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An introduction to cybernetics

New York,: J. Wiley (1956)

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  1. Transactional economics: John Dewey's ways of knowing and the radical subjectivism of the austrian school.Robert Mulligan - 2006 - Education and Culture 22 (2):61-82.
    The subjectivism of the Austrian school of economics is a special case of Dewey's transactional philosophy, also known as pragmatism or pragmatic epistemology. The Austrian economists Carl Friedrich Menger (1840-1921) and Ludwig von Mises (1881-1973) adopted an Aristotelian deductive approach to economic issues such as social behavior and exchange. Like Menger and Mises, Friedrich A. Hayek (1899-1992) viewed scientific knowledge, even in the social sciences, as asserting and aiming for objective certainty. Hayek was particularly critical of attempts to apply the (...)
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  • A total process approach to perception.Maxine Morphis - 1986 - Behavioral and Brain Sciences 9 (1):150-151.
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  • The infinite regress of optimization.Philippe Mongin - 1991 - Behavioral and Brain Sciences 14 (2):229-230.
    A comment on Paul Schoemaker's target article in Behavioral and Brain Sciences, 14 (1991), p. 205-215, "The Quest for Optimality: A Positive Heuristic of Science?" (https://doi.org/10.1017/S0140525X00066140). This comment argues that the optimizing model of decision leads to an infinite regress, once internal costs of decision (i.e., information and computation costs) are duly taken into account.
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  • The emergence of dynamical complexity: An exploration using elementary cellular automata.Eduardo Mizraji - 2004 - Complexity 9 (6):33-42.
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  • Representing the holocaust: Ideology, ethics, and the theory of multilevel systems.Andre Mineau - 1997 - The European Legacy 2 (5):841-848.
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  • Two dynamic criteria for validating claims of optimality.Geoffrey F. Miller - 1991 - Behavioral and Brain Sciences 14 (2):228-229.
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  • Complexity and optimality.Dauglas A. Miller & Steven W. Zucker - 1991 - Behavioral and Brain Sciences 14 (2):227-228.
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  • Re-framing systemic paradigms for the art of learning.Donald McNeil - 1996 - World Futures 46 (1):23-45.
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  • Straining the word “optimal”.James E. Mazur - 1991 - Behavioral and Brain Sciences 14 (2):227-227.
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  • Reproduzierbarkeit Von ergebnissen vs. heuristischer gehalt wissenschaftlicher konzepte.Bernd Martens - 1986 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 17 (2):256-264.
    By means of an ecological and system theoretic example it is argued, that the methodological principle of repeatable effects is only partially valid. In 1970 a conjecture of system's properties was published which was refuted later, but further research was initiated in different fields, just because it referred to a non-repeatable "false" result. The heuristic content of a concept seemed to be more important than a successful replication of an experiment. The case study exhibits the restricted validity of general methodological (...)
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  • Cognitive science and the pragmatics of behavior.Lawrence E. Marks - 1986 - Behavioral and Brain Sciences 9 (1):150-150.
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  • Coding Elementary Contributions to Dialogue: Individual Acts versus Dialogical Interactions.Ivana Marková & Per Linell - 1996 - Journal for the Theory of Social Behaviour 26 (4):353-373.
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  • Converging Technologies: A Critical Analysis of Cognitive Enhancement for Public Policy Application. [REVIEW]Christos Makridis - 2013 - Science and Engineering Ethics 19 (3):1017-1038.
    This paper investigates cognitive enhancement, specifically biological cognitive enhancement (BCE), as a converging technology, and its implications for public policy. With an increasing rate of technological advancements, the legal, social, and economic frameworks lag behind the scientific advancements that they support. This lag poses significant challenges for policymakers if it is not dealt with sufficiently within the right analytical context. Therefore, the driving question behind this paper is, “What contingencies inform the advancement of biological cognitive enhancement, and what would society (...)
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  • Intrinsic versus contrived intentionality.Donald M. MacKay - 1986 - Behavioral and Brain Sciences 9 (1):149-150.
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  • The relationship between information theory, statistical mechanics, evolutionary theory, and cognitive Science.Michael Leyton - 1986 - Behavioral and Brain Sciences 9 (1):148-149.
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  • The example of psychology: Optimism, not optimality.Daniel S. Levine - 1991 - Behavioral and Brain Sciences 14 (2):225-226.
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  • Semantic information: Inference rules + memory.Michael Lebowitz - 1986 - Behavioral and Brain Sciences 9 (1):147-148.
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  • Why optimality is not worth arguing about.Stephen E. G. Lea - 1991 - Behavioral and Brain Sciences 14 (2):225-225.
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  • Natural science, social science and optimality.Oleg Larichev - 1991 - Behavioral and Brain Sciences 14 (2):224-225.
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  • Two metaphors for neural afference and efference.Peter N. Kugler & M. T. Turvey - 1979 - Behavioral and Brain Sciences 2 (2):305-307.
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  • Information, causality, and intentionality.David Kelley - 1986 - Behavioral and Brain Sciences 9 (1):147-147.
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  • The environmentally sustainable organization (ESO): A systems approach.Asterios G. Kefalas - 2001 - Ethics and the Environment 6 (2):90-105.
    : Few concepts have created more sound and fury than the concepts of development and environment. The difficulty associated with these concepts increases exponentially when one attempts to clarify them by adding some attributes such as concrete definitions and measurements pertaining to the quantity and quality of these concepts. This essay deals with the private, for-profit corporation as the primary agent in the process of satisfying the human struggle for survival. This agent has been the epicenter of the "development-environment" issue (...)
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  • Cause and effect in evolution.Michael J. Katz - 1984 - Behavioral and Brain Sciences 7 (4):492-492.
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  • Second-Order Recursions of First-Order Cybernetics: An “Experimental Epistemology”.Won Jeon - 2022 - Open Philosophy 5 (1):381-395.
    This article examines central tensions in cybernetics, defined as the study of self-organization, communication, automated feedback in organisms, and other distributed informational networks, from its wartime beginnings to its contemporary adaptations. By examining aspects of both first- and second-order cybernetics, the article introduces an epistemological standpoint that highlights the tension between its definition as a theory of recursion and a theory of control, prediction, and actionability. I begin by examining the historical outcomes of the Macy Conferences to provide a context (...)
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  • Types of optimality: Who is the steersman?Michael E. Hyland - 1991 - Behavioral and Brain Sciences 14 (2):223-224.
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  • Beyond Harmony and Consensus: A Social Conflict Approach to Technology.Mikael Hård - 1993 - Science, Technology and Human Values 18 (4):408-432.
    This article presents a sociological perspective that suggests that technology should be seen as a means for groups to retain or rearrange social relations. Claiming, first, that the sociotechnical systems approach in technology-and-society studies often tend to bring out harmony and cooperation as an ideal and, second, that central social construc tivists tend to interpret closure and stabilization processes in terms of consensus, this article, instead, argues that technology should be regarded as the outcome of conflicting interests and ideas. To (...)
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  • On the stabilization of behavioral selection.Werner K. Honig - 1984 - Behavioral and Brain Sciences 7 (4):491-492.
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  • Foundations and methodology for an evolutionary world view: A review of the principia cybernetica project. [REVIEW]Francis Heylighen - 2000 - Foundations of Science 5 (4):457-490.
    The Principia Cybernetica Project was created to develop an integrated philosophy or world view, based on the theories of evolution, self-organization, systems and cybernetics. Its conceptual network has been implemented as an extensive website. The present paper reviews the assumptions behind the project, focusing on its rationale, its philosophical presuppositions, and its concrete methodology for computer-supported collaborative development. Principia Cybernetica starts from a process ontology, where a sequence of elementary actions produces ever more complex forms of organization through the mechanism (...)
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  • Optimality and constraint.David A. Helweg & Herbert L. Roitblat - 1991 - Behavioral and Brain Sciences 14 (2):222-223.
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  • Intentionality and the explanation of behavior.John Heil - 1986 - Behavioral and Brain Sciences 9 (1):146-147.
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  • Group and individual effects in selection.Marvin Harris - 1984 - Behavioral and Brain Sciences 7 (4):490-491.
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  • Fitting culture into a Skinner box.C. R. Hallpike - 1984 - Behavioral and Brain Sciences 7 (4):489-490.
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  • Uncertainty about information.Ian E. Gordon - 1986 - Behavioral and Brain Sciences 9 (1):146-146.
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  • On some specific models of intentional behavior.Richard M. Golden - 1986 - Behavioral and Brain Sciences 9 (1):144-145.
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  • The Myth of Responsibility: on Changing the Purpose Paradigm.Friedrich Glauner - 2019 - Humanistic Management Journal 4 (1):5-32.
    As part of our exploration of a new conceptual framework for an economy that works for 100% of humanity, this conceptual paper asks why all talk about the purpose of organizations seems to suffer from a certain bias, namely the bias of scarcity, and how this myth of scarcity influences our understanding of corporate responsibility. The mainstream understanding of corporate purpose always contains partly normative and partly functional aspects designed to cope with the purported problem of scarcity. According to economic (...)
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  • The emancipation of thought and culture from their original material substrates.Michael T. Ghiselin - 1984 - Behavioral and Brain Sciences 7 (4):489-489.
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  • The sigma profile: A formal tool to study organization and its evolution at multiple scales.Carlos Gershenson - 2011 - Complexity 16 (5):37-44.
    The σ profile is presented as a tool to analyze the organization of systems at different scales, and how this organization changes in time. Describing structures at different scales as goal‐oriented agents, one can define σ ∈ [0,1] (satisfaction) as the degree to which the goals of each agent at each scale have been met. σ reflects the organization degree at that scale. The σ profile of a system shows the satisfaction at different scales, with the possibility to study their (...)
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  • Self‐organizing traffic lights at multiple‐street intersections.Carlos Gershenson & David A. Rosenblueth - 2012 - Complexity 17 (4):23-39.
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  • The wider context of selection by consequences.Thomas J. Gamble - 1984 - Behavioral and Brain Sciences 7 (4):488-489.
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  • Human‐computer interaction: A critical synthesis.Chris Fields - 1987 - Social Epistemology 1 (1):5 – 25.
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  • Information is in the eye of the beholder.Rhea T. Eskew - 1986 - Behavioral and Brain Sciences 9 (1):144-144.
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  • Intentionality and information theory.David P. Ellerman - 1986 - Behavioral and Brain Sciences 9 (1):143-144.
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  • Stalking intentionality.Fred I. Dretske - 1986 - Behavioral and Brain Sciences 9 (1):142-143.
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  • Skinner – The Darwin of ontogeny?John W. Donahoe - 1984 - Behavioral and Brain Sciences 7 (4):487-488.
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  • Significance of Models of Computation, from Turing Model to Natural Computation.Gordana Dodig-Crnkovic - 2011 - Minds and Machines 21 (2):301-322.
    The increased interactivity and connectivity of computational devices along with the spreading of computational tools and computational thinking across the fields, has changed our understanding of the nature of computing. In the course of this development computing models have been extended from the initial abstract symbol manipulating mechanisms of stand-alone, discrete sequential machines, to the models of natural computing in the physical world, generally concurrent asynchronous processes capable of modelling living systems, their informational structures and dynamics on both symbolic and (...)
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  • Engineering's baby.Daniel C. Dennett - 1986 - Behavioral and Brain Sciences 9 (1):141-142.
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  • Sketch for a Theory of Evolution Based on Coding.Joachim De Beule - 2014 - Biosemiotics 7 (2):181-201.
    At the heart of evolutionary theory lays the notion of replication. Unfortunately, this notion is far less exact than the weight of its importance. In this paper, it is argued that replication always involves coding. Furthermore, when a theory of evolution is built on replication based on coding, a unifying and coherent picture arises that sheds new light on some of the controversies and open questions in contemporary biology, such as what are the roles of phylogeny and ontogeny in evolution, (...)
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  • Vaulting optimality.Peter Dayan & Jon Oberlander - 1991 - Behavioral and Brain Sciences 14 (2):221-222.
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  • Replicators, consequences, and displacement activities.Richard Dawkins - 1984 - Behavioral and Brain Sciences 7 (4):486-487.
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  • Organisms, scientists and optimality.Michael Davison - 1991 - Behavioral and Brain Sciences 14 (2):220-221.
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