This volume is a collection of papers that explore various areas of common interest between philosophy, computing, and cognition. The book illustrates the rich intrigue of this fascinating recent intellectual story. It begins by providing a new analysis of the ideas related to computer ethics, such as the role in information technology of the so-called moral mediators, the relationship between intelligent machines and warfare, and the new opportunities offered by telepresnece, for example in teaching and learning. The (...) book also ties together the concerns of epistemology and logic, showing, for example, the connections between computers, bio-robotics, and scientific research and between computational programs and scientific discovery. Important results coming from recent computational models of deduction, the dynamic nature of meaning, and the role of reasoning and learning in spatial, visual and exemplar-based compuational frameworks are also addressed. Some stimulating papers carefully study how the interplay between computing and philosophy has also shed new light on the role of rational acceptance in the logic of belief and on the status of old philosophical topics like embodiment and consciousness, the role of information and the problem of realism in the new digital world. Finally, a considerable part of the book addresses the role of intenal and external representations in scientific reasoning and creative inferences as well as the place of manipulation of objects and artifacts in human cognition. Taking these topics together this book describes an aspect of an emerging agenda which is likely to carry the interaction between philosophy, cognition and computing forward into the twenty-first century. The volume is based on the papers that were presented at the International European Conference Computing and Philosophy, E-CAP2004, Italy, held at the University of Paiva, Paiv, Italy in June 2004, chaired by Lorenzo Magnani. (shrink)

Technological advances in computer science have secured the computer metaphor status of a heuristic methodological tool used to answer the question about the nature of mind. Nevertheless, some philosophers strongly support opposite opinions. Anti-computationalism in the philosophy of mind is a methodological program that uses extremely heterogeneous grounds for argumentation, deserving analysis and discussion. This article provides an overview and interpretation of the traditional criticism of the computational theory of mind ; its basic theses have been (...) formed in Western philosophy in the last quarter of the 20th century. The main goal is to reveal the content of the arguments of typical anti-computationalist programs and expand their application to the framework of the semantic problems of the Classic Computational Theory of Mind. The main fault of the symbolic ap-proach in the classical computationalism is the absence of a full-fledged theory of semantic properties. The relevance of considering these seemingly outdated problems is justified by the fact that the problem of meaning remains in the core of the latest developments in various areas of AI and the principles of human-computer interaction. (shrink)

Embedding ethics modules within computer science courses has become a popular response to the growing recognition that CS programs need to better equip their students to navigate the ethical dimensions of computing technologies like AI, machine learning, and big data analytics. However, the popularity of this approach has outpaced the evidence of its positive outcomes. To help close that gap, this empirical study reports positive results from Northeastern’s program that embeds values analysis modules into CS courses. The resulting (...) data suggest that such modules have a positive effect on students’ moral attitudes and that students leave the modules believing they are more prepared to navigate the ethical dimensions they’ll likely face in their eventual careers. Importantly, these gains were accomplished at an institution without a philosophy doctoral program, suggesting this strategy can be effectively employed by a wider range of institutions than many have thought. (shrink)

This book presents a systematic philosophical and historical analysis of operating systems (0S). The discussion starts with the evolution of OSs since before their birth. It continues with a comprehensive philosophical analysis grounded in technical aspects. Coverage looks at software and (where appropriate) hardware as well as their historical developments. The authors not only offer historical and philosophical reflections on operating systems. They also explore the programs they coordinate and trace the epsitemic and ontological consequences of their designs. (...) Each chapter investigates one or more overlapping fragments of this fascinating history. These include: the birth of the UNIX system and the development of early systems and prototypes; a conceptual analysis of the plurality of systems; an investigation into business, ethical, and aesthetics aspects related to operating systems; and logical principles of formal languages. This book will interest researchers from a diversity of backgrounds. It will appeal to historians, philosophers, as well as logicians and computer scientists who want to engage with topics relevant to the history and philosophy of programming and more specifically that of operating systems. (shrink)

It has been known since the seventies that the formulas of modal logic are invariant for bisimulations between possible worlds models — while conversely, all bisimulation-invariant first-order formulas are modally definable. In this paper, we extend this semantic style of analysis from modal formulas to dynamic program operations. We show that the usual regular operations are safe for bisimulation, in the sense that the transition relations of their values respect any given bisimulation for their arguments. Our main result is (...) a complete syntactic characterization of all first-order definable program operations that are safe for bisimulation. This is a semantic functional completeness result for programming, which may be contrasted with the more usual analysis in terms of computational power. The 'Safety Theorem' can be modulated in several ways. We conclude with a list of variants, extensions, and further developments. (shrink)

Empirical insights into language processing have a philosophical relevance that extends well beyond philosophical questions about language. This chapter will discuss this wider relevance: We will consider how experimental philosophers can examine language processing in order to address questions in several different areas of philosophy. To do so, we will present the emerging research program of experimental argument analysis (EAA) that examines how automatic language processing shapes verbal reasoning – including philosophical arguments. The evidential strand of experimental (...) class='Hi'>philosophy uses mainly questionnaire-based methods to assess the evidentiary value of intuitive judgments that are adduced as evidence for philosophical theories and as premises for philosophical arguments. Extending this prominent strand of experimental philosophy, EAA underpins such assessments, extends the scope of the assessments, and expands the range of the empirical methods employed: EAA examines how automatic inferences that are continually made in language comprehension and production shape verbal reasoning, and draws on findings about comprehension biases that affect the contextualisation of such default inferences, in order to explain and expose fallacies. It deploys findings to assess premises and inferences from premises to conclusions, in philosophical arguments. To do so, it adapts methods from psycholinguistics and recruits methods from computational linguistics. (shrink)

The article delves into the conceptual frameworks surrounding artificial intelligence (AI) by juxtaposing it with natural intelligence and delineating the correlated notions. It enumerates the issues propelling the discourse on the explored topics. The author proposes a bifurcation between two polar concepts of artificial intelligence. The first is dubbed “imitative,” where AI is perceived in relation to natural intelligence as its technical recreation, capable of not only emulating but significantly outstripping its natural counterpart. A prerequisite for embodying this concept is (...) understanding natural intelligence; three approaches are examined: (a) acknowledging the lack of a precise understanding of natural intelligence, (b) exploring it from a biological perspective, and (c) analyzing it from a psychological perspective. The author articulates their own interpretation of natural intelligence, portraying it as a multifaceted amalgam of cultural, historical, social, and anthropological elements. From this vantage point, natural intelligence emerges not merely as a natural formation (thereby, discussions about the laws governing its function and evolution are warranted), but also as an “extra-natural” formation, its existence dictated by randomness and uniqueness, meaning natural intelligence evolves in a “singular” manner. In the context of comparing natural and artificial intelligence, the discussion encompasses several issues: the feasibility of the control of natural intelligence processes, the structure of neural networks, the superiority of computer programs in chess, the use of neural networks to write academic papers, and so forth. The conclusion posits that given artificial intelligence, despite its complexity, remains a technical invention orchestrated and brought to fruition by humans as a tool; society, if inclined to bestow AI with autonomy for tackling specific tasks, ought to do so prudently to prevent self-detriment and retain the ability to curtail or utterly revoke such autonomy. (shrink)

This is an introductory book that targets the reader who has the ambition to apply economic analysis but may be missing a technical introduction to its mathematical techniques or seeks a structured elaboration of its philosophical principles. The book juxtaposes economic analysis with moral philosophy, political theory, egalitarianism, and other methodological principles and then passes to the details of methods such as model-building, derivatives, differential equations, statistical tests, and the use of computer programs.

The core entities of computer science include formal languages, spec-ifications, models, programs, implementations, semantic theories, type inference systems, abstract and physical machines. While there are conceptual questions concerning their nature, and in particular ontological ones (Turner 2018), our main focus here will be on the relationships between them. These relationships have an extensional aspect that articulates the propositional connection between the two entities, and an intentional one that fixes the direction of governance. An analysis of these two aspects (...) will drive our investigation; an investigation that will touch upon some of the central concerns of the philosophy of computer science (Turner 2017). (shrink)

The development of the Minimalist Program, Noam Chomsky's most recent generative model of linguistics, has been highly influential over the last twenty years. It has had significant implications not only for the conduct of linguistic analysis itself, but also for our understanding of the status of linguistics as a science. The reflections and analyses in this book contain insights into the strengths and the weaknesses of the MP. Among these are, a clarification of the content of the Strong Minimalist (...) Thesis ; a synthesis of Chomsky's linguistic and interdisciplinary discourses; and an analysis of the notion of optimal computation from conceptual, empirical and philosophical perspectives. This book will encourage graduate students and researchers in linguistics to reflect on the foundations of their discipline, and the interdisciplinary nature of the topics explored will appeal to those studying biolinguistics, neurolinguistics, the philosophy of language and other related disciplines. (shrink)

Scientific discovery is often regarded as romantic and creative - and hence unanalyzable - whereas the everyday process of verifying discoveries is sober and more suited to analysis. Yet this fascinating exploration of how scientific work proceeds argues that however sudden the moment of discovery may seem, the discovery process can be described and modeled. Using the methods and concepts of contemporary information-processing psychology (or cognitive science) the authors develop a series of artificial-intelligence programs that can simulate the human (...) thought processes used to discover scientific laws. The programs - BACON, DALTON, GLAUBER, and STAHL - are all largely data-driven, that is, when presented with series of chemical or physical measurements they search for uniformities and linking elements, generating and checking hypotheses and creating new concepts as they go along. "Scientific Discovery examines the nature of scientific research and reviews the arguments for and against a normative theory of discovery; describes the evolution of the BACON programs, which discover quantitative empirical laws and invent new concepts; presents programs that discover laws in qualitative and quantitative data; and ties the results together, suggesting how a combined and extended program might find research problems, invent new instruments, and invent appropriate problem representations. Numerous prominent historical examples of discoveries from physics and chemistry are used as tests for the programs and anchor the discussion concretely in the history of science. Pat Langley is an Associate Professor in the Department of Information and Computer Science at the University of California, Irvine.Herbert Simon is a Professor in the Departments of Psychology, Computer Science, and Philosophy at Carnegie-Mellon University. Gary L. Bradshaw is an Assistant Professor in the Department of Psychology and Institute of Cognitive Science at the University of Colorado, Boulder. Jan M. Zytkow is an Associate Professor in the Computer Science Department at Wichita State University. (shrink)

Computational philosophy (CP) aims at investigating many important concepts and problems of the philosophical and epistemological tradition in a new way by taking advantage of information-theoretic, cognitive, and artificial intelligence methodologies. I maintain that the results of computational philosophy meet the classical requirements of some Peircian pragmatic ambitions. Indeed, more than a 100 years ago, the American philosopher C.S. Peirce, when working on logical and philosophical problems, suggested the concept of pragmatism(pragmaticism, in his own words) as a logical (...) criterion to analyze what words and concepts express through their practical meaning. Many words have been spent on creative processes and reasoning, especially in the case of scientific practices. In fact, many philosophers have usually offered a number of ways of construing hypotheses generation, but they aim at demonstrating that the activity of generating hypotheses is paradoxical, obscure, and thus not analyzable. Those descriptions are often so far from Peircian pragmatic prescription and so abstract to result completely unknowable and obscure. To dismiss this tendency and gain interesting insight about the so-called logic of scientific discovery we need to build constructive procedures, which could play a role in moving the problem-solving process forward by implementing them in some actual models. The computational turn gives us a new way to understand creative processes in a strictly pragmatic sense. In fact, by exploiting artificial intelligence and cognitive science tools, computational philosophy allows us to test concepts and ideas previously conceived only in abstract terms. It is in the perspective of these actual computational models that I find the central role of abduction in the explanation of creative reasoning in science. I maintain that the computational philosophy analysis of model-based and manipulative abduction and of external and epistemic mediators is important not only to delineate the actual practice of abduction, but also to further enhance the development of programs computationally adequate in rediscovering, or discovering for the first time, for example, scientific hypotheses or mathematical theorems. The last part of the paper is devoted to illustrating the problem of the extra-theoretical dimension of reasoning and discovery from the perspective of some mathematical cases derived from calculus and geometry. (shrink)

Professor Apter has written a valuable book. His work, a non-technical introduction to the most important aspect of the use of computers in psychology, is simple, readable, yet surprisingly concentrated and provocative. His first two chapters contain an unusually clear, concise examination of the extent to which minds and machines can be compared. Although brief it successfully collates the work of famous scientists and scholars of varied disciplines into a coherent cybernetic theory. Chapter three is a simplified explanation of the (...) way a digital computer works. This serves as a handy reference for the layman during more difficult discussions in ensuing chapters. Chapters four through nine recount the progress of many researchers in copying human behavior by means of machines. Here Apter examines data that have been part of the literature of computer simulation for some time, such as the Turing test, the General Problem Solver, experiments in Pattern Recognition, etc. But he also treats new or heretofore unnoticed research which undoubtedly will become elements in future philosophical controversy. One of these new cases is a program written by J. C. Loehlin called "Aldous". Aldous is the name given individually to a number of programs which interact emotionally. Loehlin so far has programmed anAldous, Decisive and Hesitant Aldouses, and, finally, Radical and Conservative Aldouses. Although the Aldous programs may sound more amusing than sober research in this area should, the details of the programming are both fascinating and philosophically significant. Loehlin, for instance, is forced to define "emotion" strictly in terms of environmental cues and elementary behavioral parameters. However, his Aldouses achieve from this a quite impressive and sophisticated repertoire of emotional responses accounting for learning, consistent emotional responses to unknown objects, mixed emotions, moods, frustration, satisfaction. Indeed, an "experienced" Aldous will exhibit behavior as unpredictable as any human. However, one still hesitates to call these reactions "emotion." Besides the obvious lack of physiological responses Aldous has no control over his environment and a merely marginal control over his emotions. Apter sees this difficulty clearly: "In particular one would like to see added the possibility of Aldous planning rather than reacting to the current situation and also the possibility of... changing not only his attitudes but also the parameters which govern the acquisition and change of attitudes." This criticism also may be levelled against other better known "personality" programs based on individual personality theories such as Heider’s or Homan’s. Apter clearly perceives theoretical difficulties in computer simulation such as these. Always, he faces them and gauges their relevance for the explanation of human behavior. The one serious shortcoming of Apter’s book is its brevity. Especially when he struggles with the theoretical implications of a particular experiment one gets the impression that he is needlessly condensing his analysis. This is particularly noticeable in the last chapter where he presents in barely-fleshed outline his position on the problem of consciousness. Despite this difficulty, however, Apter has fulfilled quite ably a promise he made in his preface that: "... this book about computers will be of interest to students of psychology and philosophy, the general reader and... perhaps also one day to computers."—J. F. (shrink)

This essay is an attempt to understand how technological metaphors, particularly computer metaphors, are relevant to moral education. After discussing various types of technological metaphors, it is argued that technological metaphors enter moral thought through their functional descriptions. The computer metaphor is then explored by turning to the hacker ethic. Analysis of this ethic reveals parallels between the experience of computer programming and the moral standards of those who are enmeshed in computer technology. This parallel (...) suggests that the hacker ethic is being pushed by a computer metaphor and its functional descriptions in a direction of individualism and systems thinking. After examining some possible implications of the computer metaphor, this essay offers suggestions concerning how technological metaphors may be critiqued. (shrink)

The prediction of protein–protein interactions based on independently obtained structural information for each interacting partner remains an important challenge in computational chemistry. Procedures where hypothetical interaction models (or decoys) are generated, then ranked using a biochemically relevant scoring function have been garnering interest as an avenue for addressing such challenges. The program PatchDock has been shown to produce reasonable decoys for modeling the association between pig alpha-amylase and the VH-domains of camelide antibody raised against it. We designed a biochemically relevant (...) method by which PatchDock decoys could be ranked in order to separate near-native structures from false positives. Several thousand steps of energy minimization were used to simulate induced fit within the otherwise rigid decoys and to rank them. We applied a partial free energy function to rank each of the binding modes, improving discrimination between near-native structures and false positives. Sorting decoys according to strain energy increased the proportion of near-native decoys near the bottom of the ranked list. Additionally, we propose a novel method which utilizes regression analysis for the selection of minimization convergence criteria and provides approximation of the partial free energy function as the number of algorithmic steps approaches infinity. (shrink)

Tylman has recently pointed out some striking conceptual and methodological analogies between philosophy and computer science. In this paper, I focus on one of Tylman’s most convincing cases, viz. the similarity between Plato’s theory of Ideas and the object-oriented programming paradigm, and analyze it in some more detail. In particular, I argue that the platonic doctrine of the Porphyrian tree corresponds to the fact that most object-oriented programming languages do not support multiple inheritance. This analysis further reinforces (...) Tylman’s point regarding the conceptual continuity between classical metaphysical theorizing and contemporary computer science. (shrink)

phenomenology and logical analysis. John Searle and Bert Dreyfus are for me two of the paradigm figures of contemporary philosophy, so I am extremely proud to have been offered the opportunity to engage with their work. The editors of The Harvard Review of Philosophy, it seems to me, have shown a keen sense of what is deep and important in our discipline by publishing extended interviews with these two influential thinkers. At the same time, writing this article (...) meant entering into a debate between Searle and Dreyfus about the priority of their respective philosophical methodologies, and this, I am afraid, is at best a no-win situation. My strategy, therefore, has been to try to engage as sympathetically as possible with the programs of each philosopher, and to draw from their work lessons that seem to me important for any philosophical account of intentionality and social reality. I have not shied away from criticism; indeed the whole paper is an extended series of criticisms of the work of both philosophers. But I hope they will recognize this for what it is: an engagement with the work of each that is based on the deepest respect and admiration. Before I jump into the fray, I should mention that I owe a personal debt of gratitude to Searle and Dreyfus as well. They were not only my two advisors in graduate school, but were the two main reasons I entered philosophy. Having become dissatisfied with the formal worlds of mathematics and computer science, their respective critiques of artificial intelligence and cognitivism showed me the rich possibilities of philosophy and struck me as a reason to become a philosopher. It is a difficult discipline, sometimes unforgiving; but I am glad I followed them into it. (shrink)

The paper critically discusses an empirical study by Mizrahi & Dickinson 2020, which analyzes in a huge data base (JSTORE) the incidence of three types of philosophical arguments. Their results are: 1. Deductive arguments were the most commeon type of argument in philosophy until the end of the 20th century. 2. Around 2008 a shift in methodology occurred, such that the indcutive arguments outweigh other types of argument. The paper, first, criticizes the empirical study as grossly false and considers (...) the - very limited - possibilities of recognition of argument types by present computer programs. (shrink)

We compare Fresco’s analysis of the Turing machine-based notion of computation with that of others, in particular with functional programming and with the reversible computing paradigm of Toffoli and others. We conclude that, although much useful philosophical work can be done by the sort of analysis that Fresco proposes, there is, nevertheless, always likely to be a number of individually viable but different accounts of computation.

The philosophy of science has lost its self-confidence. Structures in Science (2001) is an advanced textbook that explicates, updates and integrates the best insights of logical empiricism and its main critics. This "neo-classical approach" aims at providing heuristic patterns for research.The book introduces four ideal types of research programs (descriptive, explanatory, design and explicative) and reanimates the distinction between observational laws and proper theories without assuming a theory-free language. It explicates various patterns of explanation by subsumption and specification as (...) well as structures in reductive and other types of interlevel research. Its threefold analysis of theory evaluation leads to new characterizations of confirmation, empirical progress, and truth approximation. What emerges are partial analogies between progress in nomological research, presented in detail in From Instrumentalism to Constructive Realism (2000) and progress in explicative and design research. Finally, special chapters are devoted to design research programs, computational philosophy of science, the structuralist approach to theories, and research ethics.The present synopsis of Structures in Science highlights the main topics, the final emphasis being on design research and research ethics. (shrink)

In the 80's Pierangelo Miglioli, starting from motivations in the framework of Abstract Data Types and Program Synthesis, introduced semiconstructive theories, a family of large subsystems of classical theories that guarantee the computability of functions and predicates represented by suitable formulas. In general, the above computability results are guaranteed by algorithms based on a recursive enumeration of the theorems of the whole system. In this paper we present a family of semiconstructive systems, we call uniformly semiconstructive, that provide computational procedures (...) only involving formulas with bounded complexity. We present several examples of uniformly semiconstructive systems containing Harrop theories, induction principles and some well-known predicate intermediate principles. Among these, we give an account of semiconstructive and uniformly semiconstructive systems which lie between Intuitionistic and Classical Arithmetic and we discuss their constructive incompatibility. (shrink)

The rapid development of programs based on the principles of machine learning (ML) and artificial intelligence (AI) signals significant changes in the components of education, namely in the provider, the tool of transmission, and the recipient of knowledge. Historical data analysis regarding the key functions of education serves as the basis for identifying fundamental innovations introduced through AI and ML. The impact of writing, printing, and the Internet has significantly altered the tool for knowledge transmission, influencing the volume of (...) information and the number of knowledge recipients. The implementation of AI and ML transforms not only the tool but also the provider of knowledge itself, which can become impersonal thanks to the corresponding computer programs. With the historically justified increase in the volume of knowledge possessed by humanity, there is a transformation observed in education systems. This is especially true for democratic societies, where the emphasis is increasingly shifting from providing a large amount of knowledge to developing critical thinking. It has been researched that programs based on AI and ML, applying linguistic models, are capable of effectively systematizing knowledge. This lays the foundation for personalizing the entire education process for a specific knowledge recipient, without burdening the provider. However, there have been cases when such imitation misleads scientists, who perceive it as attempts at communication between programs that have an equivalence to the human level. The conclusions drawn indicate a significant transformation of the education system caused by AI and ML-based programs. However, intelligent programs are unable to evolve into independent knowledge recipients due to their inability to consciously attribute meaning to information, transforming it into knowledge. (shrink)

This article provides a detailed analysis of the transfer of a key cluster of ideas from mathematical logic to computing. We demonstrate the impact of certain of Turing’s logico-philosophical concepts from the mid-1930s on the emergence of the modern electronic computer—and so, in consequence, Turing’s impact on the direction of modern philosophy, via the computational turn. We explain why both Turing and von Neumann saw the problem of developing the electronic computer as a problem in logic, (...) and we describe their joint journey from logic to electronic computation. While much has been written about Turing’s and von Neumann’s individual contributions to the development of the computer, this article investigates less well-known terrain: their interactions and mutual influences. Along the way we argue against ‘logic skeptics’ and ‘Turing skeptics’, who claim that neither logic nor Turing played any significant role in the creation of the modern computer. (shrink)

While Bayesian models have been applied to an impressive range of cognitive phenomena, methodological challenges have been leveled concerning their role in the program of rational analysis. The focus of the current article is on computational impediments to probabilistic inference and related puzzles about empirical confirmation of these models. The proposal is to rethink the role of Bayesian methods in rational analysis, to adopt an independently motivated notion of rationality appropriate for computationally bounded agents, and to explore broad (...) conditions under which Bayesian agents would be rational. The proposal is illustrated with a characterization of costs inspired by thermodynamics. (shrink)

The philosophy of science has lost its self-confidence, witness the lack of advanced textbooks in contrast to the abundance of elementary textbooks. Structures in Science is an advanced textbook that explicates, updates, accommodates, and integrates the best insights of logical-empiricism and its main critics. This `neo-classical approach' aims at providing heuristic patterns for research. The book introduces four ideal types of research programs and reanimates the distinction between observational laws and proper theories. It explicates various patterns of explanation by (...) subsumption and specification as well as structures in reductive and other types of interlevel research. Its analysis of theory evaluation leads to new characterizations of confirmation, empirical progress, and pseudoscience. Partial analogies between progress in nomological research and progress in explicative and design research emerge. Finally, special chapters are devoted to design research programs, computational philosophy of science, the structuralist approach to theories, and research ethics. (shrink)

This paper provides a review of Raymond Turner’s book Computational Artefacts. Towards a Philosophy of Computer Science. Focus is made on the definition of program correctness as the twofold problem of evaluating whether both the symbolic program and the physical implementation satisfy a set of specifications. The review stresses how these are not two separate problems. First, it is highlighted how formal proofs of correctness need to rely on the analysis of physical computational processes. Secondly, it is (...) underlined how software testing requires considering the formal relations holding between the specifications and the symbolic program. Such a mutual dependency between formal and empirical program verification methods is finally shown to influence the debate on the epistemological status of computer science. (shrink)

The notion of “bounded rationality” was introduced by Simon as an appropriate framework for explaining how agents reason and make decisions in accordance with their computational limitations and the characteristics of the environments in which they exist (seen metaphorically as two complementary scissor blades).We elaborate on how bounded rationality is usually conceived in psychology and on its relationship with logic. We focus on the relationship between heuristics and some non-monotonic logical systems. These two categories of cognitive tools share fundamental features. (...) As a step further, we show that in some cases heuristics themselves can be formalized from this logic perspective. We have therefore two main aims: on the one hand, to demonstrate the relationship between the bounded rationality programme and logic, understood in a broad sense; on the other hand, to provide logical tools of analysis of already known heuristics. This may lead to results such as the characterization of fast and frugal binary trees in terms of their associated logic program here provided. (shrink)

ABSTRACT I argue that this examination and appreciation for the shift to abductive reasoning should be extended to the intersection of neuroscience and novel brain-computer interfaces too. This paper highlights the implications of applying abductive reasoning to personalized implantable neurotechnologies. Then, it explores whether abductive reasoning is sufficient to justify insurance coverage for devices absent widespread clinical trials, which are better applied to one-size-fits-all treatments. INTRODUCTION In contrast to the classic model of randomized-control trials, often with a large number (...) of subjects enrolled, precision medicine attempts to optimize therapeutic outcomes by focusing on the individual.[i] A recent publication highlights the strengths and weakness of both traditional evidence-based medicine and precision medicine.[ii] Plus, it outlines a tension in the shift from evidence-based medicine’s inductive reasoning style (the collection of data to postulate general theories) to precision medicine’s abductive reasoning style (the generation of an idea from the limited data available).[iii] The paper’s main example is the application of precision medicine for the treatment of cancer.[iv] I argue that this examination and appreciation for the shift to abductive reasoning should be extended to the intersection of neuroscience and novel brain-computer interfaces too. As the name suggests, brain-computer interfaces are a significant advancement in neurotechnology that directly connects someone’s brain to external or implanted devices.[v] Among the various kinds of brain-computer interfaces, adaptive deep brain stimulation devices require numerous personalized adjustments to their settings during the implantation and computation stages in order to provide adequate relief to patients with treatment-resistant disorders. What makes these devices unique is how adaptive deep brain stimulation integrates a sensory component to initiate the stimulation. While not commonly at the level of sophistication as self-supervising or generative large language models,[vi] they currently allow for a semi-autonomous form of neuromodulation. This paper highlights the implications of applying abductive reasoning to personalized implantable neurotechnologies. Then, it explores whether abductive reasoning is sufficient to justify insurance coverage for devices absent widespread clinical trials, which are better applied to one-size-fits-all treatments.[vii] ANALYSIS I. The State of Precision Medicine in Oncology and the Epistemological Shift While a thorough overview of precision medicine for the treatment of cancer is beyond the scope of this article, its practice can be roughly summarized as identifying clinically significant characteristics a patient possesses (e.g., genetic traits) to land on a specialized treatment option that, theoretically, should benefit the patient the most.[viii] However, in such a practice of stratification patients fall into smaller and smaller populations and the quality of evidence that can be applied to anyone outside these decreases in turn.[ix] As inductive logic helps to articulate, the greater the number of patients that respond to a particular therapy the higher the probability of its efficacy. By straying from this logical framework, precision medicine opens the treatment of cancer to more uncertainty about the validity of these approaches to the resulting disease subcategories.[x] Thus, while contemporary medical practices explicitly describe some treatments as “personalized”, they ought not be viewed as inherently better founded than other therapies.[xi] A relevant contemporary case of precision medicine out of Norway focuses on the care of a patient with cancer between the ventricles of the heart and esophagus, which had failed to respond to the standard regimen of therapies over four years.[xii] In a last-ditch effort, the patient elected to pay out-of-pocket for an experimental immunotherapy (nivolumab) at a private hospital. He experienced marked improvements and a reduction in the size of the tumor. Understandably, the patient tried to pursue further rounds of nivolumab at a public hospital. However, the hospital initially declined to pay for it given the “lack of evidence from randomised clinical trials for this drug relating to this [patient’s] condition.”[xiii] In rebuttal to this claim, the patient countered that he was actually similar to a subpopulation of patients who responded in “open‐label, single arm, phase 2 studies on another immune therapy drug” (pembrolizumab).[xiv] Given this interpretation of the prior studies and the patient’s response, further rounds of nivolumab were approved. Had the patient not had improvements in the tumor’s size following a round of nivolumab, then pembrolizumab’s prior empirical evidence in isolation would have been insufficient, inductively speaking, to justify his continued use of nivolumab.[xv] The case demonstrates a shift in reasoning from the traditional induction to abduction. The phenomenon of ‘cancer improvement’ is considered causally linked to nivolumab and its underlying physiological mechanisms.[xvi] However, “the weakness of abductions is that there may always be some other better, unknown explanation for an effect. The patient may for example belong to a special subgroup that spontaneously improves, or the change may be a placebo effect. This does not mean, however, that abductive inferences cannot be strong or reasonable, in the sense that they can make a conclusion probable.”[xvii] To demonstrate the limitations of relying on the abductive standard in isolation, commentators have pointed out that side effects in precision medicine are hard to rule out as being related to the initial intervention itself unless trends from a group of patients are taken into consideration.[xviii] As artificial intelligence (AI) assists the development of precision medicine for oncology, this uncertainty ought to be taken into consideration. The implementation of AI has been crucial to the development of precision medicine by providing a way to combine large patient datasets or a single patient with a large number of unique variables with machine learning to recommend matches based on statistics and probability of success upon which practitioners can base medical recommendations.[xix] The AI is usually not establishing a causal relationship[xx] – it is predicting. So, as AI bleeds into medical devices, like brain-computer interfaces, the same cautions about using abductive reasoning alone should be carried over. II. Responsive Neurostimulation, AI, and Personalized Medicine Like precision medicine in cancer treatment, computer-brain interface technology similarly focuses on the individual patient through personalized settings. In order to properly expose the intersection of AI, precision medicine, abductive reasoning, and implantable neurotechnologies, the descriptions of adaptive deep brain stimulation systems need to deepen.[xxi] As a broad summary of adaptive deep brain stimulation, to provide a patient with the therapeutic stimulation, a neural signal, typically referred to as a local field potential,[xxii] must first be detected and then interpreted by the device. The main adaptive deep brain stimulation device with premarket approval, the NeuroPace Responsive Neurostimulation system, is used to treat epilepsy by detecting and storing “programmer-defined phenomena.”[xxiii] Providers can optimize the detection settings of the device to align with the patient’s unique electrographic seizures as well as personalize the reacting stimulation’s parameters.[xxiv] The provider adjusts the technology based on trial and error. One day machine learning algorithms will be able to regularly aid this process in myriad ways, such as by identifying the specific stimulation settings a patient may respond to ahead of time based on their electrophysiological signatures.[xxv] Either way, with AI or programmers, adaptive neurostimulation technologies are individualized and therefore operate in line with precision medicine rather than standard treatments based on large clinical trials. Contemporary neurostimulation devices are not usually sophisticated enough to be prominent in AI discussions where the topics of neural networks, deep learning, generative models, and self-attention dominate the conversation. However, implantable high-density electrocorticography arrays (a much more sensitive version than adaptive deep brain stimulation systems use) have been used in combination with neural networks to help patients with neurologic deficits from a prior stroke “speak” through a virtual avatar.[xxvi] In some experimental situations, algorithms are optimizing stimulation parameters with increasing levels of independence.[xxvii] An example of neurostimulation that is analogous to the use of nivolumab in Norway surrounds a patient in the United States who was experiencing both treatment-resistant OCD and temporal lobe epilepsy.[xxviii]Given the refractory nature of her epilepsy, implantation of an adaptive deep brain stimulation system was indicated. As a form of experimental therapy, her treatment-resistant OCD was also indicated for the off-label use of an adaptive deep brain stimulation set-up. Another deep brain stimulation lead, other than the one implanted for epilepsy, was placed in the patient’s right nucleus accumbens and ventral pallidum region given the correlation these nuclei had with OCD symptoms in prior research. Following this, the patient underwent “1) ambulatory, patient-initiated magnet-swipe storage of data during moments of obsessive thoughts; (2) lab-based, naturalistic provocation of OCD-related distress (naturalistic provocation task); and (3) lab-based, VR [virtual reality] provocation of OCD-related distress (VR provocation task).”[xxix] Such signals were used to identify when to deliver the therapeutic stimulation in order to counter the OCD symptoms. Thankfully, following the procedure and calibration the patient exhibited marked improvements in their OCD symptoms and recently shared her results publicly.[xxx] In both cases, there is a similar level of abductive justification for the efficacy of the delivered therapy. In the case study in which the patient was treated with adaptive deep brain stimulation, they at least had their neural activity tested in various settings to determine the optimum parameters for treatment to avoid them being based on guesswork. Additionally, the adaptive deep brain stimulation lead was already placed before the calibration trials were conducted, meaning that the patient had already taken on the bulk of the procedural risk before the efficacy could be determined. Such an efficacy test could have been replicated in the first patient’s cancer treatment, had it been biopsied and tested against the remaining immunotherapies in vitro. Yet, in the case of cancer with few options, one previous dose of a drug that appeared to work on the patient may justify further doses. However, as the Norwegian case presents, corroboration with known responses to a similar drug (from a clinical trial) could be helpful to validate the treatment strategy. (It should be noted that both patients were resigned to these last resort options regardless of the efficacy of treatment.) There are some elements of inductive logic seen with adaptive deep brain stimulation research in general. For example, abductively the focus could be that patient X’s stimulation parameters are different from patient Y’s and patient Z’s. In contrast, when grouped as subjects who obtained personalized stimulation, patients X, Y, and Z demonstrate an inductive aspect to this approach’s safety and/or efficacy. The OCD case holds plenty of abductive characteristics in line with precision medicine’s approach to treating cancer and as more individuals try the method, there will be additional data. With the gradual integration of AI into brain-computer interfaces in the name of efficacy, this reliance on abduction will continue, if not grow, over time. Moving forward, if a responsive deep brain stimulation treatment is novel and individualized (like the dose of nivolumab) and there is some other suggestion of efficacy (like clinical similarities to other patients in the literature), then it may justify insurance coverage for the investigative intervention, absent other unrelated reasons to deny it. III. Ethical Implications and Next Steps While AI’s use in oncology and neurology is not yet as prominent as its use in other fields (e.g., radiology), it appears to be on the horizon for both.[xxxi] AI can be found in both the functioning of the neurotechnologies as well as the implementation of precision medicine. The increasing use of AI may serve to further individualize both oncologic and neurological therapies. Given these implications and the handful of publications cited in this article, it is important to have a nuanced evaluation of how these treatments, which heavily rely on abductive justification, ought to be managed. The just use an abductive approach may be difficult as AI infused precision medicine is further pursued. At baseline, such technology relies on a level of advanced technology literacy among the general public and could exclude populations who lack access to basic technological infrastructure or know-how from participation.[xxxii] Even among nations with adequate infrastructure, as more patients seek out implantable neurotechnologies, which require robust healthcare resources, the market will favor patient populations that can afford this complex care.[xxxiii] If patients already have the means to pay for an initial dose/use of a precision medicine product out of pocket, should insurance providers be required to cover subsequent treatments?[xxxiv] That is, if a first dose of a cancer drug or a deep brain stimulator over its initial battery life is successful, patients may feel justified in having the costs of further treatments covered. The Norwegian patient’s experience implies there is a precedent for the idea that some public insurance companies ought to cover successful cancer therapies, however, insurance companies may not all see themselves as obligated to cover neurotechnologies that rely on personalized settings or that are based on precision/abductive research more than on clinical trials. CONCLUSION The fact that the cases outlined above rely on abductive style of reasoning implies that there may not be as strong a justification for coverage by insurance, as they are both experimental and individualized, when compared to the more traditional large clinical trials in which groups have the same or a standardized protocol (settings/doses). If a study is examining the efficacy of a treatment with a large cohort of patients or with different experimental groups/phases, insurance companies may conclude that the resulting symptom improvements are more likely to be coming from the devices themselves. A preference for inductive justification may take priority when ruling in favor of funding someone’s continued use of an implantable neurostimulator. There are further nuances to this discussion surrounding the classifications of these interventions as research versus clinical care that warrant future exploration, since such a distinction is more of a scale[xxxv] than binary and could have significant impacts on the “right-to-try” approach to experimental therapies in the United States.[xxxvi] Namely, given the inherent limitations of conducting large cohort trials for deep brain stimulation interventions on patients with neuropsychiatric disorders, surgically innovative frameworks that blend abductive and inductive methodologies, like with sham stimulation phases, have traditionally been used.[xxxvii] Similarly, for adaptive brain-computer interface systems, if there are no large clinical trials and instead only publications that demonstrate that something similar worked for someone else, then, in addition to the evidence that the first treatment/dose worked for the patient in question, the balance of reasoning would be valid and arguably justify insurance coverage. As precision approaches to neurotechnology become more common, frameworks for evaluating efficacy will be crucial both for insurance coverage and for clinical decision making. ACKNOWLEDGEMENT This article was originally written as an assignment for Dr. Francis Shen’s “Bioethics & AI” course at Harvard’s Center for Bioethics. I would like to thank Dr. Shen for his comments as well as my colleagues in the Lázaro-Muñoz Lab for their feedback. - [i] Jonathan Kimmelman and Ian Tannock, “The Paradox of Precision Medicine,” Nature Reviews. Clinical Oncology 15, no. 6 (June 2018): 341–42, https://doi.org/10.1038/s41571-018-0016-0. [ii] Henrik Vogt and Bjørn Hofmann, “How Precision Medicine Changes Medical Epistemology: A Formative Case from Norway,” Journal of Evaluation in Clinical Practice 28, no. 6 (December 2022): 1205–12, https://doi.org/10.1111/jep.13649. [iii] David Barrett and Ahtisham Younas, “Induction, Deduction and Abduction,” Evidence-Based Nursing 27, no. 1 (January 1, 2024): 6–7, https://doi.org/10.1136/ebnurs-2023-103873. [iv] Vogt and Hofmann, “How Precision Medicine Changes Medical Epistemology,” 1208. [v] Wireko Andrew Awuah et al., “Bridging Minds and Machines: The Recent Advances of Brain-Computer Interfaces in Neurological and Neurosurgical Applications,” World Neurosurgery, May 22, 2024, S1878-8750(24)00867-2, https://doi.org/10.1016/j.wneu.2024.05.104. [vi] Mark Riedl, “A Very Gentle Introduction to Large Language Models without the Hype,” Medium (blog), May 25, 2023, https://mark-riedl.medium.com/a-very-gentle-introduction-to-large-language-models-without-the-hype-5 f67941fa59e. [vii] David E. Burdette and Barbara E. Swartz, “Chapter 4 - Responsive Neurostimulation,” in Neurostimulation for Epilepsy, ed. Vikram R. Rao (Academic Press, 2023), 97–132, https://doi.org/10.1016/B978-0-323-91702-5.00002-5. [viii] Kimmelman and Tannock, 2018. [ix] Kimmelman and Tannock, 2018. [x] Simon Lohse, “Mapping Uncertainty in Precision Medicine: A Systematic Scoping Review,” Journal of Evaluation in Clinical Practice 29, no. 3 (April 2023): 554–64, https://doi.org/10.1111/jep.13789. [xi] Kimmelman and Tannock, “The Paradox of Precision Medicine.” [xii] Vogt and Hofmann, 1206. [xiii] Vogt and Hofmann, 1206. [xiv] Vogt and Hofmann, 1206. [xv] Vogt and Hofmann, 1207. [xvi] Vogt and Hofmann, 1207. [xvii] Vogt and Hofmann, 1207. [xviii] Vogt and Hofmann, 1210. [xix] Mehar Sahu et al., “Chapter Three - Artificial Intelligence and Machine Learning in Precision Medicine: A Paradigm Shift in Big Data Analysis,” in Progress in Molecular Biology and Translational Science, ed. David B. Teplow, vol. 190, 1 vols., Precision Medicine (Academic Press, 2022), 57–100, https://doi.org/10.1016/bs.pmbts.2022.03.002. [xx] Stefan Feuerriegel et al., “Causal Machine Learning for Predicting Treatment Outcomes,” Nature Medicine 30, no. 4 (April 2024): 958–68, https://doi.org/10.1038/s41591-024-02902-1. [xxi] Sunderland Baker et al., “Ethical Considerations in Closed Loop Deep Brain Stimulation,” Deep Brain Stimulation 3 (October 1, 2023): 8–15, https://doi.org/10.1016/j.jdbs.2023.11.001. [xxii] David Haslacher et al., “AI for Brain-Computer Interfaces,” 2024, 7, https://doi.org/10.1016/bs.dnb.2024.02.003. [xxiii] Burdette and Swartz, “Chapter 4 - Responsive Neurostimulation,” 103–4; “Premarket Approval (PMA),” https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpma/pma.cfm?id=P100026. [xxiv] Burdette and Swartz, “Chapter 4 - Responsive Neurostimulation,” 104. [xxv] Burdette and Swartz, 126. [xxvi] Sean L. Metzger et al., “A High-Performance Neuroprosthesis for Speech Decoding and Avatar Control,” Nature 620, no. 7976 (August 2023): 1037–46, https://doi.org/10.1038/s41586-023-06443-4. [xxvii] Hao Fang and Yuxiao Yang, “Predictive Neuromodulation of Cingulo-Frontal Neural Dynamics in Major Depressive Disorder Using a Brain-Computer Interface System: A Simulation Study,” Frontiers in Computational Neuroscience 17 (March 6, 2023), https://doi.org/10.3389/fncom.2023.1119685; Mahsa Malekmohammadi et al., “Kinematic Adaptive Deep Brain Stimulation for Resting Tremor in Parkinson’s Disease,” Movement Disorders 31, no. 3 (2016): 426–28, https://doi.org/10.1002/mds.26482. [xxviii] Young-Hoon Nho et al., “Responsive Deep Brain Stimulation Guided by Ventral Striatal Electrophysiology of Obsession Durably Ameliorates Compulsion,” Neuron 0, no. 0 (October 20, 2023), https://doi.org/10.1016/j.neuron.2023.09.034. [xxix] Nho et al. [xxx] Nho et al.; Erik Robinson, “Brain Implant at OHSU Successfully Controls Both Seizures and OCD,” OHSU News, accessed March 3, 2024, https://news.ohsu.edu/2023/10/25/brain-implant-at-ohsu-successfully-controls-both-seizures-and-ocd. [xxxi] Awuah et al., “Bridging Minds and Machines”; Haslacher et al., “AI for Brain-Computer Interfaces.” [xxxii] Awuah et al., “Bridging Minds and Machines.” [xxxiii] Sara Green, Barbara Prainsack, and Maya Sabatello, “The Roots of (in)Equity in Precision Medicine: Gaps in the Discourse,” Personalized Medicine 21, no. 1 (January 2024): 5–9, https://doi.org/10.2217/pme-2023-0097. [xxxiv] Green, Prainsack, and Sabatello, 7. [xxxv] Robyn Bluhm and Kirstin Borgerson, “An Epistemic Argument for Research-Practice Integration in Medicine,” The Journal of Medicine and Philosophy: A Forum for Bioethics and Philosophy of Medicine 43, no. 4 (July 9, 2018): 469–84, https://doi.org/10.1093/jmp/jhy009. [xxxvi] Vijay Mahant, “‘Right-to-Try’ Experimental Drugs: An Overview,” Journal of Translational Medicine 18 (June 23, 2020): 253, https://doi.org/10.1186/s12967-020-02427-4. [xxxvii] Michael S. Okun et al., “Deep Brain Stimulation in the Internal Capsule and Nucleus Accumbens Region: Responses Observed during Active and Sham Programming,” Journal of Neurology, Neurosurgery & Psychiatry 78, no. 3 (March 1, 2007): 310–14, https://doi.org/10.1136/jnnp.2006.095315. (shrink)

This work explores the conceptual and empirical issues of the concept of knowledge and its relation to the pattern of our belief change, from formal and experimental perspectives. Part I gives an analysis of knowledge (called Sustainability) that is formally represented and naturalistically plausible at the same time, which is claimed to be a synthesized view of knowledge, covering not only empirical knowledge, but also knowledge of future, practical knowledge, mathematical knowledge, knowledge of general facts. Part II tries to (...) formalize the natural pattern of belief change assumed in Sustainability in terms of a specific formal theory of belief change, after carefully examining the notions of belief, belief change, and Information, from which the cognitive function F in Chapter 3 is actually constructed, which is later implemented by a computer program and its behavior against random input is demonstrated. In Part III we proceed to examine the analysis empirically. In particular, we will investigate Sustainability from the developmental point of view. We first justify experimental approach in philosophy as a legitimate method of philosophical investigation, and then developmental approach in particular. The specific proposal of experiment here is what we call the Gettier Task, an analogue of the famous false-belief task in developmental psychology, which has been much discussed in philosophy of mind. Two versions of the Gettier Task were tested on children aged from 6 to 12, and we claim that the data obtained empirically supports our analysis of knowledge, or Sustainability. (shrink)

Within artificial intelligence and the philosophy of mind,there is considerable disagreement over the relationship between anagent's body and its capacity for intelligent behavior. Some treatthe body as peripheral and tangential to intelligence; others arguethat embodiment and intelligence are inextricably linked. Softwareagents–-computer programs that interact with software environmentssuch as the Internet–-provide an ideal context in which to studythis tension. I develop a computational framework for analyzingembodiment. The framework generalizes the notion of a body beyondmerely having a physical presence. My (...) analysis sheds light oncertain claims made about the relevance of the body to intelligence,as well as on embodiment in software worlds. (shrink)

The project of this Précis de philosophie de la logique et des mathématiques (vol. 1 under the direction of F. Poggiolesi and P. Wagner, vol. 2 under the direction of A. Arana and M. Panza) aims to offer a rich, systematic and clear introduction to the main contemporary debates in the philosophy of mathematics and logic. The two volumes bring together the contributions of thirty researchers (twelve for the philosophy of logic and eighteen for the philosophy of (...) mathematics), specialists in the history or philosophy of logic or mathematics. Each volume consists of ten chapters; each chapter is about forty pages, is independent from the others and deals with a particular philosophical question about logic or mathematics. The objective is to offer to the French-speaking reader a reference book. On many of the issues addressed, this Précis offers the first clear and thorough synthesis that is available in French. -/- This book is intended for third year / master's students, but also for teachers of philosophy in secondary or higher education, for logicians and mathematicians willing to take a philosophical look at the fundamental concepts of their discipline, and for any reader interested in the basic issues discussed in the philosophy of logic and mathematics. Each chapter is self-contained, although some basic training in logic (or in mathematics, as the case may be) is required. The book should be looked at as a comprehensive framework for contemporary discussions in the philosophy of logic and mathematics, while also giving some guidance on the disciplinary content required by these discussions. -/- The first three chapters of the present volume (on the philosophy of mathematics) are devoted to the history of the philosophy of mathematics: from antiquity to the modern era, and from this period to the foundational crisis of the nineteenth century, to the twentieth century. Next, four chapters deal with the crucial questions for the philosophy of mathematics of the twentieth century: the opposition and/or comparison of set theory and category theory as foundational frameworks for mathematics; mathematical constructivism; the analysis of calculability; and Benaceraff's problem. The two following chapters are focused on the philosophy of mathematical practice, by treating the notion of ideals of proof, in particular explanation and purity, and the notion of informal proof and the use of visual artifacts in mathematical argumentation. Finally the last chapter deals with the applicability of mathematics, including the role of probability. -/- In Chapter 1, Sébastien Maronne and David Rabouin present the history of the philosophy of mathematics from antiquity to the modern era. The goal is to show that the philosophy of mathematics has a history as ancient as mathematics and philosophy itself, a history largely continuous with the latter, often by staying out of sync with respect to the former. In Chapter 2, Sébastien Gandon discusses the question of the foundations of mathematics from Kant to the end of the nineteenth century. By tying this question to Kant, he is able to show that the discussion of the foundations of mathematics that occupied a large part of these two centuries has much older roots. Accent is placed on the differences between the framework proposed by Kant for making sense of classical mathematics and the use made by Frege and then Russell of logical tools for giving a different reading of arithmetic and real analysis. The link between between these different conceptions and the evolution of mathematics itself is also underlined. -/- Chapter 3, written by Hourya Benis-Sinaceur and Mirna Džamonja, deals with the evolution of mathematics in the twentieth century, notably the structuralist turn of Dedekind, E. Noether, and Bourbaki, and certain more recent developments. They devote themselves, among other things, to explaining how these developments can be understood as responses to and extensions of questions posed during the foundational debate of the immediately preceding era. -/- In Chapter 4, Jean-Pierre Marquis and Jean-Jacques Szczeciniarz discuss the different foundational options coming from set theory and category theory. They discuss, among other things, Lawvere's program to replace ZF with an axiomatization of the category of sets, the latter judged better adapted to the needs of contemporary mathematics. Different reactions, as much philosophical as technical, raised by this program are also taken into account. -/- Constructive mathematics are the object of Chapter 5, in which Gerhard Heinzmann and Mark van Atten examine in a critical and comparative manner a variety of constructivisms, including intuitionism, constructive type theory, predicativism, and finitism. -/- In Chapter 6, Guido Gherardi and Maël Pegny present computability theory, stressing its philosophical consequences. They discuss the basic concepts of this theory, for the computability of both the natural numbers and the real numbers. They then tackle the theory of computational complexity. -/- In Chapter 7, Andrea Sereni and Fabrice Pataut present Benacerraf's problem, opposing every possible account of mathematical knowledge to the availability of a theory of truth founded on an abstract ontology of mathematical objects. They give an overview of the original problem and its reformulation by Hartry Field, and reconstruct the debate that this problem has raised, by meeting at once the major opposing positions today of the analytic affiliation of the philosophy of mathematics, as much those on the platonist side as on that of nominalism. -/- In Chapter 8, Valeria Giardino and Yacin Hamami treat certain crucial aspects of the philosophy of mathematical practice. They deal in particular with the notion of an informal proof and on the role of artifacts in mathematical reasoning. They consider how such proofs, making use of these means, can contribute to the understanding of theorems and mathematical theories, thanks also to the role of visualisation and the aid of computer tools. -/- Why do mathematics often give several proofs of the same theorem? This is the question that Andrew Arana raises in Chapter 9, introducing the notion of an epistemic ideal and discuss two such ideals, the explanatoriness and purity of proof. -/- Chapter 10 is devoted to the applicability of mathematics in the study of empirical phenomena. After summarizing the history of this question, going back to Plato and Aristotle and passing by Mill and Kant, Daniele Molinini and Marco Panza reconstruct the contemporary debate, in relation, among other things, to questions raised in the philosophy of science. -/- Two appendices complete the volume. In the first Frédéric Patras treats the French tradition in philosophy of mathematics of the 20th century. In the second, Maria Carla Galavotti discusses the notion of probability and its use in science. (shrink)

In recent years, the "science of science" has combined computational methods with novel data sources in order to understand the dynamics of research communities. As the name suggests, science of science is primarily focused on science and technology, with less attention to the humanities. However, many of the questions investigated by science of science are also relevant to academic philosophy: To what extent can the discipline be divided into subfields with different methods and topics? How are prestige and credit (...) distributed across the discipline? And how do these factors interact with other factors, such as gender, to shape job market outcomes? In this paper, we provide some empirically-informed answers to these questions by applying computational methods to job market data for Anglophone academic philosophy. We find, first, evidence that is consistent with the analytic-continental divide, but is also consistent with other, more complex ways of organizing academic philosophy into distinct intellectual traditions; second, a clear prestige hierarchy, dividing PhD programs into two distinct prestige categories; and third, evidence that gender, prestige, and country have notable effects on academic job market outcomes for recent philosophy PhDs. (shrink)

The article describes the state of affairs and prospects for research and development in the domain of active use of digitalization and computer programming in the study of the Indian intellectual tradition. The term “Digital Indology” is used this term as an analogy of the expression “Digital Humanities”. Here, it will be understood as the reception and study of philosophical and other classical texts of Ancient and Medieval India with the usage of digital technologies, mathematical statistics, contextual analysis (...) methods alongside with the traditional well-established approaches of philosophy and the humanities. The main trends in the development of digital philosophical Indology are being outlined The use of mathematical statistics and the methods of contextual analysis are suggested as typical auxiliary methods while analyzing classical texts of India. We do not close our eyes to the possible hidden dangers of a possible fascination with digital methods. Our brief manifesto of Digital Philosophical Indology is to define it exclusively being tool for classical Indological research, which retain its specific philosophical objectives and goals. (shrink)

This article critically examines one of the most prevalent metaphors in modern biology, namely the machine conception of the organism (MCO). Although the fundamental differences between organisms and machines make the MCO an inadequate metaphor for conceptualizing living systems, many biologists and philosophers continue to draw upon the MCO or tacitly accept it as the standard model of the organism. This paper analyses the specific difficulties that arise when the MCO is invoked in the study of development and evolution. In (...) developmental biology the MCO underlies a logically incoherent model of ontogeny, the genetic program, which serves to legitimate three problematic theses about development: genetic animism, neo-preformationism, and developmental computability. In evolutionary biology the MCO is responsible for grounding unwarranted theoretical appeals to the concept of design as well as to the interpretation of natural selection as an engineer, which promote a distorted understanding of the process and products of evolutionary change. Overall, it is argued that, despite its heuristic value, the MCO today is impeding rather than enabling further progress in our comprehension of living systems. (shrink)

The amount of personal data now collected through contemporary marketing practices is indicative of the shifting landscape of contemporary capitalism. Loyalty programs can be seen as one exemplar of this, using the ‘add-ons’ of ‘points’ and ‘miles’ to entice consumers into divulging a range of personal information. These consumers are subject to surveillance practices that have digitally identified them as significant in the eyes of a corporation, yet they are also part of a feedback loop subject to ongoing analysis. (...) This paper focuses on this analysis as the ‘cultural circuit’ of loyalty programs—the ongoing process of meaning-making in this form of contemporary marketing—as exemplary of what Nigel Thrift calls “soft capitalism”( 1997, 2005 ). Loyalty programs engage consumers in an ongoing ‘relationship’ with a corporation, yet it is one predicated on the collection and analysis of personal data in order to identify, maintain and increase profits from these consumer ‘relationships.’ This paper looks at ways of knowing, application and revision in the cultural circuit of loyalty program marketing as a form of reflexive marketing and raises concerns about consumer subjectivity in the context consumer culture that mediates much of contemporary experience. These technologies and practices continually adapt and adjust to strategically act toward consumers as a form of consumer surveillance based on an increasingly intensive and nuanced knowledge of their behaviours. (shrink)

Joel Feinberg : In Memoriam. Preface. Part I: INTRODUCTION TO THE NATURE AND VALUE OF PHILOSOPHY. 1. Joel Feinberg: A Logic Lesson. 2. Plato: "Apology." 3. Bertrand Russell: The Value of Philosophy. PART II: REASON AND RELIGIOUS BELIEF. 1. The Existence and Nature of God. 1.1 Anselm of Canterbury: The Ontological Argument, from Proslogion. 1.2 Gaunilo of Marmoutiers: On Behalf of the Fool. 1.3 L. Rowe: The Ontological Argument. 1.4 Saint Thomas Aquinas: The Five Ways, from Summa Theologica. (...) 1.5 Samuel Clarke: A Modern Formulation of the Cosmological Argument. 1.6 William L. Rowe: The Cosmological Argument. 1.7 William Paley: The Argument from Design. 1.8 Michael Ruse: The Design Argument. 1.9 David Hume: Dialogues Concerning Natural Religion. 2. The Problem of Evil. 2.1 Fyodor Dostoyevsky: Rebellion. 2.2 J. L. Mackie: Evil and Omnipotence. 2.3 Peter van Inwagen: The Argument from Evil. 2.4 Michael Murray and Michael Rea: The Argument from Evil. 2.5 B. C. Johnson: God and the Problem of Evil. 3. Reason and Faith. 3.1 W. K. Clifford: The Ethics of Belief. 3.2 William James: The Will to Believe. 3.3 Kelly James Clark: Without Evidence or Argument. 3.4 Blaise Pascal: The Wager. 3.5 Lawrence Shapiro: Miracles and Justification. 3.6 Simon Blackburn: Infini-Rien. Part III. HUMAN KNOWLEDGE: ITS GROUNDS AND LIMITS. 1. Skepticism. 1.1 John Pollock: A Brain in a Vat. 1.2 Michael Huemer: Three Skeptical Arguments. 1.3 Robert Audi: Skepticism. 2. The Nature and Value of Knowledge. 2.1 Plato: Knowledge as Justified True Belief. 2.2 Edmund Gettier: Is Justified True Belief Knowledge? 2.3 James Cornman, Keith Lehrer, and George Pappas: An Analysis of Knowledge. 2.4 Gilbert Ryle: Knowing How and Knowing That. 2.5 Plato: "Meno". 2.6 Linda Zagzebski, Epistemic Good and The Good Life. 3. Our Knowledge of the External World. 3.1 Bertrand Russell: Appearance and Reality and the Existence of Matter. 3.2 René Descartes: Meditations on First Philosophy. 3.3 John Locke: The Causal Theory of Perception. 3.4 George Berkeley: Of the Principles of Human Knowledge. 3.5 G. E. Moore: Proof of an External World. 4. The Methods of Science. 4.1 David Hume: An Inquiry Concerning Human Understanding. 4.2 Wesley C. Salmon: An Encounter with David Hume. 4.3 Karl Popper: Science: Conjectures and Refutations. 4.4 Philip Kitcher: Believing Where We Cannot Prove. Part IV: MIND AND ITS PLACE IN NATURE. 1. The Mind-Body Problem. 1.1 Brie Gertler: In Defense of Mind--Body Dualism. 1.2 Frank Jackson: The Qualia Problem. 1.3 David Papineau: The Case for Materialism. 1.4 Paul Churchland: Functionalism and Eliminative Materialism. 2. Can Non-Humans Think? 2.1 Alan Turing: Computing Machinery and Intelligence. 2.2 John R. Searle: Minds, Brains, and Programs. 2.3 William G. Lycan: Robots and Minds. 3. Personal Identity and the Survival of Death. 3.1 John Locke: The Prince and the Cobbler. 3.2 Thomas Reid: Of Mr. Locke’s Account of Our Personal Identity. 3.3 David Hume: The Self. 3.4 Derek Parfit: Divided Minds and the Nature of Persons. 3.5 Shelly Kagan: What Matters. 3.6 John Perry: A Dialogue on Personal Identity and Immortality. Part V: DETERMINISM, FREE WILL, AND RESPONSIBILITY. 1. Libertarianism: The Case for Free Will and Its Incompatibility with Determinism. 1.1 Roderick M. Chisholm: Human Freedom and the Self. 1.2 Robert Kane: Free Will: Ancient Dispute, New Themes. 2. Hard Determinism: The Case for Determinism and its Incompatibility with Its Incompatibility with Any Important Sense of Free Will. 2.1 James Rachels: The case against Free Will. 2.2 Derk Pereboom: Why We Have No Free Will and Can Live Without It. 3. Compatibilism: The Case for Determinism and Its Compatibility with the Most Important Sense of Free Will. 3.1 David Hume: Of Liberty and Necessity. 3.2 Helen Beebee: Compatibilism and the Ability to do Otherwise. 4. Freedom and Moral Responsibility. 4.1 Galen Strawson: Luck Swallows Everything. 4.2 Harry Frankfurt: Alternate Possibilities and Moral Responsibility. 4.3 Thomas Nagel: Moral Luck. 4.4 Susan Wolf: Sanity and the Metaphysics of Responsibility. Part VI: MORALITY AND ITS CRITICS. 1. Changes to Morality. 1.1 Joel Feinberg: Psychological Egoism. 1.2 Plato: The Immoralist’s Challenge. 1.3 Friedrich Nietzche: Master and Slave Morality. 1.4 Richard Joyce: The Evolutionary Debunking of Morality. 2. Proposed Standards and Right of Conduct. 2.1 Russ Shafer-Landau: Ethical Subjectivism. 2.2 Mary Midgley: Trying Out One’s New Sword. 2.3 Aristotle: Virtue and the Good Life. 2.4 Thomas Hobbes: Leviathan. 2.5 Plato: Euthyphro. 2.6 Immanuel Kant: The Good Will and the Categorical Imperative. 2.7 J.S. Mill: Utilitarianism, Chapters 2 and 4. 2.8 W. D. Ross: What Makes Right Acts Right? 2.9 Hilde Lindemann: What Is Feminist Ethics? 3. Ethical Problems. 3.1 Kwame Anthony Appiah: What Will Future Generations Condemn Us For? 3.2 Peter Singer: Famine, Affluence and Morality. 3.3 John Harris: The Survival Lottery. 3.4 James Rachels: Active and Passive Euthanasia. 3.5 Mary Anne Warren: The Moral and Legal Status of Abortion. 3.6 Don Marquis: Why Abortion Is Immoral. 4. The Meaning of Life. 4.1 Epicurus: Letter to Menoeceus. 4.2 Richard Taylor: The Meaning of Life. 4.3 Richard Kraut: Desire and the Human Good. 4.4 Leo Tolstoy: My Confession. 4.5 Susan Wolf: Happiness and Meaning. 4.6 Thomas Nagel: The Absurd. (shrink)

This article is a part of the discussion of Ilya Kasavin’s article “Creativity as a social phenomenon” and is devoted to the analysis of creativity in the era of digitalization. The author discusses creativity in computer programs and the actions of assistant robots. They can be creative because they are able to find new solutions to various problems. The Go program used new strategies that human players had never played before; another program predicted the crystal structure of various (...) substances that had not yet been studied experimentally. The concept of implicit knowledge is used to answer the question about the specifics of creativity. H. Collins singled out three types of tacit knowledge: relative, somatic and collective. The first is associated with the acquirement of various procedures – like cooking, the second – various skills related to body control, such as swimming or cycling, the third – social and cultural rules. Only the third type of tacit knowledge is associated with creativity. Programs and assistant robots can master relative and somatic implicit knowledge. They are not able to work with collective implicit knowledge. Creativity cannot be digitized. At the same time, digitalization forms important social prerequisites for the development of creativity. Currently, a new social group is being formed – the precariat. This is a marginal group; its representatives are not very firmly included in social structures. It is possible to single out the scientific precariat. With the help of digital technologies, it forms new ways, mechanisms, strategies and tools for solving various problems, form new ideas that expand our collective tacit knowledge. (shrink)

This book presents the current state of the art regarding the application of logical tools to the problems of theory and practice of lawmaking. It shows how contemporary logic may be useful in the analysis of legislation, legislative drafting and legal reasoning concerning different contexts of lawmaking. Elaborations of the process of lawmaking have variously emphasised its political, social or economic aspects. Yet despite strong interest in logical analyses of law, questions remains about the role of logical tools in (...) lawmaking. This volume attempts to bridge that gap, or at least to narrow it, drawing together some important research problems-and some possible solutions-as seen through the work of leading contemporary academics. The volume encompasses 20 chapters written by authors from 16 countries and it presents diversified views on the understanding of logic (from strict mathematical approaches to the informal, argumentative ones) and differentiated choices concerning the aspects of lawmaking taken into account. The book presents a broad set of perspectives, insights and results into the emerging field of research devoted to the logical analysis of the area of creation of law. How does logic inform lawmaking? Are legal systems consistent and complete? How can legal rules be represented by means of formal calculi and visualization techniques? Does the structure of statutes or of legal systems resemble the structure of deductive systems? What are the logical relations between the basic concepts of jurisprudence that constitute the system of law? How are theories of legal interpretation relevant to the process of legislation? How might the statutory text be analysed by means of contemporary computer programs? These and other questions, ranging from the theoretical to the immediately practical, are addressed in this definitive collection. (shrink)

My aim here is to raise a few questions concerning the problem of representation in scientific discovery computer programs. Representation, as Simon says in his paper, "imposes constraints upon the phenomena that allow the mechanisms to be inferred from the data". The issue is obviously barely outlined by Simon in his paper, while it is addressed in detail in the book by Langley, Simon, Bradshaw and Zytkow (1987), to which I shall refer in this note. Nevertheless, their analysis (...) would appear to leave open certain issues related to the nature of the "constraints" imposed by representation on problem solving strategies. (shrink)

What I call theoretical abduction (sentential and model-based) certainly illustrates much of what is important in abductive reasoning, especially the objective of selecting and creating a set of hypotheses that are able to dispense good (preferred) explanations of data, but fails to account for many cases of explanations occurring in science or in everyday reasoning when the exploitation of the environment is crucial. The concept of manipulative abduction is devoted to capture the role of action in many interesting situations: action (...) provides otherwise unavailable information that enables the agent to solve problems by starting and performing a suitable abductive process of generation or selection of hypotheses. Many external things, usually inert from the epistemological point of view, can be transformed into what I call epistemic mediators, which are illustrated in the last part of the paper, together with an analysis of the related notion of external representation . Finally, some examples of computational programs that simulate geometrical reasoning are illustrated. The computational embodiment generates a kind of squared epistemic mediator: geometrical construction, as an example of epistemic mediator, is further mediated. (shrink)

The phenomenon of digital computation is explained (often differently) in computer science, computer engineering and more broadly in cognitive science. Although the semantics and implications of malfunctions have received attention in the philosophy of biology and philosophy of technology, errors in computational systems remain of interest only to computer science. Miscomputation has not gotten the philosophical attention it deserves. Our paper fills this gap by offering a taxonomy of miscomputations. This taxonomy is underpinned by a (...) conceptual analysis of the design and implementation of conventional computational systems at various levels of abstraction. It shows that ‘malfunction’ as it is typically used in the philosophy of artefacts only represents one type of miscomputation. (shrink)

This book discusses major milestones in Rohit Jivanlal Parikh’s scholarly work. Highlighting the transition in Parikh’s interest from formal languages to natural languages, and how he approached Wittgenstein’s philosophy of language, it traces the academic trajectory of a brilliant scholar whose work opened up various new avenues in research. This volume is part of Springer’s book series Outstanding Contributions to Logic, and honours Rohit Parikh and his works in many ways. Parikh is a leader in the realm of ideas, (...) offering concepts and definitions that enrich the field and lead to new research directions. Parikh has contributed to a variety of areas in logic, computer science and game theory. In mathematical logic his contributions have been in recursive function theory, proof theory and non-standard analysis; in computer science, in the areas of modal, temporal and dynamic logics of programs and semantics of programs, as well as logics of knowledge; in artificial intelligence in the area of belief revision; and in game theory in the formal analysis of social procedures, with a strong undercurrent of philosophy running through all his work.This is not a collection of articles limited to one theme, or even directly connected to specific works by Parikh, but instead all papers are inspired and influenced by Parikh in some way, adding structures to and enriching “Parikh-land”. The book presents a brochure-like overview of Parikh-land before providing an “introductory video” on the sights and sounds that you experience when reading the book. (shrink)

This paper contributes to the computer ethics debate on software ownership protection by examining the ontological, methodological, and ethical problems related to property right infringement that should come prior to any legal discussion. The ontological problem consists in determining precisely what it is for a computer program to be a copy of another one, a largely neglected problem in computer ethics. The methodological problem is defined as the difficulty of deciding whether a given software system is a (...) copy of another system. And the ethical problem corresponds to establishing when a copy constitutes, or does not constitute, a property right infringement. The ontological problem is solved on the logical analysis of abstract machines, and the latter are argued to be the appropriate level of abstraction for software at which the methodological and the ethical problems can be successfully addressed. (shrink)

This paper applies a new theory of explanatory coherence to the early history of the idea of continental drift. The new theory consists of seven principles that establish coherence and incoherence relations among propositions. It has been implemented in a connectionist computer program called ECHO. Analysis of the arguments of Alfred Wegener, the first major proponent of continental drift, provided input to ECHO which evaluated the explanatory coherence of his hypotheses. ECHO has also been used to analyze the (...) coherence of the views of Wegener 's critics. The paper concludes by contrasting our explanatory coherence account of Wegener and his opposition with a decision-theoretic account recently offered by Ron Giere. (shrink)

This paper analyzes creative practice including virtual music composition by a human and sets of computer programs, improvisation of music and dance in human-robot ensembles, and drawings produced by a human and a robotic arm. In all of these examples, the paper argues that creativity arises from a process of human-robot collaboration. Human influences on the machines involved exist at many levels, from initial creation and programming, via processes of reprogramming and setup of underlying data and parameters, to engagement (...) throughout the process of creative production. The decision to value a machine as a creative other is supported most strongly when collaborating with the machine directly, while witnessing the creative team at work, as opposed simply to seeing the result, is more likely to bring an audience to a similar understanding. The creativity of the human-machine collaborations analyzed in this paper relies on close interaction, within which there is a continual recognition of the otherness of the machine and its nonhuman abilities. Such relations can be theorized by extending Emmanuel Levinas’ conception of the face-to-face encounter within which self and other are brought into proximity, but the alterity of the other is nonetheless retained. The paper’s analysis of creative interactions between humans and robots supports the idea that machines need not be regarded as challenging human artistic practice, but rather enable new ways for creativity to arise through human-machine collaborations within which human and nonhuman creative abilities are combined. (shrink)

Recognizing the necessary social aspects of scientific knowledge leads to a serious shift in the analysis of science. Whereas until recently the study of scientific knowledge in terms of its social qualities began with its logical structure, today the primary focuses of analysis are the human brain, the material carrier of computer programs, the economic relations in the society of commodity production, and so forth. All of this is not science, but is involved in the production of (...) new knowledge. The boundary between the spiritual and the material, between the scientific and nonscientific is blurred. To study this phenomenon, the vector of research shifts: social qualities are not sought in scientific knowledge, but the external world of non-science exhibits signs of the presence of the spiritual in the form of emerging scientific knowledge. (shrink)

The concepts and powerful mathematical tools of Dynamical Systems Theory (DST) yield illuminating methods of studying cognitive processes, and are even claimed by some to enable us to bridge the notorious explanatory gap separating mind and matter. This article includes an analysis of some of the conceptual and empirical progress Dynamical Systems Theory is claimed to accomodate. While sympathetic to the dynamicist program in principle, this article will attempt to formulate a series of problems the proponents of the approach (...) in question will need to face if they wish to prolong their optimism. The main points to be addressed involve the reductive tendencies inherent in Dynamical Systems Theory, its somewhat muddled position relative to connectionism, the metaphorical nature DST-C exhibits which hinders its explanatory potential, and DST-C's problematic account of causality. Brief discussions of the mathematical and philosophical backgrounds of DST, seminal experimental work and possible adaptations of the theory or alternative suggestions (dynamicist connectionism, neurophenomenology, R&D theory) are included. (shrink)

Constructibility and complexity play central roles in recent research in computer science, mathematics and physics. For example, scientists are investigating the complexity of computer programs, constructive proofs in mathematics and the randomness of physical processes. But there are different approaches to the explication of these concepts. This volume presents important research on the state of this discussion, especially as it refers to quantum mechanics. This `foundational debate' in computer science, mathematics and physics was already fully developed in (...) 1930 in the Vienna Circle. A special section is devoted to its real founder Hans Hahn, referring to his contribution to the history and philosophy of science. The documentation section presents articles on the early Philipp Frank and on the Vienna Circle in exile. Reviews cover important recent literature on logical empiricism and related topics. (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)