chain of pure logic can conclude with a want, preference, or care. It follows that no pure-logic machine will ever want, prefer, or care. And its behavior will never be driven in the way that deliberate human behavior is driven, in other words, it will not be motivated or goal directed. Therefore, if we want to simulate human-style interactions with the world, we will need (...) to first understand the physical structure of goal-directed systems. I argue that all such systems share a common nested structure, consisting of a smaller entity that moves within and is driven by a larger field that contains it. In such systems, the smaller contained entity is directed by the field, but also moves to some degree independently of it, allowing the entity to deviate and return, to show the plasticity and persistence that is characteristic of goal direction. If all this is right, then human want-driven behavior probably involves a behavior-generating mechanism that is contained within a neural field of some kind. In principle, for goal directedness generally, the containment can be virtual, raising the possibility that want-driven behavior could be simulated in standard computational systems. But there are also reasons to believe that goal-direction works better when containment is also physical, suggesting that a new kind of hardware may be necessary. (shrink)

The framework of plateaus, dips, and leaps shines light on periods when individuals may be inventing new methods of skilled performance. We begin with a review of the role performance plateaus have played in experimental psychology, human–computer interaction, and cognitive science. We then reanalyze two classic studies of individual performance to show plateaus and dips which resulted in performance leaps. For a third study, we show how the statistical methods of Changepoint Analysis plus a few simple heuristics may direct our (...) focus to periods of performance change for individuals. For the researcher, dips become the marker of exploration where performance suffers as new methods are invented and tested. Leaps mark the implementation of a successful new method and an incremental jump above the path plotted by smooth and steady log–log performance increments. The methods developed during these dips and leaps are the key to surpassing one's teachers and acquiring extreme expertise. (shrink)

The concept of levels of organization, despite its widespread scientific currency, has recently been criticized by a number of philosophers of science. This paper diagnoses the main source of problems facing theories of levels. On this basis, the problems with the usual criteria for distinguishing levels are evaluated: compositional relations, organizational types, and spatial scales. Drawing on some work on hierarchies in ecology, I argue in favor of an alternative conception of levels defined by the criterion of rates or time (...) scales of processes. (shrink)

The paper seeks to make a contribution towards a better understanding of the current global political unrest. It argues that this unrest reflects ongoing tensions between hierarchical and non-hierarchical interactions. It also argues that the opposition between hierarchical and non-hierarchical interactions is not ontological but rather is rooted in the way we approach reality and is, therefore, subject to our control. The tendency to exclude the process of construction from our frame of vision is characteristic for the view of reality (...) that is dominant in our civilization. Contemporary theoretical perspectives that include, but are not limited to, systems theory, complexity theory, theory of self-organization, emergence theory and autopoiesis have much to offer in addressing and resolving this problem. The paper outlines some general organizing principles that should be part of this solution. (shrink)

Functional localization has historically been one of the primary goals of neuroscience. There is still debate, however, about whether it is possible, and if so what kind of theories succeed at localization. I argue for a contextualist approach to localization. Most theorists assume that widespread contextual variability in function is fundamentally incompatible with functional decomposition in the brain, because contextualist accounts will fail to be generalizable and projectable. I argue that this assumption is misplaced. A properly articulated contextualism can ground (...) successful theories of localization even without positing completely generalizable accounts. Via a case study from perceptual neuroscience, I suggest that there is strong evidence for contextual variation in the function of perceptual brain areas. I then outline a version of contextualism that is empirically adequate with respect to this data, and claim that it can still distinguish brain areas from each other according to their functional properties. Finally, I claim that the view does not fail the norms for good theory in the way that anticontextualists suppose. It is true that, on a contextualist view, we will not have theories that are completely generalizable and predictive. We can, however, have successful partial generalizations that structure ongoing investigation and lead to novel functional insight, and this success is sufficient to ground the project of functional localization. (shrink)

Climatology is a paradigmatic complex systems science. Understanding the global climate involves tackling problems in physics, chemistry, economics, and many other disciplines. I argue that complex systems like the global climate are characterized by certain dynamical features that explain how those systems change over time. A complex system's dynamics are shaped by the interaction of many different components operating at many different temporal and spatial scales. Examining the multidisciplinary and holistic methods of climatology can help us better understand the nature (...) of complex systems in general. -/- Questions surrounding climate science can be divided into three rough categories: foundational, methodological, and evaluative questions. "How do we know that we can trust science?" is a paradigmatic foundational question (and a surprisingly difficult one to answer). Because the global climate is so complex, questions like "what makes a system complex?" also fall into this category. There are a number of existing definitions of `complexity,' and while all of them capture some aspects of what makes intuitively complex systems distinctive, none is entirely satisfactory. Most existing accounts of complexity have been developed to work with information-theoretic objects (signals, for instance) rather than the physical and social systems studied by scientists. -/- Dynamical complexity, a concept articulated in detail in the first third of the dissertation, is designed to bridge the gap between the mathematics of contemporary complexity theory (in particular the formalism of "effective complexity" developed by Gell-Mann and Lloyd [2003]) and a more general account of the structure of science generally. Dynamical complexity provides a physical interpretation of the formal tools of mathematical complexity theory, and thus can be used as a framework for thinking about general problems in the philosophy of science, including theories, explanation, and lawhood. -/- Methodological questions include questions about how climate science constructs its models, on what basis we trust those models, and how we might improve those models. In order to answer questions about climate modeling, it's important to understand what climate models look like and how they are constructed. Climate model families are significantly more diverse than are the model families of most other sciences (even sciences that study other complex systems). Existing climate models range from basic models that can be solved on paper to staggeringly complicated models that can only be analyzed using the most advanced supercomputers in the world. I introduce some of the central concepts in climatology by demonstrating how one of the most basic climate models might be constructed. I begin with the assumption that the Earth is a simple featureless blackbody which receives energy from the sun and releases it into space, and show how to model that assumption formally. I then gradually add other factors (e.g. albedo and the greenhouse effect) to the model, and show how each addition brings the model's prediction closer to agreement with observation. After constructing this basic model, I describe the so-called "complexity hierarchy" of the rest of climate models, and argue that the sense of "complexity" used in the climate modeling community is related to dynamical complexity. -/- With a clear understanding of the basics of climate modeling in hand, I then argue that foundational issues discussed early in the dissertation suggest that computation plays an irrevocably central role in climate modeling. "Science by simulation" is essential given the complexity of the global climate, but features of the climate system--the presence of non-linearities, feedback loops, and chaotic dynamics--put principled limits on the effectiveness of computational models. This tension is at the root of the staggering pluralism of the climate model hierarchy, and suggests that such pluralism is here to stay, rather than an artifact of our ignorance. Rather than attempting to converge on a single "best fit" climate model, we ought to embrace the diversity of climate models, and view each as a specialized tool designed to predict and explain a rather narrow range of phenomena. Understanding the climate system as a whole requires examining a number of different models, and correlating their outputs. This is the most significant methodological challenge of climatology. -/- Climatology's role contemporary political discourse raises an unusually high number of evaluative questions for a physical science. The two leading approaches to crafting policy surrounding climate change center on mitigation (i.e. stopping the changes from occurring) and adaptation (making post hoc changes to ameliorate the harm caused by those changes). Crafting an effective socio-political response to the threat of anthropogenic climate change, however, requires us to integrate multiple perspectives and values: the proper response will be just as diverse and pluralistic as the climate models themselves, and will incorporate aspects of both approaches. I conclude by offering some concrete recommendations about how to integrate this value pluralism into our socio-political decision making framework. (shrink)

A scientific explanatory project, part-whole explanation, and a kind of science, part-whole science are premised on identifying, investigating, and using parts and wholes. In the biological sciences, mechanistic, structuralist, and historical explanations are part-whole explanations. Each expresses different norms, explananda, and aims. Each is associated with a distinct partitioning frame for abstracting kinds of parts. These three explanatory projects can be complemented in order to provide an integrative vision of the whole system, as is shown for a detailed case study: (...) the tetrapod limb. My diagnosis of part-whole explanation in the biological sciences as well as in other domains exploring evolved, complex, and integrated systems (e.g., psychology and cognitive science) cross-cuts standard philosophical categories of explanation: causal explanation and explanation as unification. Part-whole explanation is itself one essential aspect of part-whole science. (shrink)

Methodological reductionists practice ‘wannabe reductionism’. They claim that one should pursue reductionism, but never propose how. I integrate two strains in prior work to do so. Three kinds of activities are pursued as “reductionist”. “Successional reduction” and inter-level mechanistic explanation are legitimate and powerful strategies. Eliminativism is generally ill-conceived. Specific problem-solving heuristics for constructing inter-level mechanistic explanations show why and when they can provide powerful and fruitful tools and insights, but sometimes lead to erroneous results. I show how traditional metaphysical (...) approaches fail to engage how science is done. The methods used do so, and support a pragmatic and non-eliminativist realism. (shrink)

The book from which these sections are excerpted is concerned with the prospects for assimilating the study of human intelligence and its products to the natural sciences through the investigation of cognitive structures, understood as systems of rules and representations that can be regarded as “mental organs.” These mental structui′es serve as the vehicles for the exercise of various capacities. They develop in the mind on the basis of an innate endowment that permits the growth of rich and highly articulated (...) structures along an intrinsically determined course under the triggering and partially shaping effect of experience, which fixes parameters in an intricate system of predetermined form. It is argued that the mind is modular in character, with a diversity of cognitive structures, each with its specific properties arid principles. Knowledge of language, of the behavior of objects, and much else crucially involves these mental structures, and is thus not characterizable in terms of capacities, dispositions, or practical abilities, nor is it necessarily grounded in experience in the standard sense of this term.Various types of knowledge and modes of knowledge acquisition are discussed in these terms. Some of the properties of the language faculty are investigated. The basic cognitive relation is “knowing a grammar”; knowledge of language is derivative and, correspondingly, raises further problems. Language as commonly understood is not a unitary phenomenon but involves a number of interacting systems: the “computational” system of grammar, which provides the representations of sound and meaning that permit a rich range of expressive potential, is distinct from a conceptual system with its own properties; knowledge of language must be distinguished from knowledge of how to use a language; and the various systems that enter into the knowledge and use of language must be further analyzed into their specific subcomponents. (shrink)

Plant and animal species are information-processing entities of such complexity, integration, and adaptive competence that it may be scientifically fruitful to consider them intelligent. The possibility arises from the analogy between learning and evolution, and from recent developments in evolutionary science, psychology and cognitive science. Species are now described as spatiotemporally localized individuals in an expanded hierarchy of biological entities. Intentional and cognitive abilities are now ascribed to animal, human, and artificial intelligence systems that process information adaptively, and that manifest (...) problem-solving abilities. The structural and functional similarities between such systems and species are extensive, although they “are usually obscured by population-genetic metaphors that have nonetheless contributed much to our understanding of evolution.In this target article, I use Sewall Wright's notion of the “adaptive landscape” to compare the performances of evolving species to those of intelligent organisms. With regard to their adaptive achievements and the kinds of processes by which they are achieved, biological species compare very favorably to intelligent animals by virtue of interactions between populations and their environments, between ontogeny and phylogeny, and between natural, interdemic, organic, and species selection. Addressing the question of whether species are intelligent could help to refine our ideas about species, evolution, and intelligence, and could open new lines of empirical and theoretical inquiry in many disciplines. (shrink)

Convincing disputes about explanatory reductionism in the philosophy of biology require a clear and precise understanding of what a reductive explanation in biology is. The central aim of this book is to provide such an account by revealing the features that determine the reductive character of a biological explanation. Chapters I-IV provide the ground, on which I can then, in Chapter V, develop my own account of explanatory reduction in biology: Chapter I reveals the meta-philosophical assumptions that underlie my analysis (...) of reduction, Chapter II introduces the previous debate about reduction in the philosophy of biology by pointing out the most crucial lessons one should learn from this debate, Chapter III critically discusses the two perspectives on explanatory reduction that have been proposed in the philosophy of biology so far, and Chapter IV clarifies in what ways the issue of reduction is entangled with the issue of explanation. In Chapter V I present my own account of explanatory reduction. My main claim is that reductive explanations in the biological sciences possess three main characteristics: they explain the behavior of a biological system S, first, exclusively in terms of factors that are located on a lower level of organization than S, second, by focusing on factors that are internal to S , and third, by describing the genuine parts of S only as “parts in isolation”. This account is ontic because it traces the reductivity of an explanation back to certain relations that exist in the world. (shrink)

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

It is possible to predict future life forms? In this paper it is argued that the answer to this question may well be positive. As a basis for predictions a rationale is used that is derived from historical data, e.g. from a hierarchical classification that ranks all building block systems, that have evolved so far. This classification is based on specific emergent properties that allow stepwise transitions, from low level building blocks to higher level ones. This paper shows how this (...) hierarchy can be used for predicting future life forms.The extrapolations suggest several future neural network organisms. Major aspects of the structures of these organisms are predicted. The results can be considered of fundamental importance for several reasons. Firstly, assuming that the operator hierarchy is a proper basis for predictions, the result yields insight into the structure of future organisms. Secondly, the predictions are not extrapolations of presently observed trends, but are fully integrated with all historical system transitions in evolution. Thirdly, the extrapolations suggest the structures of intelligences that, one day, will possess more powerful brains than human beings.This study ends with a discussion of possibilities for falsification of the present theory, the implications of the present predictions in relation to recent developments in artificial intelligence and the philosophical implications of the role of humanity in evolution with regard to the creation of future neural network organisms. (shrink)

The idea of levels of organization plays a central role in the philosophy of the life sciences. In this article, I first examine the explanatory goals that have motivated accounts of levels of organization. I then show that the most state-of-the-art and scientifically plausible account of levels of organization, the account of levels of mechanism proposed by Bechtel and Craver, is fundamentally problematic. Finally, I argue that the explanatory goals can be reached by adopting a deflationary approach, where levels of (...) organization give way to more well-defined and fundamental notions, such as scale and composition. (shrink)

Paper type: Conceptual perspective. Background(s): Physics, biology, epistemology Perspectives: Theory of autopoietic systems, Popperian evolutionary epistemology and the biology of cognition. Context: This paper is a contribution to developing the theories of hierarchically complex living systems and the natures of knowledge in such systems. Problem: Dissonance between the literatures of knowledge management and organization theory and my observations of the living organization led to consideration of foundation questions: What does it mean to be alive? What is knowledge? How are life (...) and knowledge related? Method: The approach is synthetic and multidisciplinary. The concept of autopoiesis (as defined by Maturana) as a definition for life, and knowledge as a product of autopoiesis are developed from first principles regarding the behavior of dynamical systems in time. Results: Autopoiesis and the construction of knowledge are inseparable aspects of physical phenomena scalable to many levels of organization (e.g., cells, multicellular organisms, organizations, social systems, etc.). The result unifies theories of epistemology, physical dynamics, life, biological evolution, knowledge and social systems. Implications: Results highlight the importance to understand autopoiesis as first defined by Maturana and Varela – as a complex physical phenomenon persisting over time. Autopoietic “self-observation” is not paradoxical. As dynamic physical processes, any internal/external activities relating to “observations” are displaced in time. The worlds living systems act on are not those observed. “Circularly closed” systems are actually open spirals along the axis of time. (shrink)

The aim of this dissertation is to create a naturalistic philosophical picture of creative capacities that are specific to our species, focusing on artistic ability, religious reflection, and scientific study. By integrating data from diverse domains within a philosophical anthropological framework, I have presented a cognitive and evolutionary approach to the question of why humans, but not other animals engage in such activities. Through an application of cognitive and evolutionary perspectives to the study of these behaviors, I have sought to (...) provide a more solid footing for philosophical anthropological discussions of uniquely human behavior. In particular, I have argued that art, religion and science, which are usually seen as achievements that are quite remote from ordinary modes of reasoning, are subserved by evolved cognitive processes that serve functions in everyday cognitive tasks, that arise early and spontaneously in cognitive development, that are shared cross-culturally, and that have evolved in response to selective pressures in our ancestral past. These mundane cognitive processes provide a measuring rod with which we can assess a diversity of cultural phenomena; they form a unified explanatory framework to approach human culture. I have argued that we can explain uncommon thoughts in terms of interactions between common minds. This dissertation is subdivided into four parts. Part I outlines the problem of human uniqueness, examining theories on how humans conceptualize the world, and what their mental tool box looks like. Part II discusses the evolutionary and cognitive origins of human artistic behavior. Part III focuses on the cognitive science of religion, especially on how it can be applied to the reasoning of theologians and philosophers of religion. Part IV considers the cognitive basis of scientific practice. (shrink)

Theories of cognition and theories of social practices and institutions have often each separately acknowledged the relevance of the other; but seldom have there been consistent and sustained attempts to synthesize these two areas within one explanatory framework. This is precisely what my dissertation aims to remedy. I propose that certain recent developments and themes in philosophy of mind and cognitive science, when understood in the right way, can explain the emergence and dynamics of social practices and institutions. Likewise, the (...) view I construct explains how social practices and institutions shape the character of cognition of their constituent agents. Moreover, I explain both cognitive and social agency under the single explanatory framework provided by Dynamic Systems Theory. Drawing upon the phenomenological tradition, "embodied, "extended", "embedded", "enactive", and "ecological" approaches to cognition, as well as the conceptual resources of Dynamic Systems Theory, I construct a theory of agency that sees cognitive and social agents as far-from-equilibrium, open, recursively self-maintenant dynamic systems. Depending on the specifics of concrete circumstances, such systems, which I call "Dynamic Embodied Agents", may develop and possess emergent capacities for error-detection, flexible learning, normative behavior, representation, self-reflection, various modes of pattern-recognition, a temporal sense of self, and even moral responsibility. Some such systems are also sensitive to perceived social influences ; while reciprocally constituting and causally affecting them. (shrink)

Life-science phenomena are often explained by specifying the mechanisms that bring them about. The new mechanistic philosophers have done much to substantiate this claim and to provide us with a better understanding of what mechanisms are and how they explain. Although there is disagreement among current mechanists on various issues, they share a common core position and a seeming commitment to some form of scientific realism. But is such a commitment necessary? Is it the best way to go about mechanistic (...) explanation? In this article, we propose an alternative antirealist account that also fits explanatory practice in the life sciences. We pay special attention to mechanistic models, i.e. scientific models that involve a mechanism, and to the role of coherence considerations in building such models. To illustrate our points, we consider the mechanism for the action potential. 1 Introduction2 Some Core Features of Mechanistic Explanation3 Scientific Realism and Mechanistic Explanation4 Antirealist Mechanistic Explanation: The Case of the Action Potential5 Some Outstanding Issues for the Antirealist Mechanist6 Two Problems for the Realist Mechanist7 Conclusions. (shrink)

Reconstructability analysis (RA) decomposes wholes, namely data in the form either of set theoretic relations or multivariate probability distributions, into parts, namely relations or distributions involving subsets of variables. Data is modeled and compressed by variable-based decomposition, by more general state-based decomposition, or by the use of latent variables. Models, which specify the interdependencies among the variables, are selected to minimize error and complexity.

Biological boundaries are important because of what they reveal about the evolution of a lineage, the relationship between organisms of different lineages, the structure and function of particular subsystems of the organism, the interconnection between an organism and its environment, and a myriad of other important issues related to individuality, development, and evolution. Since there is no single unifying theory for all biological sciences, there are various possible theoretical characterizations of what counts as a biological boundary. Theoretical specificity is crucial (...) for the full characterization of a boundary; to this end, it is useful to begin with general properties and then to compare these to those properties specific to a particular biological boundary. This dual heuristic approach—top-down and bottom-up—can then be repeated to increase the robustness of the boundary characterization. In what follows, I explore both the general structural and functional aspects of biological boundaries and those specific to a biological subsystem, which itself is critical to boundary formation, maintenance, and evolution—the vertebrate immune system. It is a remarkable and sobering regularity of nature that organisms remain whole because their parts are constantly being built up and broken down, and this is no less true of their boundaries. The vertebrate immune system is at the center of this process of biological boundary maintenance and breakdown. (shrink)

How can we explain the intentional nature of an expert’s actions, performed without immediate and conscious control, relying instead on automatic cognitive processes? How can we account for the differences and similarities with a novice’s performance of the same actions? Can a naturalist explanation of intentional expert action be in line with a philosophical concept of intentional action? Answering these and related questions in a positive sense, this dissertation develops a three-step argument. Part I considers different methods of explanations in (...) cognitive neuroscience (Bennett & Hacker’s philosophical, conceptual analysis; Marr’s three levels of explanation; Neural Correlates of Consciousness research; mechanistic explanation), defending ‘mechanistic explanation’ as a method that provides the necessary tools for integrating interdisciplinary insights into human action. Furthermore, a dynamic, explanatory mechanism allows the assessment of the impact of learning and development on expert action in a valuable way that other methods don’t. Part II continues by scrutinizing several cognitive neuroscientific theories of learning and development (neuroconstructivism; dual-processing theories; simulation theory; extended mind/cognition hypothesis), arguing for the complex interactions between different types of processing and different action representations involved in expert action performances. Moreover, according to our discussion of a particular ‘simulation theory’ these interactions can be influenced in several ways with the use of language, allowing an agent to configure a specific action representation for performance at a later stage. The results of Parts I and II are then applied in Part III to a parallel discussion of philosophical analyses of intentional action (discussing i.a. Frankfurt, Bratman, Pacherie and Ricoeur) and cognitive neuroscientific insights in it. Both approaches are found to converge in emphasizing the importance for an expert to develop stable patterns of actions that comply maximally not only with his intentions, but also with his motor expertise and with situational conditions. Consequently, his actions – automatic, or not – rely on this ‘sculpted space of actions’. (shrink)

The general problem of visual search can be shown to be computationally intractable in a formal, complexity-theoretic sense, yet visual search is extensively involved in everyday perception, and biological systems manage to perform it remarkably well. Complexity level analysis may resolve this contradiction. Visual search can be reshaped into tractability through approximations and by optimizing the resources devoted to visual processing. Architectural constraints can be derived using the minimum cost principle to rule out a large class of potential solutions. The (...) evidence speaks strongly against bottom-up approaches to vision. In particular, the constraints suggest an attentional mechanism that exploits knowledge of the specific problem being solved. This analysis of visual search performance in terms of attentional influences on visual information processing and complexity satisfaction allows a large body of neurophysiological and psychological evidence to be tied together. (shrink)