This book discusses how scientific and other types of cognition make use of models, abduction, and explanatory reasoning in order to produce important or creative changes in theories and concepts. It includes revised contributions presented during the international conference on Model-Based Reasoning (MBR’015), held on June 25-27 in Sestri Levante, Italy. The book is divided into three main parts, the first of which focuses on models, reasoning and representation. It highlights key theoretical concepts from an applied perspective, addressing issues concerning (...) information visualization, experimental methods and design. The second part goes a step further, examining abduction, problem solving and reasoning. The respective contributions analyze different types of reasoning, discussing various concepts of inference and creativity and their relationship with experimental data. In turn, the third part reports on a number of historical, epistemological and technological issues. By analyzing possible contradictions in modern research and describing representative case studies in experimental research, this part aims at fostering new discussions and stimulating new ideas. All in all, the book provides researchers and graduate students in the field of applied philosophy, epistemology, cognitive science and artificial intelligence alike with an authoritative snapshot of current theories and applications of model-based reasoning. (shrink)

John Dewey attempted a pragmatic aufhebung of the disparate methodological aims of social science-explanation, understanding, and critique- in his 1938 Logic: the theory of Inquiry. There, in his penultimate chapter ‘Social Inquiry’, Dewey performed a trademark implementation of his deflation of absolutistic and universalistic pretensions in intellectual and theoretical discourse, in this case with respect to any one approach to social science. This deflation--as elsewhere in his analogous treatments of epistemology, ethics, and the theory of action-- involved the (...) reconstruction of the claims of the naturalist, interpretivist, and critical schools of social science into one overall pattern of social inquiry. This recasts the different and seemingly irreconcilable aims of these schools into a series of steps in a practice. That these claims, then, simultaneously stand independently but in varying degrees of tension with, and support of, each other is a hallmark of pragmatism’s embrace of pluralism in intelligent problem solving. Dewey’s discussion of interpretation needs supplementation from his broader philosophical commitments in order to see the full sense of both the compatibility and the incompatibility of his theory with philosophical hermeneutics. (shrink)

It has been argued that our currently most satisfactory social epistemology of science can’t account for science that is based on artificial intelligence (AI) because this social epistemology requires trust between scientists that can take full responsibility for the research tools they use, and scientists can’t take full responsibility for the AI tools they use since these systems are epistemically opaque. I think this argument overlooks that much AI-based science can be done without opaque models, and that (...) agents can take full responsibility for the systems they use even if these systems are opaque. Requiring that an agent fully understand how a system works is an untenably strong condition for that agent to take full responsibility for the system and risks absolving AI developers from responsibility for their products. AI-based science need not create trust-related social epistemological problems if we keep in mind that what makes both individual scientists and their use of AI systems trustworthy isn’t full transparency of the internal processing but their adherence to social and institutional norms that ensure that scientific claims can be trusted. (shrink)

The relevance of this study's topic is supported by the argument that a philosophical understanding of the fundamental concepts of epistemology as they pertain to the educational process is crucial as the educational setting becomes increasingly digitalised. This paper aims to explore the epistemological component of the philosophy of learning in light of the educational process digitalisation. The research comprised a sample of 462 university students from Kazakhstan, with 227 participants assigned to the experimental and 235 to the control groups. (...) The research employed a scientific methodology that encompassed various scientific methods, including sociological, socio-philosophical, and culturological analysis. In addition, philosophical methods such as system analysis, structural and functional analysis, and comparative and hermeneutical approaches were utilised for the philosophical and epistemological interpretation of the cognitive activity mechanism of students and its dynamics in light of digitalising education. Furthermore, psychological diagnostic methods such as SAMOAL personality self-actualisation diagnostics, personality volitional potential diagnostics, and psychodiagnostic methods were employed. This study offers a theoretical rationale for the integration of digital technology in the realm of education, grounded in philosophical and epistemological principles. In addition, it challenges the null hypothesis that the philosophical and epistemological framework of the educational process does not influence its cognitive and personal aspects. Digital learning has been found to improve both cognitive and personal aspects of the learning process. All indicators of self-realization increased statistically significantly (p 0.05). The positive impact of digital technologies on increasing the communicative competence of students can be considered as a factor in mitigating epistemic risks in the learning process.The findings can be used to avoid epistemological risks and improve learning effectiveness in a digital learning environment. There are plans to investigate the gender dimension of digital learning epistemology in the future. (shrink)

Problem solving has recently become a central topic both in the philosophy of science and in cognitive science. This paper integrates approaches to problem solving from these two disciplines and discusses the epistemological consequences of such an integration. The paper first analyzes problem solving as getting a true answer to a question. It then explores some stages of cognitive activity relevant to question answering that have been delineated by historians and philosophers of (...) science and by cognitive psychologists and artificial intelligencers. The traditional opposition between discovery and justification is challenged. It is suggested that epistemology may be conceptualized, in part, as the critical assessment of problem-solving strategies. (shrink)

Nowadays, new directions for the development of artificial intelligence (AI) have emerged, the task has been set to develop artificial general intelligence (AGI), which is able to go beyond the narrow AI, gain a high degree of autonomy, independently solve problems in different environmental conditions and thus have the ability to perform the functions of natural intelligence. In this regard, important philosophical, theoretical, and methodological questions arise concerning the definition and evaluation of the social significance of new AI achievements, especially (...) regarding the correlation of their socio-humanitarian and technological aspects. It is necessary to overcome a purely technocratic approach, which usually ignores the negative consequences of digitalization, the possible risks and threats of AI development. These difficulties are conditioned by the paradigm gap between the system of concepts used to describe technologies and the systems of concepts specific to socio-humanitarian descriptions and explanations. The so-called ontological paradox arises during digital transformation and the introduction of AI into social systems. An ontological paradox is also an epistemological paradox, since any statement of an ontological type presupposes its epistemological justification. To overcome this gap, V.E. Lepskiy proposes the conception of self-developing polysubjective environments – cybernetics of the third order. This conception allows to create a conceptual “bridge” between two systems of definitions that do not have direct logical connections between them. A productive tool for this can be the information approach, which is widely used to solve such interdisciplinary problems. This makes possible to theoretically correctly connect technological and socio-humanitarian descriptions in a single conceptual structure and to analyze main socio-humanitarian criteria for evaluating digital innovations. The article discusses in detail the main provisions of the information approach and its application for the development of AGI systems, the socio-humanitarian assessment of AI autonomous development, and the resolution of security issues. (shrink)

Forensic science and its influence on policing and the criminal justice system have increased since the beginning of the twentieth century. While the philosophies of the forensic science pioneers remain the pillar of modern practice, rapid advances in technology and the underpinning sciences have seen an explosion in the number of disciplines and tools. Consequently, the way in which we exploit and interpret the remnant of criminal activity are adapting to this changing environment. In order to (...) best exploit the trace, an interdisciplinary approach to both research and investigation is required. In this paper, nine postdoctoral research fellows from a multidisciplinary team discuss their vision for the future of forensic science at the crime scene, in the laboratory and beyond. This paper does not pretend to be exhaustive of all fields of forensic science, but describes a portion of the postdoctoral fellows’ interests and skills. (shrink)

A problem is a situation in which an agent seeks to attain a given goal without knowing how to achieve it. Human problem solving is typically studied as a search in a problem space composed of states (information about the environment) and operators (to move between states). A problem such as playing a game of chess has possible states, and a traveling salesperson problem with as little as 82 cities already has more than different (...) tours (similar to chess). Biological neurons are slower than the digital switches in computers. An exhaustive search of the problem space exceeds the capacity of current computers for most interesting problems, and it is fairly clear that humans cannot in their lifetime exhaustively search even small fractions of these problem spaces. Yet, humans play chess and solve logistical problems of similar complexity on a daily basis. Even for simple problems humans do not typically engage in exploring even a small fraction of the problem space. This begs the question: How do humans solve problems on a daily basis in a fast and efficient way? Recent work suggests that humans build a problem representation and solve the represented problem—not the problem that is out there. The problem representation that is built and the process used to solve it are constrained by limits of cognitive capacity and a cost–benefit analysis discounting effort and reward. In this article, we argue that better understanding the way humans represent and solve problems using heuristics can help inform how simpler algorithms and representations can be used in artificial intelligence to lower computational complexity, reduce computation time, and facilitate real-time computation in complex problem solving. (shrink)

The paper analysis the strategies and concepts developed in the world in modern directions: innova- tive economy, digital economy, artificial intelligence, Industry 4.0 and others. The problem is to determine the initial fundamental parameters of order and their prospects in the global world, the definition and principles of artificial intel- ligence systems, its structure and important aspects and principles of future science and technology in analysis and synthesis based on synergetic approaches, innovative, information, converged technologies, taking into account (...) the design of future and viable and safe and sustainable development in the context of Industry 4.0, digital economy and Society 5.0. The general scheme of the integrated model of sustainable and socio-humanitarian development in the global system is proposed, which the authors call the noosphere model of sustainable and safe development as an important fundamental platform for the design and creation of AIS. In the XXI century education, science and innova- tive technologies becomes the «basis» of the spiritual and material reproduction of man, transforming the society of the XXI century into an educational society, ensures compliance with the law of advanced development of human quality, quality of social intelligence and quality of educational systems in society. noosphere evolution. Industry 4.0 is the next stage in the digitalization of production technologies with the accelerated introduction of new developments and cyberphysical systems, which is changing business models. Accordingly, Industry 4.0 as a separate segment of digital manufacturing technologies takes its place among other digital sectors. The analysis of the Strategies and Concepts developed in the world in modern directions: innovative economy, digital economy, artificial intelli- gence, Industry 4.0 and others is carried out in the work. The problem is to determine the initial fundamental para- meters of order and their prospects in the global world, the definition and principles of artificial intelligence systems, its structure and important aspects and principles of future science and technology in analysis and synthesis ba- sed on synergetic approaches, innovative, information, converged technologies, taking into account the design of future and viable and safe and sustainable development in the context of Industry 4.0, DE and Society 5.0. The main scientific and technological factors in the 21st century will be the development of: Artificial Intelligence, Nano Technologies, Bio-Media Technologies, Cognitive Technologies and Socio-Humanitarian Technologies. More specifically – this is the modern development of Innovative Technologies: «NBIC SH». Therefore, it is necessary to note the importance of taking into account the principle of I2K2+SH, ie: Intellectualization, Integration, Convergence, Coevolution and Socio- humanitarian technologies. It is important and necessary to take into account modern principles of designing a su- stainable and secure AIS and solving the problem of Harmonization between 2 worlds: Real and Virtual, especially in their hybridization. Our immediate future is a hybrid nonlinear world. Note that digitalization, artificial intelligence and robotics will be the main drivers of global economic growth by 2030E. An important paradigm and platform is the Integrated System: for Industry 4.0, DE and AI, taking into account the complex principle of I2K2+SH. If the transition of the Ukrainian economy to an innovative one does not take place in the next 3-5 years, Ukraine will remain in the backyard of civilization. (shrink)

PLEASE NOTE: This is the corrected 2nd eBook edition, 2021. ●●●●● _Critique of Impure Reason_ has now also been published in a printed edition. To reduce the otherwise high price of this scholarly, technical book of nearly 900 pages and make it more widely available beyond university libraries to individual readers, the non-profit publisher and the author have agreed to issue the printed edition at cost. ●●●●● The printed edition was released on September 1, 2021 and is now available through (...) all booksellers, including Barnes & Noble, Amazon, and brick-and-mortar bookstores under ISBN 978-0-578-88646-6. ●●●●● In light of the length of this book, readers who would like to have a more detailed description of the book's objectives and method may find it helpful to read the detailed and clearly written Wikipedia entry about this work: From the Wikipedia search page, use the search phrase "Critique of Impure Reason". At least at the time of this writing (11/29/2021), the Wikipedia entry is well-researched and accurate. ●●●●● In addition, a "Primer on Bartlett's CRITIQUE OF IMPURE REASON" has been made available by the author. It is available under its title through PhilPapers and other philosophy online archives. ●●●●● COMMENDATIONS OF THIS WORK, from the back cover of the published edition: ●●●●● “I admire its range of philosophical vision.” – Nicholas Rescher, Distinguished University Professor of Philosophy, University of Pittsburgh, author of more than 100 books. ●●●●● “Bartlett’s _Critique of Impure Reason_ is an impressive, bold, and ambitious work. Careful scholarship is balanced by original analyses that lead the reader to recognize the limits of meaning, knowledge, and conceptual possibility. The work addresses a host of traditional philosophical problems, among them the nature of space, time, causality, consciousness, the self, other minds, ontology, free will and determinism, and others. The book culminates in a fascinating and profound new understanding of relativity physics and quantum theory.” – Gerhard Preyer, Professor of Philosophy, Goethe-University, Frankfurt am Main, Germany, author of many books including _Concepts of Meaning_, _Beyond Semantics and Pragmatics_, _Intention and Practical Thought_, and _Contextualism in Philosophy_. ●●●●● “[This work’s] goal is of a unique and difficult species: Dr. Bartlett seeks to develop a formal logical calculus on the basis of transcendental philosophical arguments; in fact, he hopes that this calculus will be the formal expression of the transcendental foundation of knowledge.... I consider Dr. Bartlett’s work soundly conceived and executed with great skill.” – C. F. von Weizsäcker, philosopher and physicist, former Director, Max-Planck-Institute, Starnberg, Germany. ●●●●● “Bartlett has written an American “Prolegomena to All Future Metaphysics.” He aims rigorously to eliminate meaningless assertions, reach bedrock, and place philosophy on a firm foundation that will enable it, like science and mathematics, to produce lasting results that generations to come can build on. This is a great book, the fruit of a lifetime of research and reflection, and it deserves serious attention.” — Martin X. Moleski, former Professor, Canisius College, Buffalo, NY, studies of scientific method, the presuppositions of thought, and the self-referential nature of epistemology. ●●●●● “Bartlett has written a book on what might be called the underpinnings of philosophy. It has fascinating depth and breadth, and is all the more striking due to its unifying perspective based on the concepts of reference and self-reference.” – Don Perlis, Professor of Computer Science, University of Maryland, author of numerous publications on self-adjusting autonomous systems and philosophical issues concerning self-reference, mind, and consciousness. ●●●●● ●●●●● The _Critique of Impure Reason: Horizons of Possibility and Meaning_ comprises a major and important contribution to philosophy. Thanks to the generosity of its publisher, this massive 885-page volume has been published as a free open access eBook (3.75MB) as well as an open access printed edition. It inaugurates a revolutionary paradigm shift in philosophical thought by providing compelling and long-sought-for solutions to a wide range of philosophical problems. In the process, the work fundamentally transforms the way in which the concepts of reference, meaning, and possibility are understood. The book includes a Foreword by the celebrated German philosopher and physicist Carl Friedrich von Weizsäcker. ●●●●● In Kant’s _Critique of Pure Reason_ we find an analysis of the preconditions of experience and of knowledge. In contrast, but yet in parallel, the new _Critique_ focuses upon the ways—unfortunately very widespread and often unselfconsciously habitual—in which many of the concepts that we employ _conflict_ with the very preconditions of meaning and of knowledge. ●●●●● This is a book about the boundaries of frameworks and about the unrecognized conceptual confusions in which we become entangled when we attempt to transgress beyond the limits of the possible and meaningful. We tend either not to recognize or not to accept that we all-too-often attempt to trespass beyond the boundaries of the frameworks that make knowledge possible and the world meaningful. ●●●●● The _Critique of Impure Reason_ proposes a bold, ground-breaking, and startling thesis: that a great many of the major philosophical problems of the past can be solved through the recognition of a viciously deceptive form of thinking to which philosophers as well as non-philosophers commonly fall victim. For the first time, the book advances and justifies the criticism that a substantial number of the questions that have occupied philosophers fall into the category of “impure reason,” violating the very conditions of their possible meaningfulness. ●●●●● The purpose of the study is twofold: first, to enable us to recognize the boundaries of what is referentially forbidden—the limits beyond which reference becomes meaningless—and second, to avoid falling victims to a certain broad class of conceptual confusions that lie at the heart of many major philosophical problems. As a consequence, the boundaries of _possible meaning_ are determined. ●●●●● Bartlett, the author or editor of more than 20 books, is responsible for identifying this widespread and delusion-inducing variety of error, _metalogical projection_. It is a previously unrecognized and insidious form of erroneous thinking that undermines its own possibility of meaning. It comes about as a result of the pervasive human compulsion to seek to transcend the limits of possible reference and meaning. ●●●●● Based on original research and rigorous analysis combined with extensive scholarship, the _Critique of Impure Reason_ develops a self-validating method that makes it possible to recognize, correct, and eliminate this major and pervasive form of fallacious thinking. In so doing, the book provides at last provable and constructive solutions to a wide range of major philosophical problems. ●●●●● CONTENTS AT A GLANCE ▪▪▪▪▪ Preface ▪▪▪▪▪ Foreword by Carl Friedrich von Weizsäcker ▪▪▪▪▪ Acknowledgments ▪▪▪▪▪ Avant-propos: A philosopher’s rallying call ▪▪▪▪▪ Introduction ▪▪▪▪▪ A note to the reader ▪▪▪▪▪ A note on conventions ▪▪▪▪▪ ▪▪▪▪▪ ▪▪▪▪▪ PART I ▪▪▪▪▪ ▪▪▪▪▪ WHY PHILOSOPHY HAS MADE NO PROGRESS AND HOW IT CAN ▪▪▪▪▪ 1 Philosophical-psychological prelude ▪▪▪▪▪ 2 Putting belief in its place: Its psychology and a needed polemic ▪▪▪▪▪ 3 Turning away from the linguistic turn: From theory of reference to metalogic of reference ▪▪▪▪▪ 4 The stepladder to maximum theoretical generality ▪▪▪▪▪ ▪▪▪▪▪ ▪▪▪▪▪ PART II ▪▪▪▪▪ THE METALOGIC OF REFERENCE ▪▪▪▪▪ A New Approach to Deductive, Transcendental Philosophy ▪▪▪▪▪ 5 Reference, identity, and identification ▪▪▪▪▪ 6 Self-referential argument and the metalogic of reference ▪▪▪▪▪ 7 Possibility theory ▪▪▪▪▪ 8 Presupposition logic, reference, and identification ▪▪▪▪▪ 9 Transcendental argumentation and the metalogic of reference ▪▪▪▪▪ 10 Framework relativity ▪▪▪▪▪ 11 The metalogic of meaning ▪▪▪▪▪ 12 The problem of putative meaning and the logic of meaninglessness ▪▪▪▪▪ 13 Projection ▪▪▪▪▪ 14 Horizons ▪▪▪▪▪ 15 De-projection ▪▪▪▪▪ 16 Self-validation ▪▪▪▪▪ 17 Rationality: Rules of admissibility ▪▪▪▪▪ ▪▪▪▪▪ ▪▪▪▪▪ PART III ▪▪▪▪▪ PHILOSOPHICAL APPLICATIONS OF THE METALOGIC OF REFERENCE ▪▪▪▪▪ Major Problems and Questions of Philosophy and the Philosophy of Science ▪▪▪▪▪ 18 Ontology and the metalogic of reference ▪▪▪▪▪ 19 Discovery or invention in general problem-solving, mathematics, and physics ▪▪▪▪▪ 20 The conceptually unreachable: “The far side” ▪▪▪▪▪ 21 The projections of the external world, things-in-themselves, other minds, realism, and idealism ▪▪▪▪▪ 22 The projections of time, space, and space-time ▪▪▪▪▪ 23 The projections of causality, determinism, and free will ▪▪▪▪▪ 24 Projections of the self and of solipsism ▪▪▪▪▪ 25 Non-relational, agentless reference and referential fields ▪▪▪▪▪ 26 Relativity physics as seen through the lens of the metalogic of reference ▪▪▪▪▪ 27 Quantum theory as seen through the lens of the metalogic of reference ▪▪▪▪▪ 28 Epistemological lessons learned from and applicable to relativity physics and quantum theory ▪▪▪▪▪ ▪▪▪▪▪ PART IV ▪▪▪▪▪ HORIZONS ▪▪▪▪▪ 29 Beyond belief ▪▪▪▪▪ 30 _Critique of Impure Reason_: Its results in retrospect ▪▪▪▪▪ ▪▪▪▪▪ SUPPLEMENT ▪▪▪▪▪ The Formal Structure of the Metalogic of Reference ▪▪▪▪▪ APPENDIX I ▪▪▪▪▪ The Concept of Horizon in the Work of Other Philosophers ▪▪▪▪▪ APPENDIX II ▪▪▪▪▪ Epistemological Intelligence ▪▪▪▪▪ References ▪▪▪▪▪ Index ▪▪▪▪▪ About the author. 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In a letter of 1932, Karl Popper described Die beiden Grundprobleme der Erkenntnistheorie – _The Two Fundamental Problems of the Theory of Knowledge_ – as ‘…a child of crises, above all of …the crisis of physics.’ Finally available in English, it is a major contribution to the philosophy of science, epistemology and twentieth century philosophy generally. The two fundamental problems of knowledge that lie at the centre of the book are the problem of induction, that although we are (...) able to observe only a limited number of particular events, science nevertheless advances unrestricted universal statements; and the problem of demarcation, which asks for a separating line between empirical science and non-science. Popper seeks to solve these two basic problems with his celebrated theory of falsifiability, arguing that the inferences made in science are not inductive but deductive; science does not start with observations and proceed to generalise them but with problems, which it attacks with bold conjectures. _The Two Fundamental Problems of the Theory of Knowledge_ is essential reading for anyone interested in Karl Popper, in the history and philosophy of science, and in the methods and theories of science itself. (shrink)

In a letter of 1932, Karl Popper described Die beiden Grundprobleme der Erkenntnistheorie – _The Two Fundamental Problems of the Theory of Knowledge_ – as ‘…a child of crises, above all of …the crisis of physics.’ Finally available in English, it is a major contribution to the philosophy of science, epistemology and twentieth century philosophy generally. The two fundamental problems of knowledge that lie at the centre of the book are the problem of induction, that although we are (...) able to observe only a limited number of particular events, science nevertheless advances unrestricted universal statements; and the problem of demarcation, which asks for a separating line between empirical science and non-science. Popper seeks to solve these two basic problems with his celebrated theory of falsifiability, arguing that the inferences made in science are not inductive but deductive; science does not start with observations and proceed to generalise them but with problems, which it attacks with bold conjectures. _The Two Fundamental Problems of the Theory of Knowledge_ is essential reading for anyone interested in Karl Popper, in the history and philosophy of science, and in the methods and theories of science itself. (shrink)

In a letter of 1932, Karl Popper described _Die beiden Grundprobleme der Erkenntnistheorie – The Two Fundamental Problems of the Theory of Knowledge_ – as ‘…a child of crises, above all of …the crisis of physics.’ Finally available in English, it is a major contribution to the philosophy of science, epistemology and twentieth century philosophy generally. The two fundamental problems of knowledge that lie at the centre of the book are the problem of induction, that although we are (...) able to observe only a limited number of particular events, science nevertheless advances unrestricted universal statements; and the problem of demarcation, which asks for a separating line between empirical science and non-science. Popper seeks to solve these two basic problems with his celebrated theory of falsifiability, arguing that the inferences made in science are not inductive but deductive; science does not start with observations and proceed to generalise them but with problems, which it attacks with bold conjectures. _The Two Fundamental Problems of the Theory of Knowledge_ is essential reading for anyone interested in Karl Popper, in the history and philosophy of science, and in the methods and theories of science itself. (shrink)

This article challenges stereotypical conceptions of Law and Science as cultural opposites, arguing that English criminal trial practice is fundamentally congruent with modern science’s basic epistemological assumptions, values and methods of inquiry. Although practical tensions undeniably exist, they are explicable—and may be neutralised—by paying closer attention to criminal adjudication’s normative ideals and their institutional expression in familiar aspects of common law trial procedure, including evidentiary rules of admissibility, trial by jury, adversarial fact-finding, cross-examination and the ethical duties of (...) expert witnesses. Effective partnerships between lawyers and forensic scientists are indispensable for integrating scientific evidence into criminal proceedings, and must be renegotiated between individual practitioners on an on-going basis. Fruitful interdisciplinary collaboration between scholars with a shared interest in forensic science should dispense with reductive cultural stereotypes of Science and Law. (shrink)

This paper briefly surveys several prominent modeling approaches to knowledge-based intelligent systems design and, especially, expert systems and the breakthroughs that have most broadened and improved their applications. We argue that the implementation of technology that aims to emulate rudimentary aspects of human intelligence has enhanced KBIS design, but that weaknesses remain that could be addressed with existing research in cognitive science. For example, we propose that systems based on representational plasticity, functional dynamism, domain specificity, creativity, and concept learning, (...) with their theoretical and experimental rigor, can best characterize the problem-solving capabilities of humans and can best overcome five key limitations currently exhibited by knowledge-based intelligent systems. We begin with a brief survey of the relevant work related to KBIS design and then discuss these five shortcomings with new suggestions for how to integrate results from cognitive science to resolve each of them. Our ultimate goal is to increase awareness and direct attention to areas of theoretical and experimental cognitive research that are fundamentally relevant to the goals underlying KBISes. (shrink)

Sign-theoretical concepts have been used in research into the nature of living systems, not only by biologists, semioticians, and philosophers, but also by scientists who analyze organisms from the perspective of Decision Theory. Decision Theory (DT) describes both the external behavior and the internal information-processing of any kind of agent in terms of problem solving. Such “problem solving” is considered a complex process of: (1) defining a goal in an environment, (2) selecting the means to reach (...) the defined goal, and (3) controlling the effects of said selected means on the environment. The hypothesis that problem-solving agents are, first, physical entities and, second, sign-using systems is one of the most influential ideas in Decision Theory. This idea has been developed under the name of the ‘Physical Symbol System Hypothesis’ (PSSH) since the 1950s, particularly by two American scientists, Allen Newell and Herbert A. Simon, who did interdisciplinary research in cognitive science, artificial intelligence, economics, and decision theory. This paper first gives a short overview of the basic semiotic theory that underlies Newell’s and Simon’s work, and then offers some ideas on a specifically biosemiotic use of the Physical SymbolSystem Hypothesis. (shrink)

The United States Supreme Court has long recognized the value of scientific evidence - especially when compared to other types of evidence such as eyewitness identifications, confessions, and informant testimony. For example, in Escobedo v. Illinois, the Court observed: “We have learned the lesson of history, ancient and modern, that a system of criminal law enforcement which comes to depend on the ‘confession’ will, in the long run, be less reliable and more subject to abuses than a system which depends (...) on extrinsic evidence independently secured through skillful investigation.” Similarly, in Davis v. Mississippi, the Court commented:Detention for fingerprinting may constitute a much less serious intrusion upon personal security than other types of police searches and detentions. Fingerprinting involves none of the probing into an individual's private life and thoughts that marks an interrogation or search. Nor can fingerprint detention be employed repeatedly to harass any individual, since the police need only one set of each person's prints. Furthermore, fingerprinting is an inherently more reliable and effective crime-solving tool than eyewitness identifications or confessions and is not subject to such abuses as the improper line-up and the “third degree.”. (shrink)

This paper focuses on the normative analysis—in the sense of the classic decision-theoretic formulation—of decision problems that arise in connection with forensic expert reporting. We distinguish this analytical account from other common types of decision analyses, such as descriptive approaches. While decision theory is, since several decades, an extensively discussed topic in legal literature, its use in forensic science is more recent, and with an emphasis on goals such as the analysis of the logical structure of (...) class='Hi'>forensic expert conclusions regarding, for example, propositions of common source of evidential and known materials. Typical examples are so-called identification decisions, especially categorical conclusions according to which fingermarks come from a particular a person of interest. We will present and compare ways of stating forensic identification decisions in decision-theoretic terms and explain their underlying rationale. In particular, we will emphasize the importance of viewing this analysis as normative in the sense of providing a reflective rather than a prescriptive reference point against which people in charge of forensic identification decisions may compare their otherwise intuitive and informal reasoning, before acting. Normative decision analysis in forensic science thus provides a vector through which current practice can be articulated, scrutinized and rethought. (shrink)

We all believe an unbounded number of things about the way the world is and about the way the world works. For example, I believe that if I move this book into the other room, it will not change color -- unless there is a paint shower on the way, unless I carry an umbrella through that shower, and so on; I believe that large red trucks at high speeds can hurt me, that trucks with polka dots can hurt me, (...) and so on; that if I move this book, the room will stay in place -- unless there is a pressure switch under the book attached to a bomb, unless the switch communicates to the bomb by radio and there is shielding in the way, and so on; that the moon is not made of green cheese, that the moon is not made of caviar, that the moon is not made of gold, and so on. The problems involved in accounting for such infinite proliferations of beliefs -- and the computations and inferences that take them into account -- are collectively called the Frame Problems, and are considered by some to constitute a major discovery of a new philosophical problem. How could we possibly learn them all? How could the brain possibly hold them all? The problems appear insoluble, impossible. Yet we all learn and hold such unbounded numbers of beliefs; in particular, children do. Something must be wrong. I wish to argue that the frame problem arises from a fundamental presupposition about the nature of representation -- a false presupposition. Yet, it is a presupposition that dominates contemporary developmental psychology (and psychology more broadly, and cognitive science, artificial intelligence, philosophy of mind, and so on). In particular, I will offer an alternative model of the nature of representation within which the frame problem does not arise -- within which such unboundedness is natural. (shrink)

Originally published as Scientific Research, this pair of volumes constitutes a fundamental treatise on the strategy of science. Mario Bunge, one of the major figures of the century in the development of a scientific epistemology, describes and analyzes scientific philosophy, as well as discloses its philosophical presuppositions. This work may be used as a map to identify the various stages in the road to scientific knowledge. Philosophy of Science is divided into two volumes, each with two parts. Part (...) 1 offers a preview of the scheme of science and the logical and semantical took that will be used throughout the work. The account of scientific research begins with part 2, where Bunge discusses formulating the problem to be solved, hypothesis, scientific law, and theory. The second volume opens with part 3, which deals with the application of theories to explanation, prediction, and action. This section is graced by an outstanding discussion of the philosophy of technology. Part 4 begins with measurement and experiment. It then examines risks in jumping to conclusions from data to hypotheses as well as the converse procedure. Bunge begins this mammoth work with a section entitled "How to Use This Book." He writes that it is intended for both independent reading and reference as well as for use in courses on scientific method and the philosophy of science. It suits a variety of purposes from introductory to advanced levels. Philosophy of Science is a versatile, informative, and useful text that will benefit professors, researchers, and students in a variety of disciplines, ranging from the behavioral and biological sciences to the physical sciences. (shrink)

Every time we think ahead, we are crafting a story. Every daily plan—and every political vision, social movement, scientific hypothesis, business proposal, and technological breakthrough—starts with “what if?” Linking causes to effects, considering hypotheticals and counterfactuals, asking how other people will react: these are the essence of narrative. So why do we keep overlooking story’s importance to intelligence in favor of logic? This book explains how and why our brains think in stories. Angus Fletcher, an expert in neuroscientific approaches to (...) narrative, identifies this capacity as “storythinking.” He demonstrates that storythinking is fundamental to what makes us human. Artificial intelligence can perform symbolic logic, rational deduction, and mathematical calculation, but it is incapable of deliberating in narrative. Drawing on new research in neuroscience and narrative theory, Fletcher explores the nature of imagination, innovation, and creativity. He provides concise answers to big questions: How does storythinking work? Why did it evolve? How can it misfire? What problems can it solve? Revealing the significance of storythinking from science to business to philosophy, this book also provides ways for readers to harness its power to script better tomorrows. (shrink)

Argumentation theory is a distinctly multidisciplinary field of inquiry. It draws its data, assumptions, and methods from disciplines as disparate as formal logic and discourse analysis, linguistics and forensic science, philosophy and psychology, political science and education, sociology and law, and rhetoric and artificial intelligence. This presents the growing group of interested scholars and students with a problem of access, since it is even for those active in the field not common to have acquired a familiarity (...) with relevant aspects of each discipline that enters into this multidisciplinary matrix. This book offers its readers a unique comprehensive survey of the various theoretical contributions which have been made to the study of argumentation. It discusses the historical works that provide the background to the field and all major approaches and trends in contemporary research. Argument has been the subject of systematic inquiry for twenty-five hundred years. It has been graced with theories, such as formal logic or the legal theory of evidence, that have acquired a more or less settled provenance with regard to specific issues. But there has been nothing to date that qualifies as a unified general theory of argumentation, in all its richness and complexity. This being so, the argumentation theorist must have access to materials and methods that lie beyond his or her "home" subject. It is precisely on this account that this volume is offered to all the constituent research communities and their students. Apart from the historical sections, each chapter provides an economical introduction to the problems and methods that characterize a given part of the contemporary research program. Because the chapters are self-contained, they can be consulted in the order of a reader's interests or research requirements. But there is value in reading the work in its entirety. Jointly authored by the very people whose research has done much to define the current state of argumentation theory and to point the way toward more general and unified future treatments, this book is an impressively authoritative contribution to the field. (shrink)

Many of the computing systems programmed using Machine Learning are opaque: it is difficult to know why they do what they do or how they work. Explainable Artificial Intelligence aims to develop analytic techniques that render opaque computing systems transparent, but lacks a normative framework with which to evaluate these techniques’ explanatory successes. The aim of the present discussion is to develop such a framework, paying particular attention to different stakeholders’ distinct explanatory requirements. Building on an analysis of “opacity” from (...) philosophy of science, this framework is modeled after accounts of explanation in cognitive science. The framework distinguishes between the explanation-seeking questions that are likely to be asked by different stakeholders, and specifies the general ways in which these questions should be answered so as to allow these stakeholders to perform their roles in the Machine Learning ecosystem. By applying the normative framework to recently developed techniques such as input heatmapping, feature-detector visualization, and diagnostic classification, it is possible to determine whether and to what extent techniques from Explainable Artificial Intelligence can be used to render opaque computing systems transparent and, thus, whether they can be used to solve the Black Box Problem. (shrink)

This paper uses a case study of a 1970s controversy in artificial-intelligence research to explore how scientists understand the relationships between research and practical applications. It is part of a project that seeks to map such relationships in order to enable better policy recommendations to be grounded empirically through historical evidence. In 1972 the mathematician James Lighthill submitted a report, published in 1973, on the state of artificial-intelligence research under way in the United Kingdom. The criticisms made in the report (...) have been held to be a major cause behind the dramatic slowing down of such research. This paper has two aims, one narrow and one broad. The narrow aim is to inquire into the causes, motivations and content of the Lighthill report. I argue that behind James Lighthill's criticisms of a central part of artificial intelligence was a principle he held throughout his career – that the best research was tightly coupled to practical problem solving. I also show that the Science Research Council provided a preliminary steer to the direction of this apparently independent report. The broader aim of the paper is to map some of the ways that scientists have articulated and justified relationships between research and practical, real-world problems, an issue previously identified as central to historical analysis of modern science. The paper therefore offers some deepened historical case studies of the processes identified in Agar's ‘working-worlds’ model. (shrink)

A new approach to Hume's problem of induction that justifies the optimality of induction at the level of meta-induction. Hume's problem of justifying induction has been among epistemology's greatest challenges for centuries. In this book, Gerhard Schurz proposes a new approach to Hume's problem. Acknowledging the force of Hume's arguments against the possibility of a noncircular justification of the reliability of induction, Schurz demonstrates instead the possibility of a noncircular justification of the optimality of induction, or, more (...) precisely, of meta-induction (the application of induction to competing prediction models). Drawing on discoveries in computational learning theory, Schurz demonstrates that a regret-based learning strategy, attractivity-weighted meta-induction, is predictively optimal in all possible worlds among all prediction methods accessible to the epistemic agent. Moreover, the a priori justification of meta-induction generates a noncircular a posteriori justification of object induction. Taken together, these two results provide a noncircular solution to Hume's problem. Schurz discusses the philosophical debate on the problem of induction, addressing all major attempts at a solution to Hume's problem and describing their shortcomings; presents a series of theorems, accompanied by a description of computer simulations illustrating the content of these theorems (with proofs presented in a mathematical appendix); and defends, refines, and applies core insights regarding the optimality of meta-induction, explaining applications in neighboring disciplines including forecasting sciences, cognitive science, social epistemology, and generalized evolution theory. Finally, Schurz generalizes the method of optimality-based justification to a new strategy of justification in epistemology, arguing that optimality justifications can avoid the problems of justificatory circularity and regress. (shrink)

According to the diversity-beats-ability theorem, groups of diverse problem solvers can outperform groups of high-ability problem solvers. We argue that the model introduced by Lu Hong and Scott Page is inadequate for exploring the trade-off between diversity and ability. This is because the model employs an impoverished implementation of the problem-solving task. We present a new version of the model which captures the role of ‘ability’ in a meaningful way, and use it to explore the trade-offs (...) between diversity and ability in scientific problem solving. (shrink)

Predictive policing has become a new panacea for crime prevention. However, we still know too little about the performance of computational methods in the context of predictive policing. The paper provides a detailed analysis of existing approaches to algorithmic crime forecasting. First, it is explained how predictive policing makes use of predictive models to generate crime forecasts. Afterwards, three epistemologies of predictive policing are distinguished: mathematical social science, social physics and machine learning. Finally, it is shown that these epistemologies (...) have significant implications for the constitution of predictive knowledge in terms of its genesis, scope, intelligibility and accessibility. It is the different ways future crimes are rendered knowledgeable in order to act upon them that reaffirm or reconfigure the status of criminological knowledge within the criminal justice system, direct the attention of law enforcement agencies to particular types of crimes and criminals and blank out others, satisfy the claim for the meaningfulness of predictions or break with it and allow professionals to understand the algorithmic systems they shall rely on or turn them into a black box. By distinguishing epistemologies and analysing their implications, this analysis provides insight into the techno-scientific foundations of predictive policing and enables us to critically engage with the socio-technical practices of algorithmic crime forecasting. (shrink)

The present paper will summarise the methodology, the scientific outcomes, and the potential for generalisation of the model of a project that studied cohabitation between human inhabitants and liminal species (in the present case, corvids) in Tartu, Estonia, from October 2021 to July 2023, with a comparative field study in Paris, France. It will present the context and goals of using a semiotic model to map interspecific cohabitation, expose what kind of data can be used to feed the model in (...) a relevant way and how it was done in the case of this project. This paper will present how the model diagnosed issues in cohabitation, both from material nuisances and cultural aspects, insisting on the concept of “hostile minority” that emerged from the study. It will discuss the importance of problem-solving in interspecific cohabitation, what the model suggested regarding this aspect, how this tool can be generalised to a large variety of cases and why it should be used this way. (shrink)

The frame problem was originally a problem for Artificial Intelligence, but philosophers have interpreted it as an epistemological problem for human cognition. As a result of this reinterpretation, however, specifying the frame problem has become a difficult task. To get a better idea of what the frame problem is, how it gives rise to more general problems of relevance, and how deep these problems run, I expound six guises of the frame problem. I then (...) assess some proposed heuristic solutions to the frame problem; I show that these proposals misunderstand, and fail to address, an important aspect of the frame problem. Finally, I argue that though human cognition does not solve the frame problem in its epistemological guise, human cognition avoids some of the epistemological worries. (shrink)

The basic task of the essay is to exhibit science as a rational enterprise. I argue that in order to do this we need to change quite fundamentally our whole conception of science. Today it is rather generally taken for granted that a precondition for science to be rational is that in science we do not make substantial assumptions about the world, or about the phenomena we are investigating, which are held permanently immune from empirical appraisal. (...) According to this standard view, science is rational precisely because science does not make a priori metaphysical presuppositions about the world forever preserved from possible empirical refutation. It is of course accepted that an individual scientist, developing a new theory, may well be influenced by his own metaphysical presuppositions. In addition, it is acknowledged that a successful scientific theory, within the context of a particular research program, may be protected for a while from refutation, thus acquiring a kind of temporary metaphysical status, as long as the program continues to be empirically progressive. All such views unite, however, in maintaining that science cannot make permanent metaphysical presuppositions, held permanently immune from objective empirical evaluation. According to this standard view, the rationality of science arises, not from the way in which new theories are discovered, but rather from the way in which already formulated theories are appraised in the light of empirical considerations. And the fundamental problem of the rationality of science—the Humean problem of induction— concerns precisely the crucial issue of the rationality of accepting theories in the light of evidence. In this essay I argue that this widely accepted standard conception of science must be completely rejected if we are to see science as a rational enterprise. In order to assess the rationality of accepting a theory in the light of evidence it is essential to consider the ultimate aims of science. This is because adopting different aims for science will lead us, quite rationally, to accept different theories in the light of evidence. I argue that a basic aim of science is to explain. At the outset science simply presupposes, in a completely a priori fashion, that explanations can be found, that the world is ultimately intelligible or simple. In other words, science simply presupposes in an a priori way the metaphysical thesis that the world is intelligible, and then seeks to convert this presupposed metaphysical theory into a testable scientific theory. Scientific theories are only accepted insofar as they promise to help us realize this fundamental aim. At once a crucial problem arises. If scientific theories are only accepted insofar as they promise to lead us towards articulating a presupposed metaphysical theory, it is clearly essential that we can choose rationally, in an a priori way, between all the very different possible metaphysical theories that can be thought up, all the very different ways in which the universe might ultimately be intelligible. For holding different aims, accepting different metaphysical theories conceived of as blueprints for future scientific theories will, quite rationally, lead us to accept different scientific theories. Thus it is only if we can choose rationally between conflicting metaphysical blueprints for future scientific theories that we will be in a position to appraise rationally the acceptability of our present day scientific theories. We thus face the crucial problem: How can we choose rationally between conflicting possible aims for science, conflicting metaphysical blueprints for future scientific theories ? It is only if we can solve this fundamental problem concerning the aims of science that we can be in a position to appraise rationally the acceptability of existing scientific theories. There is a further point here. If we could choose rationally between rival aims, rival metaphysical blueprints for future scientific theories, then we would in effect have a rational method for the discovery of new scientific theories! Thus we reach the result: there is only a rational method for the appraisal of existing scientific theories if there is a rational method of discovery. I shall argue that the aim-oriented theory of scientific inquiry to be advocated here succeeds in exhibiting science as a rational enterprise in that it succeeds in providing a rational procedure for choosing between rival metaphysical blueprints: it thus provides a rational, if fallible, method of discovery, and a rational method for the appraisal of existing scientific theories—thus resolving the Humean problem. In Part I of the essay I argue that the orthodox conception of science fails to exhibit science as a rational enterprise because it fails to solve the Humean problem of induction. The presuppositional view advocated here does however succeed in resolving the Humean problem. In Part II of the essay I spell out the new aim-oriented theory of scientific method that becomes inevitable once we accept the basic presuppositional viewpoint. I argue that this new aim oriented conception of scientific method is essentially a rational method of scientific discovery, and that the theory has important implications for scientific practice. (shrink)

This essay examines arguments for and against the proposition that Artificial Intelligence (AI) research makes an important contribution to the understanding of the human mind. A number of recent articles have seemed to question the value of Al ideas in specific domains (e.g., language. mental imagery, problem solving). In the present paper, it is argued that the real disagreement concerns the form of a scientific psychology. The critics of Artificial Intelligence believe that many acceptable psychological theories exist and (...) the important task of the scientist is to show that certain of these theories are true. They criticize AI research because it has not produced theories whose adequacy can be tested by empirical research. The supporters of AI research believe that no adequate psychological theories exist. They claim that the important task is to develop adequate theories and until such theories have been constructed, it is pointless to worry about empirical tests.A number of major arguments attacking and defending AI research are examined in order to illustrate that the real issue is methodological. It is concluded that the present debate is counterproductive since both sides assume that the methodological premise of the other side is wrong without attempting to refute it. A more constructive debate would address directly the more fundamental question of what methods offer the greatest promise of solving the major problems of psychology. (shrink)

The concept of information has been extensively studied and written about, yet no consensus on a unified definition of information has to date been reached. This paper seeks to establish the basis for a unified definition of information. We claim a biosemiotics perspective, based on Gregory Bateson’s definition of information, provides a footing on which to build because the frame this provides has applicability to both the sciences and humanities. A key issue in reaching a unified definition of information is (...) the fundamental problem of identifying how a human organism, in a self-referential process, develops from a state in which its knowledge of the human-organism-in-its-environment is almost non-existent to a state in which the human organism not only recognizes the existence of the environment but also sees itself as part of the human-organism-in-its-environment system. This allows a human organism not only to self-referentially engage with the environment and navigate through it, but also to transform it in its own image and likeness. In other words, the Fundamental Problem of the Science of Information concerns the phylogenetic development process, as well as the ontogenetic development process of Homo sapiens sapiens from a single cell to our current multicellular selves, all in a changing long-term and short-term environment, respectively. (shrink)

Among the many fundamental problems Wittgenstein discussed, two are especially relevant for evolutionary theory. The first one is the problem of negation and its relation to the intentionality of thought. Its resolution answers the question of how thought can anticipate reality though what is thought may not exist, and explains how empirical propositions are distinguishable from mathematical, logical, and conceptual (or what are traditionally called metaphysical) propositions. The second is the problem of the grounds of sensory experience. Wittgenstein’s (...) resolution of this problem is revolutionary since he showed that first-person, present-tense expressions (these are sentences by which we express what goes on in our mind) have no grounds at all, clarifying why we should abandon (all variants of) representative idealism. In combination with the resolution of the first problem, it enables us to develop new explanations about how during the course of development and evolution the mind evolves and evolved. (shrink)

The article addresses the problem of identifying methods to develop the ability of artificial intelligence (AI) systems to provide explanations for their findings. This issue is not new, but, nowadays, the increasing complexity of AI systems is forcing scientists to intensify research in this direction. Modern neural networks contain hundreds of layers of neurons. The number of parameters of these networks reaches trillions, genetic algorithms generate thousands of generations of solutions, and the semantics of AI models become more complicated, (...) going to the quantum and non-local levels. The world’s leading companies are investing heavily in creating explainable AI (XAI). However, the result is still unsatisfactory: a person often cannot understand the “explanations” of AI because the latter makes decisions differently than a person, and perhaps because a good explanation is impossible within the framework of the classical AI paradigm. AI faced a similar problem 40 years ago when expert systems contained only a few hundred logical production rules. The problem was then solved by complicating the logic and building added knowledge bases to explain the conclusions given by AI. At present, other approaches are needed, primarily those that consider the external environment and the subjectivity of AI systems. This work focuses on solving this problem by immersing AI models in the social and economic environment, building ontologies of this environment, taking into account a user profile and creating conditions for purposeful convergence of AI solutions and conclusions to user-friendly goals. (shrink)

In science policy, it is generally acknowledged that science-based problem-solving requires interdisciplinary research. For example, policy makers invest in funding programs such as Horizon 2020 that aim to stimulate interdisciplinary research. Yet the epistemological processes that lead to effective interdisciplinary research are poorly understood. This article aims at an epistemology for interdisciplinary research, in particular, IDR for solving ‘real-world’ problems. Focus is on the question why researchers experience cognitive and epistemic difficulties in conducting IDR. Based (...) on a study of educational literature it is concluded that higher-education is missing clear ideas on the epistemology of IDR, and as a consequence, on how to teach it. It is conjectured that the lack of philosophical interest in the epistemology of IDR is due to a philosophical paradigm of science, which prevents recognizing severe epistemological challenges of IDR, both in the philosophy of science as well as in science education and research. The proposed alternative philosophical paradigm entails alternative philosophical presuppositions regarding aspects such as the aim of science, the character of knowledge, the epistemic and pragmatic criteria for accepting knowledge, and the role of technological instruments. This alternative philosophical paradigm assume the production of knowledge for epistemic functions as the aim of science, and interprets ‘knowledge’ as epistemic tools that must allow for conducting epistemic tasks by epistemic agents, rather than interpreting knowledge as representations that objectively represent aspects of the world independent of the way in which it was constructed. The engineering paradigm of science involves that knowledge is indelibly shaped by how it is constructed. Additionally, the way in which scientific disciplines construct knowledge is guided by the specificities of the discipline, which can be analyzed in terms of disciplinary perspectives. This implies that knowledge and the epistemic uses of knowledge cannot be understood without at least some understanding of how the knowledge is constructed. Accordingly, scientific researchers need so-called metacognitive scaffolds to assist in analyzing and reconstructing how ‘knowledge’ is constructed and how different disciplines do this differently. In an engineering paradigm of science, these metacognitive scaffolds can also be interpreted as epistemic tools, but in this case as tools that guide, enable and constrain analyzing and articulating how knowledge is produced. In interdisciplinary research, metacognitive scaffolds assist interdisciplinary communication aiming to analyze and articulate how the discipline constructs knowledge. (shrink)

"Psychiatry and Philosophy of Science" explores conceptual issues in psychiatry from the perspective of analytic philosophy of science. Through an examination of those features of psychiatry that distinguish it from other sciences - for example, its contested subject matter, its particular modes of explanation, its multiple different theoretical frameworks, and its research links with big business - Rachel Cooper explores some of the many conceptual, metaphysical and epistemological issues that arise in psychiatry. She shows how these pose interesting (...) challenges for the philosopher of science while also showing how ideas from the philosophy of science can help to solve conceptual problems within psychiatry. Cooper's discussion ranges over such topics as the nature of mental illnesses, the treatment decisions and diagnostic categories of psychiatry, the case-history as a form of explanation, how psychiatry might be value-laden, the claim that psychiatry is a multi-paradigm science, the distortion of psychiatric research by pharmaceutical industries, as well as engaging with the fundamental question whether the mind is reducible to something at the physical level. "Psychiatry and Philosophy of Science" demonstrates that cross-disciplinary contact between philosophy of science and psychiatry can be immensely productive for both subjects and it will be required reading for mental health professionals and philosophers alike. (shrink)

In this essay we critically evaluate the progress that has been made in solving the problem of meaning in artificial intelligence (AI) and robotics. We remain skeptical about solutions based on deep neural networks and cognitive robotics, which in our opinion do not fundamentally address the problem. We agree with the enactive approach to cognitive science that things appear as intrinsically meaningful for living beings because of their precarious existence as adaptive autopoietic individuals. But this approach (...) inherits the problem of failing to account for how meaning as such could make a difference for an agent’s behavior. In a nutshell, if life and mind are identified with physically deterministic phenomena, then there is no conceptual room for meaning to play a role in its own right. We argue that this impotence of meaning can be addressed by revising the concept of nature such that the macroscopic scale of the living can be characterized by physical indeterminacy. We consider the implications of this revision of the mind-body relationship for synthetic approaches. (shrink)

The basic task of the essay is to exhibit science as a rational enterprise. I argue that in order to do this we need to change quite fundamentally our whole conception of science. Today it is rather generally taken for granted that a precondition for science to be rational is that in science we do not make substantial assumptions about the world, or about the phenomena we are investigating, which are held permanently immune from empirical appraisal. (...) According to this standard view, science is rational precisely because science does not make a priori metaphysical presuppositions about the world forever preserved from possible empirical refutation. It is of course accepted that an individual scientist, developing a new theory, may well be influenced by his own metaphysical presuppositions. In addition, it is acknowledged that a successful scientific theory, within the context of a particular research program, may be protected for a while from refutation, thus acquiring a kind of temporary metaphysical status, as long as the program continues to be empirically progressive. All such views unite, however, in maintaining that science cannot make permanent metaphysical presuppositions, held permanently immune from objective empirical evaluation. According to this standard view, the rationality of science arises, not from the way in which new theories are discovered, but rather from the way in which already formulated theories are appraised in the light of empirical considerations. And the fundamental problem of the rationality of science--the Humean problem of induction--concerns precisely the crucial issue of the rationality of accepting theories in the light of evidence. In this essay I argue that this widely accepted standard conception of science must be completely rejected if we are to see science as a rational enterprise. In order to assess the rationality of accepting a theory in the light of evidence it is essential to consider the ultimate aims of science. This is because adopting different aims for science will lead us, quite rationally, to accept different theories in the light of evidence. I argue that a basic aim of science is to explain. At the outset science simply presupposes, in a completely a priori fashion, that explanations can be found, that the world is ultimately intelligible or simple. In other words, science simply presupposes in an a priori way the metaphysical thesis that the world is intelligible, and then seeks to convert this presupposed metaphysical theory into a testable scientific theory. Scientific theories are only accepted insofar as they promise to help us realize this fundamental aim. At once a crucial problem arises. If scientific theories are only accepted insofar as they promise to lead us towards articulating a presupposed metaphysical theory, it is clearly essential that we can choose rationally, in an a priori way, between all the very different possible metaphysical theories that can be thought up, all the very different ways in which the universe might ultimately be intelligible. For holding different aims, accepting different metaphysical theories conceived of as blueprints for future scientific theories will, quite rationally, lead us to accept different scientific theories. Thus it is only if we can choose rationally between conflicting metaphysical blueprints for future scientific theories that we will be in a position to appraise rationally the acceptability of our present day scientific theories. We thus face the crucial problem: How can we choose rationally between conflicting possible aims for science, conflicting metaphysical blueprints for future scientific theories? It is only if we can solve this fundamental problem concerning the aims of science that we can be in a position to appraise rationally the acceptability of existing scientific theories. There is a further point here. If we could choose rationally between rival aims, rival metaphysical blueprints for future scientific theories, then we would in effect have a rational method for the discovery of new scientific theories! Thus we reach the result: there is only a rational method for the appraisal of existing scientific theories if there is a rational method of discovery. I shall argue that the aim-oriented theory of scientific inquiry to be advocated here succeeds in exhibiting science as a rational enterprise in that it succeeds in providing a rational procedure for choosing between rival metaphysical blueprints: it thus provides a rational, if fallible, method of discovery, and a rational method for the appraisal of existing scientific theories--thus resolving the Humean problem. In Part I of the essay I argue that the orthodox conception of science fails to exhibit science as a rational enterprise because it fails to solve the Humean problem of induction. The presuppositional view advocated here does however succeed in resolving the Humean problem. In Part II of the essay I spell out the new aim-oriented theory of scientific method that becomes inevitable once we accept the basic presuppositional viewpoint. I argue that this new aim oriented conception of scientific method is essentially a rational method of scientific discovery, and that the theory has important implications for scientific practice. (shrink)

This book explores new findings on the long-neglected topic of theory construction and discovery, and challenges the orthodox, current division of scientific development into discrete stages: the stage of generation of new hypotheses; the stage of collection of relevant data; the stage of justification of possible theories; and the final stage of selection from among equally confirmed theories. The chapters, written by leading researchers, offer an interdisciplinary perspective on various aspects of the processes by which theories rationally should, and descriptively (...) are, built. They address issues such as the role of problem-solving and heuristic reasoning in theory-building; how inferences and models shape the pursuit of scientific knowledge; the relation between problem-solving and scientific discovery; the relative values of the syntactic, semantic, and pragmatic view of theories in understanding theory construction; and the relation between ampliative inferences, heuristic reasoning, and models as a means for building new theories and knowledge. Through detailed arguments and examinations, the volume collectively challenges the orthodox view’s main tenets by characterizing the ways in which the different “stages” are logically, temporally, and psychologically intertwined. As a group, the chapters provide several attempts to answer long-standing questions about the possibility of a unified conceptual framework for building theories and formulating hypotheses. (shrink)

The article presents grounds for defining the fetish of artificial intelligence (AI). We highlight the fundamental differences of AI from all earlier technological advances, as they are primarily related to its introduction into the human cognitive sphere and generating fundamentally new uncontrollable consequences for society. We provide solid evidence that the leaders of the globalist project are the main beneficiaries of the AI fetish. This is clearly manifested in the works of philosophers who are close to major technology corporations and (...) their mega-projects. We suggest considering the problem of how to use the capabilities of AI to overcome the growing international conflicts and the global crisis. The focus is on the problem of agency, which solution from the standpoint of an anthropomorphic approach to AI is fraught with serious negative consequences. Endowing AI with agency, responsibility is implicitly removed from the person who uses the technology, and the established legislative practice is also destroyed. We present AI as an agent endowed with a set of invariant generalized qualities that is similar to natural subjects. These qualities include: the ability to deliberation, reflexivity, communication and elements of sociability. Such a representation of AI as an agent (pseudo-subject) is consistent with the principle of distributed control in biology and psychology, which was called the principle of a dual subject. In combination with the systems of principles and ontologies specified in the concept of post-nonclassical cybernetics of self-developing environments, this will allow the use of AI as a means of social innovation, while maintaining control over AI technologies. This will also help to pose and solve the problem of integrating formations of artificial and natural intelligence while maintaining the basic qualities of carriers of natural intelligence. (shrink)

The article presents grounds for defining the fetish of artificial intelligence (AI). We highlight the fundamental differences of AI from all earlier technological advances, as they are primarily related to its introduction into the human cognitive sphere and generating fundamentally new uncontrollable consequences for society. We provide solid evidence that the leaders of the globalist project are the main beneficiaries of the AI fetish. This is clearly manifested in the works of philosophers who are close to major technology corporations and (...) their mega-projects. We suggest considering the problem of how to use the capabilities of AI to overcome the growing international conflicts and the global crisis. The focus is on the problem of agency, which solution from the standpoint of an anthropomorphic approach to AI is fraught with serious negative consequences. Endowing AI with agency, responsibility is implicitly removed from the person who uses the technology, and the established legislative practice is also destroyed. We present AI as an agent endowed with a set of invariant generalized qualities that is similar to natural subjects. These qualities include: the ability to deliberation, reflexivity, communication and elements of sociability. Such a representation of AI as an agent (pseudo-subject) is consistent with the principle of distributed control in biology and psychology, which was called the principle of a dual subject. In combination with the systems of principles and ontologies specified in the concept of post-nonclassical cybernetics of self-developing environments, this will allow the use of AI as a means of social innovation, while maintaining control over AI technologies. This will also help to pose and solve the problem of integrating formations of artificial and natural intelligence while maintaining the basic qualities of carriers of natural intelligence. (shrink)

This article examines the fundamental ontological significance that the category of perception has in philosophy of G.W. Leibniz, and establishes the connection between the category of perception and modern panpsychism. There is a problem of definition of protopsychic properties in modern panpsychism. The problem is expressed not only in the absence of such a definition, but also in the absence of a good strategy for finding possible candidates for the role of protopsychic property. To solve this problem, (...) the author considers the status of the monad as the center of perception in Leibniz’s monadology, as well as the question of the relation of different monads to each other. Based on Leibniz’s ideas, the following modifications for modern panpsychism have been proposed, among others. First, it was proposed that protopsychic properties be viewed as properties that represent all reality in some vague way, preventing the emergence of high-level psychic properties. Second, it was proposed that mental properties be viewed not as a combination of protopsychic properties, but as state of protopsychic properties. This means that to form high-level mental properties, protopsychic properties must not only form some system, but must also be partially blocked. The author also considers the question of whether it is possible to borrow the proposed ideas in modern panpsychism. The problem for this is the ontological differences between modern naturalistic panpsychism and Leibniz’s classical panpsychism. The article proposes three different strategies for dealing with this problem related to three interpretations of Leibniz’s philosophy. First, it is possible to limit ourselves to considering physics as a set of structural phenomena derived from the activity of monads. Second, it is possible to try to show that there is some other, non-causal, type of relationship between the monads. Third, it is possible to consider this system as naturalistic pantheism and assume that God is a mediator and a common non-spatial coordinate system through which the monads agree with each other. (shrink)

This book discusses how scientific and other types of cognition make use of models, abduction, and explanatory reasoning in order to produce important and innovative changes in theories and concepts. Gathering revised contributions presented at the international conference on Model-Based Reasoning, held on October 24–26 2018 in Seville, Spain, the book is divided into three main parts. The first focuses on models, reasoning, and representation. It highlights key theoretical concepts from an applied perspective, and addresses issues concerning information visualization, experimental (...) methods, and design. The second part goes a step further, examining abduction, problem solving, and reasoning. The respective papers assess different types of reasoning, and discuss various concepts of inference and creativity and their relationship with experimental data. In turn, the third part reports on a number of epistemological and technological issues. By analyzing possible contradictions in modern research and describing representative case studies, this part is intended to foster new discussions and stimulate new ideas. All in all, the book provides researchers and graduate students in the fields of applied philosophy, epistemology, cognitive science, and artificial intelligence alike with an authoritative snapshot of the latest theories and applications of model-based reasoning. (shrink)

More than 50 years ago, Bongard introduced 100 visual concept learning problems as a challenge for artificial vision systems. These problems are now known as Bongard problems. Although they are well known in cognitive science and artificial intelligence, only very little progress has been made toward building systems that can solve a substantial subset of them. In the system presented here, visual features are extracted through image processing and then translated into a symbolic visual vocabulary. We introduce a formal (...) language that allows representing compositional visual concepts based on this vocabulary. Using this language and Bayesian inference, concepts can be induced from the examples that are provided in each problem. We find a reasonable agreement between the concepts with high posterior probability and the solutions formulated by Bongard himself for a subset of 35 problems. While this approach is far from solving Bongard problems like humans, it does considerably better than previous approaches. We discuss the issues we encountered while developing this system and their continuing relevance for understanding visual cognition. For instance, contrary to other concept learning problems, the examples are not random in Bongard problems; instead they are carefully chosen to ensure that the concept can be induced, and we found it helpful to take the resulting pragmatic constraints into account. (shrink)

Many current problems surrounding science revolve around the complex epistemological framework that shapes a new vision of knowledge about reality. The traditional epistemological positions are characterized by the explanation of nature by means of concatenated facts; that is, as bricks attached to each other giving shape to the edifice of science. A conception of this nature showed that the idea of certainty was nothing more than a mere illusion, opening the way, on the contrary, to the idea of (...) the uncertainty of knowledge based on the descriptions of quantum physics, also a product of the non-reductionist conception of reality, thus criticizing the specular representation. This article addresses the main problems surrounding the critical conceptions that the emerging epistemology reveals about knowledge, pointing towards a much more complex vision of reality. (shrink)

In science policy, it is generally acknowledged that science-based problem-solving requires interdisciplinary research. For example, policy makers invest in funding programs such as Horizon 2020 that aim to stimulate interdisciplinary research. Yet the epistemological processes that lead to effective interdisciplinary research are poorly understood. This article aims at an epistemology for interdisciplinary research, in particular, IDR for solving ‘real-world’ problems. Focus is on the question why researchers experience cognitive and epistemic difficulties in conducting IDR. Based (...) on a study of educational literature it is concluded that higher-education is missing clear ideas on the epistemology of IDR, and as a consequence, on how to teach it. It is conjectured that the lack of philosophical interest in the epistemology of IDR is due to a philosophical paradigm of science, which prevents recognizing severe epistemological challenges of IDR, both in the philosophy of science as well as in science education and research. The proposed alternative philosophical paradigm entails alternative philosophical presuppositions regarding aspects such as the aim of science, the character of knowledge, the epistemic and pragmatic criteria for accepting knowledge, and the role of technological instruments. This alternative philosophical paradigm assume the production of knowledge for epistemic functions as the aim of science, and interprets ‘knowledge’ as epistemic tools that must allow for conducting epistemic tasks by epistemic agents, rather than interpreting knowledge as representations that objectively represent aspects of the world independent of the way in which it was constructed. The engineering paradigm of science involves that knowledge is indelibly shaped by how it is constructed. Additionally, the way in which scientific disciplines construct knowledge is guided by the specificities of the discipline, which can be analyzed in terms of disciplinary perspectives. This implies that knowledge and the epistemic uses of knowledge cannot be understood without at least some understanding of how the knowledge is constructed. Accordingly, scientific researchers need so-called metacognitive scaffolds to assist in analyzing and reconstructing how ‘knowledge’ is constructed and how different disciplines do this differently. In an engineering paradigm of science, these metacognitive scaffolds can also be interpreted as epistemic tools, but in this case as tools that guide, enable and constrain analyzing and articulating how knowledge is produced. In interdisciplinary research, metacognitive scaffolds assist interdisciplinary communication aiming to analyze and articulate how the discipline constructs knowledge. (shrink)

Alan M. Turing, pioneer of computing and WWII codebreaker, is one of the most important and influential thinkers of the twentieth century. In this volume for the first time his key writings are made available to a broad, non-specialist readership. They make fascinating reading both in their own right and for their historic significance: contemporary computational theory, cognitive science, artificial intelligence, and artificial life all spring from this ground-breaking work, which is also rich in philosophical and logical insight. An (...) introduction by leading Turing expert Jack Copeland provides the background and guides the reader through the selection. About Alan Turing Alan Turing FRS OBE, (1912-1954) studied mathematics at King's College, Cambridge. He was elected a Fellow of King's in March 1935, at the age of only 22. In the same year he invented the abstract computing machines - now known simply as Turing machines - on which all subsequent stored-program digital computers are modelled. During 1936-1938 Turing continued his studies, now at Princeton University. He completed a PhD in mathematical logic, analysing the notion of 'intuition' in mathematics and introducing the idea of oracular computation, now fundamental in mathematical recursion theory. An 'oracle' is an abstract device able to solve mathematical problems too difficult for the universal Turing machine. In the summer of 1938 Turing returned to his Fellowship at King's. When WWII started in 1939 he joined the wartime headquarters of the Government Code and Cypher School (GC&CS) at Bletchley Park, Buckinghamshire. Building on earlier work by Polish cryptanalysts, Turing contributed crucially to the design of electro-mechanical machines ('bombes') used to decipher Enigma, the code by means of which the German armed forces sought to protect their radio communications. Turing's work on the version of Enigma used by the German navy was vital to the battle for supremacy in the North Atlantic. He also contributed to the attack on the cyphers known as 'Fish'. Based on binary teleprinter code, Fish was used during the latter part of the war in preference to morse-based Enigma for the encryption of high-level signals, for example messages from Hitler and other members of the German High Command. It is estimated that the work of GC&CS shortened the war in Europe by at least two years. Turing received the Order of the British Empire for the part he played. In 1945, the war over, Turing was recruited to the National Physical Laboratory (NPL) in London, his brief to design and develop an electronic computer - a concrete form of the universal Turing machine. Turing's report setting out his design for the Automatic Computing Engine (ACE) was the first relatively complete specification of an electronic stored-program general-purpose digital computer. Delays beyond Turing's control resulted in NPL's losing the race to build the world's first working electronic stored-program digital computer - an honour that went to the Royal Society Computing Machine Laboratory at Manchester University, in June 1948. Discouraged by the delays at NPL, Turing took up the Deputy Directorship of the Royal Society Computing Machine Laboratory in that year. Turing was a founding father of modern cognitive science and a leading early exponent of the hypothesis that the human brain is in large part a digital computing machine, theorising that the cortex at birth is an 'unorganised machine' which through 'training' becomes organised 'into a universal machine or something like it'. He also pioneered Artificial Intelligence. Turing spent the rest of his short career at Manchester University, being appointed to a specially created Readership in the Theory of Computing in May 1953. He was elected a Fellow of the Royal Society of London in March 1951 (a high honour). (shrink)

This research presents a computer model called EUREKA that begins with novice‐like strategies and knowledge organizations for solving physics word problems and acquires features of knowledge organizations and basic approaches that characterize experts in this domain. EUREKA learns a highly interrelated network of problem‐type schemas with associated solution methodologies. Initially, superficial features of the problem statement form the basis for both the problem‐type schemas and the discriminating features that organize them in the P‐MOP (Problem Memory (...) Organization Packet) network. As EUREKA solves more problems, the content of the schemas and the discriminating features change to reflect more fundamental physics principles. This changing network allows EUREKA to shift from a novicelike means‐ends strategy to a more expertlike “knowledge development” strategy in which the presence of abstract concepts are triggered by problem features. In this model, the strategy shift emerges as a natural consequence of the evolving expertlike organization of problem‐type schemos. EUREKA captures many of the descriptive models of novice expert differences, and also suggests a number of empirically testable assumptions regarding problem‐solving strategies and the representation of problem‐solving knowledge. (shrink)