How do minds emerge from developing brains? According to the representational features of cortex are built from the dynamic interaction between neural growth mechanisms and environmentally derived neural activity. Contrary to popular selectionist models that emphasize regressive mechanisms, the neurobiological evidence suggests that this growth is a progressive increase in the representational properties of cortex. The interaction between the environment and neural growth results in a flexible type of learning: minimizes the need for prespecification in accordance with recent neurobiological evidence (...) that the developing cerebral cortex is largely free of domain-specific structure. Instead, the representational properties of cortex are built by the nature of the problem domain confronting it. This uniquely powerful and general learning strategy undermines the central assumption of classical learnability theory, that the learning properties of a system can be deduced from a fixed computational architecture. Neural constructivism suggests that the evolutionary emergence of neocortex in mammals is a progression toward more flexible representational structures, in contrast to the popular view of cortical evolution as an increase in innate, specialized circuits. Human cortical postnatal development is also more extensive and protracted than generally supposed, suggesting that cortex has evolved so as to maximize the capacity of environmental structure to shape its structure and function through constructive learning. (shrink)

We examine the question of which aspects of language are uniquely human and uniquely linguistic in light of recent suggestions by Hauser, Chomsky, and Fitch that the only such aspect is syntactic recursion, the rest of language being either specific to humans but not to language (e.g. words and concepts) or not specific to humans (e.g. speech perception). We find the hypothesis problematic. It ignores the many aspects of grammar that are not recursive, such as phonology, morphology, case, agreement, and (...) many properties of words. It is inconsistent with the anatomy and neural control of the human vocal tract. And it is weakened by experiments suggesting that speech perception cannot be reduced to primate audition, that word learning cannot be reduced to fact learning, and that at least one gene involved in speech and language was evolutionarily selected in the human lineage but is not specific to recursion. The recursion-only claim, we suggest, is motivated by Chomsky’s recent approach to syntax, the Minimalist Program, which de-emphasizes the same aspects of language. The approach, however, is sufficiently problematic that it cannot be used to support claims about evolution. We contest related arguments that language is not an adaptation, namely that it is “perfect,” non-redundant, unusable in any partial form, and badly designed for.. (shrink)

This paper argues that two of my critics (Cowie and Wilson) have become fixated on Fodor’s notion of modularity, both to their own detriment and to the detriment of their understanding of Carruthers, 2006. The paper then focuses on the supposed inadequacies of the latter’s explanations of both content flexibility and human uniqueness, alleged by Machery and Cowie respectively.

Hierarchical organization is an essential characteristic of living things. Although most biologists affirm the concept of living things as hierarchically organized structures, there are widespread differences of interpretation in the meaning of hierarchy and of how the concept of hierarchy applies to living things. One such basic difference involves the distinction between the concept of control hierarchy and classification hierarchy. It is suggested that control hierarchies are distinguished from classification hierarchies in that while the former involve authority relationships between levels, (...) the latter do not. This is illustrated in an analysis of proposed hierarchies of replicators and interactors.The analysis of levels of hierarchies and their relationships also brings up the part-whole problem. An authority relationship between levels implies that the whole has a determining influence on the parts that make up the whole, and that parts have no independent, meaningful existence apart from the whole. The concept of an authority relationship in a part-whole relationship introduces the question of the independence or sovereignty of the components of the subordinate levels in a hierarchically organized living thing. This problem is discussed along with an analysis of the rather novel theory of enkapsis proposed by H. Dooyeweerd, in which he distinguishes part-whole relationships from enkaptic relationships. (shrink)

The Darwinian concept of natural selection was conceived within a set of Newtonian background assumptions about systems dynamics. Mendelian genetics at first did not sit well with the gradualist assumptions of the Darwinian theory. Eventually, however, Mendelism and Darwinism were fused by reformulating natural selection in statistical terms. This reflected a shift to a more probabilistic set of background assumptions based upon Boltzmannian systems dynamics. Recent developments in molecular genetics and paleontology have put pressure on Darwinism once again. Current work (...) on self-organizing systems may provide a stimulus not only for increased problem solving within the Darwinian tradition, especially with respect to origins of life, developmental genetics, phylogenetic pattern, and energy-flow ecology, but for deeper understanding of the very phenomenon of natural selection itself. Since self-organizational phenomena depend deeply on stochastic processes, self-organizational systems dynamics advance the probability revolution. In our view, natural selection is an emergent phenomenon of physical and chemical selection. These developments suggest that natural selection may be grounded in physical law more deeply than is allowed by advocates of the autonomy of biology, while still making it possible to deny, with autonomists, that evolutionary explanations can be modeled in terms of a deductive relationship between laws and cases. We explore the relationship between, chance, self-organization, and selection as sources of order in biological systems in order to make these points. (shrink)

The logic of scientific discovery is now a concern of computer scientists, as well as philosophers. In the computational approach to inductive inference, theories are treated as algorithms (computer programs), and the goal is to find the simplest algorithm that can generate the given data. Both computer scientists and philosophers want a measure of simplicity, such that simple theories are more likely to be true than complex theories. I attempt to provide such a measure here. I define a measure of (...) simplicity for directed graphs, inspired by Herbert Simon''s work. Many structures, including algorithms, can be naturally modelled by directed graphs. Furthermore, I adapt an argument of Simon''s to show that simple directed graphs are more stable and more resistant to damage than complex directed graphs. Thus we have a reason for pursuing simplicity, other than purely economical reasons. (shrink)

The functional complexity, or the number of functions, of organisms hasfigured prominently in certain theoretical and empirical work inevolutionary biology. Large-scale trends in functional complexity andcorrelations between functional complexity and other variables, such assize, have been proposed. However, the notion of number of functions hasalso been operationally intractable, in that no method has been developedfor counting functions in an organism in a systematic and reliable way.Thus, studies have had to rely on the largely unsupported assumption thatnumber of functions can be (...) measured indirectly, by using number ofmorphological, physiological, and behavioral parts as a proxy. Here, amodel is developed that supports this assumption. Specifically, the modelpredicts that few parts will have many functions overlapping in them, andtherefore the variance in number of functions per part will be low. If so,then number of parts is expected to be well correlated with number offunctions, and we can use part counts as proxies for function counts incomparative studies of organisms, even when part counts are low. Alsodiscussed briefly is a strategy for identifying certain kinds of parts inorganisms in a systematic way. (shrink)

Richard Levins’ distinction between aggregate, composed and evolved systems acquires new significance as we recognize the importance of mechanistic explanation. Criteria for aggregativity provide limiting cases for absence of organization, so through their failure, can provide rich detectors for organizational properties. I explore the use of failures of aggregativity for the analysis of mechanistic systems in diverse contexts. Aggregativity appears theoretically desireable, but we are easily fooled. It may be exaggerated through approximation, conditions of derivation, and extrapolating from some conditions (...) of decomposition illegtimately to others. Evolved systems particularly may require analyses under alternative complementary decompositions. Exploring these conditions helps us to better understand the strengths and limits of reductionistic methods. (shrink)

Biology lacks a central organism concept that unambiguously marks the distinction between organism and non-organism because the most important questions about organisms do not depend on this concept. I argue that the two main ways to discover useful biological generalizations about multicellular organization--the study of homology within multicellular lineages and of convergent evolution across lineages in which multicellularity has been independently established--do not require what would have to be a stipulative sharpening of an organism concept.

What is common to all languages is notation, so Universal Grammar can be understood as a system of notational types. Given that infants acquire language, it can be assumed to arise from some a priori mental structure. Viewing language as having the two layers of calculus and protocol, we can set aside the communicative habits of speakers. Accordingly, an analysis of notation results in the three types of Identifier, Modifier and Connective. Modifiers are further interpreted as Quantifiers and Qualifiers. The (...) resulting four notational types constitute the categories of Universal Grammar. Its ontology is argued to consist in the underlying cognitive schema of Essence, Quantity, Quality and Relation. The four categories of Universal Grammar are structured as polysemous fields and are each constituted as a radial network centred on some root concept which, however, need not be lexicalized. The branches spread out along troponymic vectors and together map out all possible lexemes. The notational typology of Universal Grammar is applied in a linguistic analysis of the ‘parts of speech’ using the English language. The analysis constitutes a ‘proof of concept’ in (1) showing how the schema of Universal Grammar is capable of classifying the so-called ‘parts of speech’, (2) presenting a coherent analysis of the verb, and (3) showing how the underlying cognitive schema allows for a sub-classification of the auxiliaries. (shrink)

Living organisms are currently most often seen as complex dynamical systems that develop and evolve in relation to complex environments. Reflections on the meaning of the complex dynamical nature of living systems show an overwhelming multiplicity in approaches, descriptions, definitions and methodologies. Instead of sustaining an epistemic pluralism, which often functions as a philosophical armistice in which tolerance and so-called neutrality discharge proponents of the burden to clarify the sources and conditions of agreement and disagreement, this paper aims at analysing: (...) (i) what has been Kant's original conceptualisation of living organisms as natural purposes; (ii) how the current perspectives are to be related to Kant's viewpoint; (iii) what are the main trends in current complexity thinking. One of the basic ideas is that the attention for structure and its epistemological consequences witness to a great extent of Kant's viewpoint, and that the idea of organisational stratification today constitutes a different breeding ground within which complexity issues are raised. The various approaches of complexity in biological systems are captured in terms of two different styles, universalism and (weak and strong) constructivism, between which hybrid forms exist. (shrink)

If one investigates a process that has several causes but assumes that it has only one cause, one risks ruling out important causal factors. Three mechanisms account for this mistake: either the significance of the single cause under test is masked by noise contributed by the unsuspected and uncontrolled factors, or the process appears only when two or more causes interact, or the process appears when there are present any of a number of sufficient causes which are not mutally exclusive. (...) In ecology and evolutionary biology, experiments usually test single factor hypotheses, and many scientists apparently believe that hypotheses incorporating several factors are so much more difficult to test that to do so would not be practical. We discuss several areas in ecology and evolutionary biology in which the presupposition of simple causation has apparently impeded progress. We also examine a more mature field, the study of atherosclerosis, in which single factor studies did significantly delay progress towards understanding what now appears to be a multifactor process. The problem has three solutions: either factorial experiments, dynamic models that make quantitative predictions, response-surface methods, or all three. In choosing a definition for ‘cause’, we make a presupposition that profoundly influences subsequent observations and experimental designs. Alternative definitions of causation should be considered as contributing to potential cures for research problems. (shrink)

Organizations in changing environments need to become flexible, responsive and participative. We develop an understanding of governance in these organizations by drawing analogies between organization theory and theories of non-linear dynamics. We identify freedom and creativity as driving principles in 'chaotic' participative organizations, and explore the ethics of their exercise within organizational communities of practice, communities of discernment and communities of commitment.

The holism-reductionism debate, one of the classic subjects of study in the philosopy of science, is currently at the heart of epistemological concerns in ecology. Yet the division between holism and reductionism does not always stand out clearly in this field. In particular, almost all work in ecosystem ecology and landscape ecology presents itself as holistic and emergentist. Nonetheless, the operational approaches used rely on conventional reductionist methodology.From an emergentist epistemological perspective, a set of general 'transactional' principles inspired by the (...) work of J. Dewey and J.K. Feibleman are proposed in an effort to develop a coherent ontological and methodological semantics. (shrink)

Long-term vegetation dynamics based on paleo-pollen data display transient behaviour, often alternating in phase between predominant determinism and predominant 'turbulence', when viewed as a trajectory in a multivariate phase space. Given this, the metaphor of vegetation dynamics as a 'flowing stream', first introduced by Cooper in his classic 1926 paper entitled "The fundamentals of vegetation change", is re-examined and revealed to be not only useful, but strikingly realistic. Vegetation dynamic theory is reviewed and classic theories are found to reflect reality (...) poorly. It is suggested that vegetation dynamics is a far from equilibrium system, and that the application of nonequilibrium thermodynamic theory is appropriate. (shrink)

The Architecture of the Mind is itself built on foundations that deserve probing. In this brief commentary I focus on these foundations—Carruthers’ conception of modularity, his arguments for thinking that the mind is massively modular in structure, and his view of human cognitive architecture.

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 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. (shrink)

A central concern of the cognitive science of language since its origins has been the concept of the linguistic system. Recent approaches to the system concept in language point to the exceedingly complex relations that hold between many kinds of interdependent systems, but it can be difficult to know how to proceed when “everything is connected.” This paper offers a framework for tackling that challenge by identifying *scale* as a conceptual mooring for the interdisciplinary study of language systems. The paper (...) begins by defining the scale concept—simply, the possibility for a measure to be larger or smaller in different instances of a system, such as a phonemic inventory, a word's frequency value in a corpus, or a speaker population. We review sites of scale difference in and across linguistic subsystems, drawing on findings from linguistic typology, grammatical description, morphosyntactic theory, psycholinguistics, computational corpus work, and social network demography. We consider possible explanations for scaling differences and constraints in language. We then turn to the question of *dependencies between* sites of scale difference in language, reviewing four sample domains of scale dependency: in phonological systems, across levels of grammatical structure (Menzerath's Law), in corpora (Zipf's Law and related issues), and in speaker population size. Finally, we consider the implications of the review, including the utility of a scale framework for generating new questions and inspiring methodological innovations and interdisciplinary collaborations in cognitive-scientific research on language. (shrink)

The abstract basis of modern computation is the formal description of a finite state machine, the Universal Turing Machine, based on manipulation of integers and logic symbols. In this contribution to the discourse on the computer-brain analogy, we discuss the extent to which analog computing, as performed by the mammalian brain, is like and unlike the digital computing of Universal Turing Machines. We begin with ordinary reality being a permanent dialog between continuous and discontinuous worlds. So it is with computing, (...) which can be analog or digital, and is often mixed. The theory behind computers is essentially digital, but efficient simulations of phenomena can be performed by analog devices; indeed, any physical calculation requires implementation in the physical world and is therefore analog to some extent, despite being based on abstract logic and arithmetic. The mammalian brain, comprised of neuronal networks, functions as an analog device and has given rise to artificial neural networks that are implemented as digital algorithms but function as analog models would. Analog constructs compute with the implementation of a variety of feedback and feedforward loops. In contrast, digital algorithms allow the implementation of recursive processes that enable them to generate unparalleled emergent properties. We briefly illustrate how the cortical organization of neurons can integrate signals and make predictions analogically. While we conclude that brains are not digital computers, we speculate on the recent implementation of human writing in the brain as a possible digital path that slowly evolves the brain into a genuine (slow) Turing machine. (shrink)

A long-standing problem in understanding goal-directed systems has been the insufficiency of mechanistic explanations to make sense of them. This paper offers a solution to this problem. It begins by observing the limitations of mechanistic decompositions when it comes to understanding physical fields. We argue that introducing the field concept, as it has been developed in field theory, alongside mechanisms is able to provide an account of goal directedness in the sciences.

Cognitive scientists have focused too narrowly on the acquisition of data and on the methods to extract patterns from those data. We argue that a successful science of the mind requires widening our focus to include the problems being solved by cognitive processes. Frameworks that characterize cognitive processes in terms of instrumental problem‐solving, such as those within the evolutionary social sciences, become necessary if we wish to discover more accurate descriptions of those processes.

Complexity sciences have become famous worldwide thanks to several popular books that served as echo chambers of their promises. These consisted in departing from “classical science” defined as deterministic, reductionist, analytic and mono-disciplinary. Their founders and supporters declared that complexity sciences were going to give rise (or that they have given rise) to a post-Laplacian, antireductionist, holistic and interdisciplinary approach. By taking a closer look at their content and practices, I argue in this article that, because of their physics-oriented, computationalist, (...) and mathematical assumptions, complexity sciences have paradoxically produced knowledge at odds with these four tenets. (shrink)

In this interview, Daniel Little provides an overview of his life and work in academia. Among other things, he discusses an actor-centred approach to theory of social ontology. For Little, this approach complements the assumptions of critical realism, in that it accords full ontological importance to social structures, causal mechanisms, and enduring and influential normative systems. The approach casts doubt, however, on the idea of ‘strong emergence' of social structures, the idea that social structures have properties and causal powers that (...) cannot, in principle be explained by their constituents (social actors) and their relationships. Little’s approach to social ontology endorses instead the idea of relative explanatory autonomy. During the interview, many points of convergence become evident between this actor-centred approach to social ontology and the fundamental insights of critical realism. According to Little, analysis of social ontology also turns out to be especially relevant to philosophy of history. (shrink)

This paper examines the dispute between Burge and McDowell over methodology in the philosophy of perception. Burge (2005, 2011) has argued that the disjunctivism posited by naive perceptual realists is incompatible with the results of current perceptual science, while McDowell (2010, 2013) defends his disjunctivism by claiming an autonomous field of enquiry for perceptual epistemology, one that does not employ the classificatory schemes of the science. Here it is argued that the crucial point at issue in the dispute is Burge’s (...) acceptance, and McDowell’s rejection, of the ‘Cartesian idealization’ of mind as a self-contained system. Burge’s case against disjunctivism rests on the assumption of a clearly demarcated boundary between mind and world, a picture of the mind that McDowell’s philosophy reacts against. This boundary is required for scientific, causal explanations of perceptual processing because it is a simplifying assumption that helps present scientists with a clearly demarcated object of investigation. Concurring with McDowell, I conclude that philosophers need not carve up their objects of investigation in the same way. (shrink)

Why would decision makers adopt heuristics, priors, or in short “habits” that prevent them from optimally using pertinent information—even when such information is freely-available? One answer, Herbert Simon’s “procedural rationality” regards the question invalid: DMs do not, and in fact cannot, process information in an optimal fashion. For Simon, habits are the primitives, where humans are ready to replace them only when they no longer sustain a pregiven “satisficing” goal. An alternative answer, Daniel Kahneman’s “mental economy” regards the question valid: (...) DMs make decisions based on optimization. Kahneman understands optimization not differently from the standard economist’s “bounded rationality.” This might surprise some researchers given that the early Kahneman, along with Tversky, have uncovered biases that appear to suggest that choices depart greatly from rational choices. However, once we consider cognitive cost as part of the constraints, such biases turn out to be occasional failures of habits that are otherwise optimal on average. They are optimal as they save us the cognitive cost of case-by-case deliberation. While Kahneman’s bounded rationality situates him in the neoclassical economics camp, Simon’s procedural rationality echoes Bourdieu’s “habitus” camp. To abridge the fault line of the two camps, this paper proposes a “two problem areas hypothesis.” Along the neoclassical camp, habits satisfy wellbeing, what this paper calls “substantive satisfaction.” Along the Bourdieu camp, habits satisfy belonging, love, and bonding with one’s environment, what this paper calls “transcendental satisfaction.”. (shrink)

Among the notions of levels invoked in accounts of biological phenomena, I focus on two: levels of production mechanisms and levels of control mechanisms. I argue that these two notions of level exhibit different characteristics: production mechanisms are organized hierarchically while control mechanisms are often organized heterarchically. I illustrate the differences in these modes of organization by examining production and control mechanisms involved in cell division in Escherichia coli and in circulation of blood in mammals. I conclude by exploring how (...) these two notions of levels can be integrated by adopting a perspective in which organisms are autonomous systems maintaining themselves far from equilibrium. (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)

In the management and business ethics literatures, stakeholder engagement has been demonstrated to lead to more ethical management practices. However, there may be limits on the extent to which stakeholder engagement can, as currently conceptualized, resolve some of the more difficult ethical challenges faced by managers. In this paper we argue that stakeholder engagement, when seen as a way of reducing five types of knowledge problems—risk, ambiguity, complexity, equivocality, and a priori irreducible uncertainty—can aid managers in resolving such ethical challenges. (...) Using a practical illustration of the ethical challenges surrounding the development and application of genetic modification technologies, we demonstrate how stakeholder engagement enables managers to better address these knowledge problems, thereby to manage more ethically. In this way, we suggest that stakeholder engagement has an even more crucial role to play in business ethics research and practice. (shrink)

This paper proposes an integrated-structure notion of interlevel emergence, from a dynamic relational ontological perspective. First, I will argue that only the individualist essentialism of atomistic metaphysics can block the possibility of interlevel emergence. Then I will show that we can make sense of emergence by recognizing the formation of structures of transformative and interdependent causal relations in the generation and development of a particular class of mereological complexes called integrated systems. Finally, I shall argue that even though the emergent (...) structural attributes of such systems are not micro-determined nor micro-reducible, they can still be accounted for by an interlevel integrative neo-mechanistic form of explanation. (shrink)

We present two case studies from contemporary biology in which we observe conflicts between established and emerging approaches. The first case study discusses the relation between molecular biology and systems biology regarding the explanation of cellular processes, while the second deals with phylogenetic systematics and the challenge posed by recent network approaches to established ideas of evolutionary processes. We show that the emergence of new fields is in both cases driven by the development of high-throughput data generation technologies and the (...) transfer of modeling techniques from other fields. New and emerging views are characterized by different philosophies of nature, i.e. by different ontological and methodological assumptions and epistemic values and virtues. This results in a kind of conflict we call “epistemic competition” that manifests in two ways: On the one hand, opponents engage in mutual critique and defense of their fundamental assumptions. On the other hand, they compete for the acceptance and integration of the knowledge they provide by a broader scientific community. Despite an initial rhetoric of replacement, the views as well as the respective audiences come to be seen as more clearly distinct during the course of the debate. Hence, we observe—contrary to many other accounts of scientific change—that conflict results in the formation of new niches of research, leading to co-existence and perceived complementarity of approaches. Our model thus contributes to the understanding of the pluralization of the scientific landscape. (shrink)

In a wide range of fields, the word “modular” is used to describe complex systems that can be decomposed into smaller systems with limited interactions between them. This essay argues that mathematical knowledge can fruitfully be understood as having a modular structure and explores the ways in which modularity in mathematics is epistemically advantageous.

The Lisp programming language is often described as the first functional programming language and also as an important early AI language. In the history of functional programming, however, it occupies a rather anomalous position, as the circumstances of its development do not fit well with the widely accepted view that functional languages have been developed through a theoretically-inspired project of deriving practical programming languages from the lambda calculus. This paper examines the origins of Lisp in the early AI programming work (...) of the mid-to-late 1950s, and in particular in the work of Allen Newell, Cliff Shaw and Herbert Simon. Their 1956 program, the Logic Theory Machine, introduced new ideas about data and program structures that were articulated in response to perceived limitations in existing programming technique. Later writers, notably John Backus, have described these features as constituting a “programming language style” distinct from the traditional style that preceded it. The paper examines the origins of the earlier style in practices of manual computation, analyses the key technical differences between it and the style first manifested in the Logic Theory Machine, and concludes that programming practice and experience play a large and underappreciated role in the development of programming styles and languages. (shrink)

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

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)

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)

La psychologie a toujours traité le comportement et l’expérience comme étant enchâssés dans un flux temporel unidimensionnel, « le courant du comportement » dans lequel les événements et les actions occupent des intervalles de temps qui ne se chevauchent pas. Pourtant, une analyse phénoménologique révèle que la structure de nos vies est bien plus riche et intéressante. En utilisant la notion de « quasidécomposabilité » de Herbert Simon, je décris cette structure comme un assemblage d’épisodes quasi-indépendants se réalisant de façon (...) concurrente, et de nature asynchrone. Il s’agit d’une « structure profonde» des vies contrairement à la conception courante qui conçoit les vies comme « plates » Psychology has always treated behavior and experience as embedded in a unidimensional flow in time, the "stream of behavior." This means that events and actions occupy non-overlapping time-intervals in this stream. Nevertheless a phenomenological analysis reveals that the structure of lives is richer and far more interesting. Using Herb Simon's notion of near-decomposability, I describe the structure of lives as a composite of nearly independent strands that run concurrently, and are asynchronous. This is a "deep structure"' of lives in contrast to the current conception, which conceives of lives as "flat." . (shrink)

The article introduces recursive ontology, a general ontology which aims to describe how being is organized and what are the processes that drive it. In order to answer those questions, I use a multidisciplinary approach that combines the theory of levels, philosophy and systems theory. The main claim of recursive ontology is that being is the product of a single recursive process of generation that builds up all of reality in a hierarchical fashion from fundamental physical particles to human societies. (...) To support this assumption, I provide the general laws and the basic principles of recursive ontology as well as a semi-formalised model of the theory based on a recursive generative grammar. Recursive ontology not only actively promotes a multidisciplinary investigation of reality, but also can be used as a general framework to develop future domain-specific theories. (shrink)

The emergence of transaction cost economics in the early 1970s with Oliver Williamson's successful reconciliation of the so-called neoclassical approach with Herbert Simon's organizational theory can be considered an important part of the first cognitive turn in economics. The development of TCE until the late 1980s was particularly marked by treating the firm as an avoider of negative frictions, i.e., of transaction costs. However, since the 1990s TCE has been enriched by various approaches stressing the role of the firm in (...) creating positive value, e.g., the literature on modularity. Hence, a second cognitive turn has taken place: the firm is no longer only seen as an avoider of negative costs but also as a creator of positive knowledge. (shrink)

It is proposed that there are important generalizations about problem solving in design activity that reach across specific disciplines. A framework for the study of design is presented that (a) characterizes design as a radial category and fleshes out the task environment of the prototypical cases; (b) takes the task environment seriously; (c) shows that this task environment occurs in design tasks, but does not occur in every nondesign task; (d) explicates the impact of this task environment on the design (...) problem space; and (e) demonstrates that, given the structure of the information-processing system, the features noted in the problem spaces of design tasks will not all occur in problem spaces where the task environment is vastly different. This analysis leads to the claim that there are a set of invariant features in the problem spaces of design situations that collectively constitute a design problem space. Protocol studies are reported in which the problem spaces of three design tasks in architecture, mechanical engineering, and instructional design are explored and compared with several protocols from nondesign problem-solving tasks. (shrink)