Contemporary complexity theory has been instrumental in providing novel rigorous definitions for some classic philosophical concepts, including emergence. In an attempt to provide an account of emergence that is consistent with complexity and dynamical systems theory, several authors have turned to the notion of constraints on state transitions. Drawing on complexity theory directly, this paper builds on those accounts, further developing the constraint-based interpretation of emergence and arguing that such accounts recover many of the features of more traditional accounts. We (...) show that the constraint-based account of emergence also leads naturally into a meaningful definition of self-organization, another concept that has received increasing attention recently. Along the way, we distinguish between order and organization, two concepts which are frequently conflated. Finally, we consider possibilities for future research in the philosophy of complex systems, as well as applications of the distinctions made in this paper. (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)

I propose a cautionary assessment of the recent debate concerning the impact of the dynamical approach on philosophical accounts of scientific explanation in the cognitive sciences and, particularly, the cognitive neurosciences. I criticize the dominant mechanistic philosophy of explanation, pointing out a number of its negative consequences: In particular, that it doesn’t do justice to the field’s diversity and stage of development, and that it fosters misguided interpretations of dynamical models’ contribution. In order to support these arguments, I analyze a (...) case study in computational neuroethology and show why it should not be understood through a mechanistic lens; I specially focus on Zednik’s mechanistic interpretation of the case study. In addition, I argue for a greater appreciation of the relation between explanation and other epistemic goals in the field, as well as an increased sensitivity towards the associated contextual factors. (shrink)

This article attempts to show why an experimental hypothesis is plausible and merits testing; in brief, the hypothesis is that autism begins with a failure in early learning and that changing the environment of early learning would dramatically change its incidence. Strong statistical evidence supporting this hypothesis has been published by Waldman et al., but to date this evidence has largely been ignored, perhaps because it challenges prevalent beliefs about autism. This article also suggests that the current epidemic of autism (...) is serious enough, and intellectually mysterious enough, to merit attention from a wider community of brain scientists: New ideas are needed. A confirmation of this article’s hypothesis would have interesting implications for brain science. (shrink)

Complexity theory attempts to explain, at the most general possible level, the interesting behaviors of complex systems. Two such behaviors are the emergence of simple or stable high-level behavior from relatively complex low-level behavior, and the emergence of sophisticated high-level behavior from relatively simple low-level behavior; they are often found nested in the same system. Concerning the emergence of simplicity, this essay examines Herbert Simon's explanation from near-decomposability and a stochastic explanation that generalizes the approach of statistical physics. A more (...) general notion of an abstract difference-making structure is introduced with examples, and a discussion of evolvability follows. Concerning the emergence of sophistication, this essay focuses on, first, the energetics approach associated with dissipative structures and the fourth law of thermodynamics, and second, the notion of a complex adaptive system. (shrink)

Three objections to Max Urchs's paper on complexity are discussed. First, Urchs's macroeconomic illustrations of the benefits of complexity thinking are open to more conventional interpretations. Second, Urchs formulates a thesis concerning the relationship between science and society which is untenable if taken as a historical claim and insufficiently developed if taken as a metaphor. Third, methodological problems in history and philosophy of science plague Urchs's discussion of neuroscience.

The development of new theory is often spurred by novel techniques that provide better answers to existing questions, or that allow asking new ones. In the field of strategy and organization science, models of complex adaptive systems have renewed theoretical work on a fundamental question: how organizations can adapt effectively to their environments. This article has three objectives: to highlight some areas where models of organizations as complex adaptive systems have made substantial contributions: the search for solutions to sets of (...) interdependent choices, the challenge of balancing processes of exploration and exploitation, and the organization of imperfect decision makers; to point to several challenges and tradeoffs which can limit the explanatory power and eventual impact of the modeling enterprise; and to sketch out possible future directions of research that would do further justice to the notion of organizations as complex adaptive systems. (shrink)

Einstein’s razor, a corollary of Ockham’s razor, is often paraphrased as follows: make everything as simple as possible, but not simpler. This rule of thumb describes the challenge that designers of a legal system face—to craft simple laws that produce desired ends, but not to pursue simplicity so far as to undermine those ends. Complexity, simplicity’s inverse, taxes cognition and increases the likelihood of suboptimal decisions. In addition, unnecessary legal complexity can drive a misallocation of human capital toward comprehending and (...) complying with legal rules and away from other productive ends. While many scholars have offered descriptive accounts or theoretical models of legal complexity, most empirical research to date has been limited to simple measures of size, such as the number of pages in a bill. No extant research rigorously applies a meaningful model to real data. As a consequence, we have no reliable means to determine whether a new bill, regulation, order, or precedent substantially effects legal complexity. In this paper, we begin to address this need by developing a proposed empirical framework for measuring relative legal complexity. This framework is based on “knowledge acquisition”, an approach at the intersection of psychology and computer science, which can take into account the structure, language, and interdependence of law. We then demonstrate the descriptive value of this framework by applying it to the U.S. Code’s Titles, scoring and ranking them by their relative complexity. We measure various features of a title including its structural size, the net flow of its intra-title citations and its linguistic entropy. Our framework is flexible, intuitive, and transparent, and we offer this approach as a first step in developing a practical methodology for assessing legal complexity. (shrink)

In the methodology of scientific research programs (MSRP) there are important features on the problem of prediction, especially regarding novel facts. In his approach, Imre Lakatos proposed three different levels on prediction: aim, process, and assessment. Chapter 5 pays attention to the characterization of prediction in the methodology of research programs. Thus, it takes into account several features: (1) its pragmatic characterization, (2) the logical perspective as a proposition, (3) the epistemological component, (4) its role in the appraisal of research (...) programs, and (5) its place as a value for scientific research. -/- The notion of “novel facts” is highly relevant in his conception, where several aspects are involved: the directions of novel facts, the different kinds of novelty, and the transition from six possible options of “novel facts” to four choices. Thereafter, the prediction of novel facts as the criterion of appraisal is considered. On the one hand, this requires analyzing the theoretical, empirical, and heuristic possibilities of appraisal; and, on the other, whether there is an overemphasis on the role of prediction in methodology of scientific research programs. As a consequence, there is an analysis of Lakatos’ criterion of appraisal in MSRP and economics. (shrink)

Since the foundation of the Santa Fe Institute, the new science of complex adaptive systems has seen extraordinary development, breaking with previous, more epistemological, trends in complexity theory. This article makes a critique of CAS as a model of the current global complexity. Its basic model, the cellular automaton, which focuses on the interactive dynamics among components, ignores the nature of any complex system as constructed by the observer/actor and is unable to explain the sociohistorical construction of the agents/subjects and (...) the social structure in which they are located, thus being unable to accurately represent the overall social complexity. (shrink)

In the paper we consider an electricity provider company that makes decision on allocating resources on electric network maintenance. The investments decrease malfunction rate of network nodes. An accidental event or a malfunctioning on underground system can have various consequences and in different perspectives, such as deaths and injuries of pedestrians, fires in nearby locations, disturbances in the flow of vehicular traffic, loss to the company image, operating and financial losses, etc. For this reason it is necessary to apply an (...) approach of the risk management that considers the multidimensional view of the consequences. Furthermore an analysis of decision making should consider network dependencies between the nodes of the electricity distribution system. In the paper we propose the use of the simulation to assess the network effects in the multidimensional risk assessment in electricity grid. The analyzed effects include node overloading due to malfunction of adjacent nodes and blackouts that take place where there is temporarily no path in the grid between the power plant and a node. The simulation results show that network effects have crucial role for decisions in the network maintenance – outcomes of decisions to repair a particular node in the network can have significant influence on performance of other nodes. However, those dependencies are non-linear. The effects of network connectivity on its multidimensional performance assessment depend heavily on the overloading effect level. The simulation results do not depend on network type structure – however simulation outcomes for random networks have shown higher variance compared to small-world networks. (shrink)

In this contribution, I comment on Raffaella Campaner’s defense of explanatory pluralism in psychiatry (in this volume). In her paper, Campaner focuses primarily on explanatory pluralism in contrast to explanatory reductionism. Furthermore, she distinguishes between pluralists who consider pluralism to be a temporary state on the one hand and pluralists who consider it to be a persisting state on the other hand. I suggest that it would be helpful to distinguish more than those two versions of pluralism – different understandings (...) of explanatory pluralism both within philosophy of science and psychiatry – namely moderate/temporary pluralism, anything goes pluralism, isolationist pluralism, integrative pluralism and interactive pluralism. Next, I discuss the pros and cons of these different understandings of explanatory pluralism. Finally, I raise the question of how to implement or operationalize explanatory pluralism in scientific practice; how to structure the “genuine dialogue” or shape “the pluralistic attitude” Campaner is referring to. As tentative answers, I explore a question-based framework for explanatory pluralism as well as social-epistemological procedures for interaction among competing approaches and explanations. (shrink)

In this paper, I discuss the concept of complexity. I show that the principle of natural selection as acting on complexity gives a solution to the problem of reconciling the seemingly contradictory notion of generally increasing complexity and the observation that most species don’t follow such a trend. I suggest the process of evolution to be illustrated by means of a schematic diagram of complexity versus time, interpreted as a form of the Tree of Life. The suggested model implies that (...) complexity is cumulatively increasing, giving evolution a direction, an arrow of time, thus also implying that the latest emerging species will be the one with the highest level of complexity. Since the human species is the last species evolved in the evolutionary process seen at large, this means that we are the species with the highest complexity. The model implies that the human species constitutes an integral part of organic evolution, yet rendering us the exclusive status as the species of the highest complexity. (shrink)

A systematic theory of naturalism, bridging metaphysics and the science of complexity and emergence.

This article contends that the evolution toward interdisciplinary collaboration that we are witnessing in the sciences must also occur in the humanities to ensure their very survival. That is, humanists must be open to working with scientists and social scientists interested in similar research questions and vice versa. Digital humanities is a positive first step. Complexity science should be the next step. Even though much of the ground-breaking work in complexity science has been done in the natural sciences and mathematics, (...) it can, if critically adapted, provide the needed metaphor for a broad integration of disciplines, humanistic and otherwise. Given its almost a-disciplinary nature, a complexity approach to the research problems in the humanities necessarily breaks down silos. Moreover, it can restore and reframe the seamless intellectual fabric sought by researchers before the atomization of the various disciplines in the nineteenthcentury academy. (shrink)

Robert Jervis's System Effects (1997) shares a great deal with game theory, complex-systems theory, and systems theory in international relations, yet it transcends them all by taking account of the role of ideas in human behavior. The ideational element inserts unpredictability into Jervis's understanding of system effects. Each member of a ?system? of interrelated actors interprets her situation to require certain actions based on the effects these will cause among other members of the system, but these other actors' responses to (...) one's action will be based on their own perceptions of their situation and their interpretations of what it requires. These ideas are fallible, but we cannot predict the mistakes people will make if the errors are based on information we do not have or do not interpret in the same way they do. Not only members of a system but social-scientific observers and policy makers are ignorant of others' information and interpretations, and therefore are as likely to err in their behavioral predictions as are members of the system. Thus, Jervis's book raises serious questions about how to evaluate policies directed toward producing positive system effects. The questions are unanswerable at this point, but they might be susceptible to analysis by an ambitious form of political theory. (shrink)

Complexity science has witnessed a number of advances since the publication of Jervis's System Effects. These advances better allow us to untangle the messy elements in a system and predict sets of likely outcomes. However, just because a system is complex doesn't mean that all the ideas relating to complexity—such as agent-based modeling, path dependency, tipping points, between-class versus within-class effects, and networks—are necessarily relevant. One of our tasks is to determine whether they are—and, if so, their implications. As examples, (...) we use China's role in the formation of the United States housing bubble; the federal government's bailout of AIG and Bear Stearns but not Lehman Brothers; and China's failure to experience a regime change such as the Middle East's Arab Spring. (shrink)

These preprints were automatically compiled into a PDF from the collection of papers deposited in PhilSci-Archive in conjunction with the PSA 2012.

Reductionism has dominated science and philosophy for centuries. Complexity has recently shown that interactions—which reductionism neglects—are relevant for understanding phenomena. When interactions are considered, reductionism becomes limited in several aspects. In this paper, I argue that interactions imply nonreductionism, non-materialism, non-predictability, non-Platonism, and non-Nihilism. As alternatives to each of these, holism, informism, adaptation, contextuality, and meaningfulness are put forward, respectively. A worldview that includes interactions not only describes better our world, but can help to solve many open scientific, philosophical, and (...) social problems caused by implications of reductionism. (shrink)

Evolution is often characterized as a tinkerer that creates efficient but messy solutions to problems. We analyze the nature of the problems that arise when we try to explain and understand cognitive phenomena created by this haphazard design process. We present a theory of explanation and understanding and apply it to a case problem – solutions generated by genetic algorithms. By analyzing the nature of solutions that genetic algorithms present to computational problems, we show that the reason for why evolutionary (...) designs are often hard to understand is that they exhibit non-modular functionality, and that breaches of modularity wreak havoc on our strategies of causal and constitutive explanation. (shrink)

Many of the existing ethical analyses of genetic engineering technologies (GET) focus on how they can be used to enhance individuals—to improve individual well-being, health and cognition. There is a gap in the current literature about the specific ways enhancement technologies could be used to improve our populations and species, viewed as a whole. In this paper, I explore how GET may be used to enhance the species through improvements in the gene pool. I argue one aspect of the species (...) that may be desirable to enhance is ‘persistence’ or long-term viability. I then look at some of the ways in which GET could be used to improve human persistence and argue that the use of GET to secure benefits for individuals may compromise persistence. This suggests conflicts between uses of GET to enhance individuals and uses to promote the persistence of the species may occur. As GET are further developed, the likelihood that these conflicts will actually arise, and how we should resolve them if they do, will need to be considered. (shrink)

The mass popular dissent which has marked the early twenty-first century, from al-Qaeda to the Arab Spring to the Occupy movement, can be read as expressions of collective, subjective, existential mutation. This reading is inspired by Félix Guattari, who described the 1979 Iranian revolution, the Polish Solidarity movement and the 1989 Chinese student demonstrations as demands for subjective singularisation. In each of these examples of social discontent, past and present, demands vary widely even within the same movement, spanning economics, lifestyle, (...) religion and politics, and ranging from cries for liberation to conservative returns to ancient tradition. In order to perform a schizoanalysis of such amorphous movements, Guattari developed a four-part ecosophic object, composed of existential Territories, Universes of value, material-energetic Flows and machinic Phyla. Assemblages can be dynamically mapped along these four dimensions. Of crucial importance in the age of extreme deterritorialisation is the existential Territory, which could potentially take the form of a homeland based on poetry or music rather than on ethnicity or even place. Such portable homelands would characterise the dissensual processual post-media era for which Guattari longed. (shrink)

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

The task of designing effective economic and political institutions requires substantial foresight. The designer must anticipate not only the behavior of individual actors, but also how that behavior will aggregate. Rising complexity brought about by increases in speeds of adaptation, diversity, connectedness, and interdependence make institutional design all the more challenging. Given the focus on equilibria, the extant literature on mechanism design might appear incapable of coping with this complexity. Yet, I suggest that a deeper engagement with the origins of (...) the mechanism-design framework reveals insights that may help us design robust, adaptive institutions that can harness complexity. (shrink)

In some areas of cognitive science we are confronted with ultrafast cognition, exquisite context sensitivity, and scale-free variation in measured cognitive activities. To move forward, we suggest a need to embrace this complexity, equipping cognitive science with tools and concepts used in the study of complex dynamical systems. The science of movement coordination has benefited already from this change, successfully circumventing analogous paradoxes by treating human activities as phenomena of self-organization. Therein, action and cognition are seen to be emergent in (...) ultrafast symmetry breaking across the brain and body; exquisitely constituted of the otherwise trivial details of history, context, and environment; and exhibiting the characteristic scale-free signature of self-organization. (shrink)

Lately there has been a growing interest in evolutionary studies concerning how the regularities and patterns found in the living cell could have emerged spontaneously by way of self-assembly and self-organization. It is reasonable to postulate that the chemical compounds found in the primitive Earth would have mostly been very simple in nature, and would have been immersed in the natural dynamics of the physical world, some of which would have involved self-organization. It seems likely that some molecular processes self-organized (...) spontaneously into a hierarchy of complex behaviours. Our conceptual search herein reaches back to the time when prebiotic phenomena began to take shape. This was before the origin of life, so in this paper we hope to shed new light on some of the theoretical issues that surround the ways in which cellular organization might have evolved without the aid of replicated information. (shrink)

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

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

The mathematical universe hypothesis is a theory that the physical universe is not merely described by mathematics, but is mathematics, specifically a mathematical structure. Our research provides evidence that the mathematical structure of the universe is biological in nature and all systems, processes, and objects within the universe function in harmony with biological patterns. Living organisms are the result of the universe’s biological pattern and are embedded within their physiology the patterns of this biological universe. Therefore physiological patterns in living (...) organisms can be used as models to structurally map analogies from the biological domain to any target domain to reveal and understand the biological nature of the target domain. Our paper explores various analogies, structurally mapping a red blood cell to a cup; proteins produced from ribosomes to music produced from instruments; a beating heart to the melting and freezing of Antartica; cells, tissue, organs and blood, to people, organizations, industries, and money, and; bio-economic concepts in cellular society to socioeconomic concepts in human society. It also discusses how phenomena in cellular mitosis can help explain phenomena in the universe, such as black holes, dark matter, dark energy, and the structure of the universe. Building upon the concept of perennial wisdom, our research has provided evidence that the ideas of a biological universe were expressed across many past cultures and historical periods. The implications of this theory are vast, encompassing fields such as physics, science, philosophy, religion, law, economics, politics, and engineering, thus serving as a unifying theory for all knowledge. Our theory is supported by meta-analysis of scientific, historic, and religious literature, observations and first principles logic. (shrink)

Argues that truth, moral right, political right, and aesthetic value may be understood as arising out of a naturalist account of humanity, if naturalism is rightly conceived.

Bhaskar's philosophy supports society via a process of homeostasis to resist socioecological system disintegration by developing its values and ethics in response to endogenous and exogenous change. To the contrary, positivist (first generation) and hermeneuticist (second generation) approaches to systems theory have distorted humanity's mechanism of homeostasis because, amongst other things, they disallow the use of facts to guide values/actions. Since acting on knowledge is, ceteris paribus, a given in Bhaskar's approach, resolving socioecological system problems involves correcting the method of (...) homeostasis (our method of finding knowledge and acting on it) rather than correcting the consequences of the failure of homeostasis (the poorly informed action or inaction). This is reminiscent of Gandhi's approach to social transformation in which we trust the means to arrive at appropriate ends and it releases activists from having to be keepers of the moral high-ground or having to try to change people's behaviour. (shrink)

There is growing need for hybrid curricula that integrate constructivist methods from Science and Technology Studies (STS) into both engineering and policy courses at the undergraduate and graduate levels. However, institutional and disciplinary barriers have made implementing such curricula difficult at many institutions. While several programs have recently been launched that mix technical training with consideration of “societal” or “ethical issues,” these programs often lack a constructivist element, leaving newly-minted practitioners entering practical fields ill-equipped to unpack the politics of knowledge (...) and technology or engage with skeptical publics. This paper presents a novel format for designing interdisciplinary coursework that combines conceptual content from STS with training in engineering and policy. Courses following this format would ideally be team taught by instructors with advanced training in diverse fields, and hence co-learning between instructors and disciplines is a key element of the format. Several instruments for facilitating both student and instructor collaborative learning are introduced. The format is also designed for versatility: in addition to being adaptable to both technical and policy training environments, topics are modularized around a conceptual core so that issues ranging from biotech to nuclear security can be incorporated to fit programmatic needs and resources. (shrink)

In their recent book, Ladyman and Wiesner (What is a complex system?, Yale University Press, 2020) delineate the bounds of the exciting interdisciplinary field of complexity science. In this work, they provide examples of generally accepted complex systems and common features which these possess to varying degrees. In this paper, I plan to extend their list to include the formal study of natural language, i.e. linguistics. In fact, I will argue that language exhibits many of the hallmarks of a complex (...) system, specifically a complex biological system. Thus, my aim is to advocate contra the the ‘Minimalist Program’ (Chomsky, The minimalist program, MIT Press, Cambridge, 1995), which motivates simple underlying mechanisms (i.e. Merge) in their idealisations, that biolinguistics should embrace a ‘Maximalist Program’ in which multiple subfields contribute component explanations to an emerging whole. (shrink)

Los conceptos de evolución y cambio evolutivo son a la vez laxos y polisémicos: se aplican a fenómenos muy diferentes y no siempre se definen con precisión. La aplicación extendida del paradigma neo-darwinista clásico, además, suele imponer un esquema adaptacionista al análisis de los hechos evolutivos, en el cual las funciones juegan un papel lógicamente anterior a los rasgos y sus formas. En este artículo proponemos, como etapa previa a la formulación de hipótesis sobre las causas del cambio, un modelo (...) de espacio forma-función que pueda responder a preguntas como cuándo se da un cambio en los rasgos de una arquitectura biológica y de qué tipo son estos cambios. Un modelo de este tipo nos permite ver claramente cuándo se da un cambio en la forma o en la función y qué alcance evolutivo tiene. (shrink)

A growing body of literature focuses on practice as a central aspect of teacher education. Whereas this approach emerged mainly from teacher preparation programs in specific content areas such as math, science, and literacy studies, socially related educational fields have served as a peripheral player alone. Recently, however, scholars have suggested incorporating a practice-based approach to teacher education into the social studies. In this article, we draw on complexity theory to reexamine this proposal, evaluating the connections between teaching practices and (...) democratic citizenship. First, we introduce complexity theory. Second, we offer a critical examination of key publications discussing practice-based teacher education, using the components of complexity theory as an organizing scheme. We then highlight practice-based teacher education’s reductive aspects, leading to an argument that promoting democratic citizenship through civics teacher education should be seen as a complex endeavor where borders are blurred, interactions are nonlinear, and reality changes frequently. Finally, we offer to supplement the practice approach with aspects of teacher education that stem from complexity theory. (shrink)

Fleur Johns argues that the contraposition of a ‘bottom-up’ approach of politics of prototypical technique rather than the ‘top-down’ politics of the master plan or normative principle no longer seems as straightforwardly radical as it appeared when James C Scott posited the value of local knowledge or métis against grand plans of high modernization, just over 20 years ago. This paper seeks to follow Johns’ call, ‘to capture and probe some of the effects of sensibility, rationality or style widely reproduced (...) in the details of development work’. It draws upon fieldwork in Nairobi to open up a discussion of a shift in sensibility from a ‘bottom-up’ or ‘postliberal’ approach to a framing of open-ended encounter. The paper critiques this imaginary of relational encounter by engaging contemporary work in critical black studies. It suggests that the problem of critique is that it reproduces the problems of governing imaginaries, continually seeking to rework the human subject via adaptive capacities, sensitivities to difference and openness to alterity, while leaving intact the coloniality of being, the antiblack world. (shrink)

The book’s core argument is that an artificial intelligence that could equal or exceed human intelligence—sometimes called artificial general intelligence (AGI)—is for mathematical reasons impossible. It offers two specific reasons for this claim: Human intelligence is a capability of a complex dynamic system—the human brain and central nervous system. Systems of this sort cannot be modelled mathematically in a way that allows them to operate inside a computer. In supporting their claim, the authors, Jobst Landgrebe and Barry Smith, marshal evidence (...) from mathematics, physics, computer science, philosophy, linguistics, and biology, setting up their book around three central questions: What are the essential marks of human intelligence? What is it that researchers try to do when they attempt to achieve "artificial intelligence" (AI)? And why, after more than 50 years, are our most common interactions with AI, for example with our bank’s computers, still so unsatisfactory? Landgrebe and Smith show how a widespread fear about AI’s potential to bring about radical changes in the nature of human beings and in the human social order is founded on an error. There is still, as they demonstrate in a final chapter, a great deal that AI can achieve which will benefit humanity. But these benefits will be achieved without the aid of systems that are more powerful than humans, which are as impossible as AI systems that are intrinsically "evil" or able to "will" a takeover of human society. (shrink)

This dissertation argues that cognition is a kind of natural agency. Natural agency is the capacity that certain systems have to act in accordance with their own norms. Natural agents are systems that bias their repertoires in response to affordances in the pursuit of their goals. Cognition is a special mode of this general phenomenon. Cognitive systems are agents that have the additional capacity to actively take their worlds to be certain ways, regardless of whether the world is really that (...) way. In this way, cognitive systems are desituated. Desituatedness is the root of specifically cognitive capacities for representation and abstraction. There are two main reasons why this view needs defending. First, natural agency is typically viewed as incompatible with natural science because it is committed to a teleological mode of explanation. Second, cognition is typically held to be categorically distinct from natural agency. This dissertation argues against both of these views. It argues against the incompatibility of agency and natural science by demonstrating that systems biology, general systems theory, and sciences that deal with complex systems have typically underappreciated conceptual and theoretical resources for grounding agency in the causal structure of the world. These conceptual resources do not, however, reduce agency to systems theory because the normativity inherent in agency demands descriptive resources beyond those of even the most sophisticated systems theory. It argues against the categorical difference between natural agency and cognition by pointing out that separating cognition from a richer web of situated, ecologically embedded relations between the agent and the world generates the frame problem, which is an insuperable obstacle to making cognition that is sufficiently responsive to the complexity of the world. Rooting cognition in natural agency is a more robust empirical bet for theorizing cognition and artificial intelligence. (shrink)

The modern social world is unjust. It is also complex. What does this latter fact imply about the kind of approach that should be used in ameliorating the injustice expressed in the former fact? One answer, recently put forth by Jacob Barrett, is that _ideal theory_, which he understands as being fundamentally defined by the identification and subsequent pursuit of an aspirational macro-level institutional goal, lacks a place in social reform. The reason he thinks ideal theory lacks a place has (...) to do with its inability to deal with complexity. _Pace_ Barrett, I argue that ideal theory, suitably understood, can play a valuable role in social reform in a complex world. While some ideal theorists have underappreciated the extent to which complexity considerations complicate social reform, this does not mean that there is no place for it. This paper surveys the resources ideal theory has available to cope with what I call _the challenge of complexity_. Although the coping techniques will be successful to varying degrees, I believe, in aggregate, a compelling case can be made that ideal theory can combat the challenge of complexity. Still, one may worry that even if ideal theory can adequately deal with complexity, it cannot adequately deal with ever-changing social circumstances. Call this _the dancing landscape objection_. A static ideal theory cannot overcome it, but a _dynamic ideal theory_ could. In sum, dynamic ideal theory can contribute to social reform in a complex and mutable social world. (shrink)

The proliferation of digital data has been the impetus for the emergence of a new discipline for the study of social life: ‘computational social science’. Much research in this field is founded on the premise that society is a complex system with emergent structures that can be modeled or reconstructed through digital data. This paper suggests that computational social science serves practical and legitimizing functions for digital capitalism in much the same way that neoclassical economics does for neoliberalism. In recognition (...) of this homology, this paper develops a critique of the complexity perspective of computational social science and argues for a heterodox computational social science founded on the meta-theory of critical realism that is critical, methodological pluralist, interpretative and explanative. This implies diverting computational social science’ computational methods and digital data so as to not be aimed at identifying invariant laws of social life, or optimizing state and corporate practices, but to instead be used as part of broader research strategies to identify contingent patterns, develop conjunctural explanations, and propose qualitatively different ways of organizing social life. (shrink)

ABSTRACT In this article, I undertake a critical interrogation of the complex relations of control operating in the contemporary workplace of the knowledge worker by drawing on Foucault’s theorisation of power and resistance. I plot the risks to which the knowledge worker are exposed, the conditions of possibility as well as the challenges that complicate productive resistance in the workplace. In the process, I make use of an array of existing scholarly research that utilises the Foucauldian framework of the relationality (...) of power and develops some Foucauldian concepts further and applies them to our present context. (shrink)

Reduction is a concept first introduced by Descartes in explaining his view of the rationalization of philosophy through mathematics. He seeks to consider length, breadth, and depth for phenomena so that reducing the phenomenon to his own analytical geometric apparatus; thus shrinking the whole world into a small machine. In the present study, the authors took into account the deficiency in defining the reduction of phenomena to a mathematically sound system as the reason for a large group of problems and (...) therefore they came to redefine the Cartesian reductionism of phenomena by removing the search space through a learning system. In due definition, it is possible to reduce the NP problems to P space without using a quantum algorithm that requires a quantum computer to exist. The present study points out that the problems arising from the mathematical modeling of the Covid-19 pandemic are due to a deficiency in the definition of Cartesian reduction, which leads to an increase in the computational complexity of its diagnosis and treatment using computational tools. (shrink)