There are currently considerable confusion and disarray about just how we should view computationalism, connectionism and dynamicism as explanatory frameworks in cognitive science. A key source of this ongoing conflict among the central paradigms in cognitive science is an equivocation on the notion of computation simpliciter. ‘Computation’ is construed differently by computationalism, connectionism, dynamicism and computational neuroscience. I claim that these central paradigms, properly understood, can contribute to an integrated cognitive science. Yet, before this claim can be defended, (...) a better understanding of ‘computation’ is required. ‘Digital computation’ is an ambiguous concept. It is not just the classical dichotomy between analogue and digital computation that is the basis for the equivocation on ‘computation’ simpliciter in cognitive science, but also the diversity of extant accounts of digital computation. There are many answers on what it takes for a system to perform digital computation. Answers to this problem range from Turing machine computation, through the formal manipulation of symbols, the execution of algorithms and others, to the strong-pancomputational thesis, according to which every physical system computes every Turing-computable function. Despite some overlap among them, extant accounts of concrete digital computation are non-equivalent, thus, rendering ‘digital computation’ ambiguous. The objective of this dissertation is twofold. First, it is to promote a clearer understanding of concrete digital computation. Accordingly, my main thesis is that not only are extant accounts of concrete digital computation non-equivalent, but most of them are inadequate. I show that these accounts are not just intensionally different (this is quite trivially the case), but also extensionally distinct. In the course of examining several key accounts of concrete digital computation, I propose the instructional information processing account, according to which digital computation is the processing of discrete data in accordance with finite instructional information. The second objective is to establish the foundational role of computation in cognitive science whilst rejecting the purported representational nature of computation. (shrink)

This paper deals with the question: what are the key requirements for a physical system to perform digital computation? Time and again cognitive scientists are quick to employ the notion of computation simpliciter when asserting basically that cognitive activities are computational. They employ this notion as if there was or is a consensus on just what it takes for a physical system to perform computation, and in particular digital computation. Some cognitive scientists in referring (...) to digital computation simply adhere to Turing’s notion of computability . Classical computability theory studies what functions on the natural numbers are computable and what mathematical problems are undecidable. Whilst a mathematical formalism of computability may perform a methodological function of evaluating computational theories of certain cognitive capacities, concrete computation in physical systems seems to be required for explaining cognition as an embodied phenomenon . There are many non-equivalent accounts of digital computation in physical systems. I examine only a handful of those in this paper: (1) Turing’s account ; (2) The triviality “account”; (3) Reconstructing Smith’s account of participatory computation ; (4) The Algorithm Execution account . My goal in this paper is twofold. First, it is to identify and clarify some of the underlying key requirements mandated by these accounts. I argue that these differing requirements justify a demand that one commits to a particular account when employing the notion of computation in regard to physical systems. Second, it is to argue that despite the informative role that mathematical formalisms of computability may play in cognitive science, they do not specify the relationship between abstract and concrete computation. (shrink)

It is common in cognitive science to equate computation (and in particular digital computation) with information processing. Yet, it is hard to find a comprehensive explicit account of concrete digital computation in information processing terms. An information processing account seems like a natural candidate to explain digital computation. But when ‘information’ comes under scrutiny, this account becomes a less obvious candidate. Four interpretations of information are examined here as the basis for an (...) information processing account of digital computation, namely Shannon information, algorithmic information, factual information and instructional information. I argue that any plausible account of concrete computation has to be capable of explaining at least the three key algorithmic notions of input, output and procedures. Whist algorithmic information fares better than Shannon information, the most plausible candidate for an information processing account is instructional information. (shrink)

Gualtiero Piccinini articulates and defends a mechanistic account of concrete, or physical, computation. A physical system is a computing system just in case it is a mechanism one of whose functions is to manipulate vehicles based solely on differences between different portions of the vehicles according to a rule defined over the vehicles. Physical Computation discusses previous accounts of computation and argues that the mechanistic account is better. Many kinds of computation are explicated, such as (...) digital vs. analog, serial vs. parallel, neural network computation, program-controlled computation, and more. Piccinini argues that computation does not entail representation or information processing although information processing entails computation. Pancomputationalism, according to which every physical system is computational, is rejected. A modest version of the physical Church-Turing thesis, according to which any function that is physically computable is computable by Turing machines, is defended. (shrink)

The online space has become a digital public square, where individuals interact and share ideas on the most trivial to the most serious of matters, including discussions of controversial ethical issues in science, technology and medicine. In the last decade, new disciplines like computational social science and social data science have created methods to collect and analyse such data that have considerably expanded the scope of social science research. Empirical bioethics can benefit from the integration of such digital (...) methods to investigate novel digital phenomena and trace how bioethical issues take shape online.Here, using concrete examples, we demonstrate how novel methods based on digital approaches in the social sciences can be used effectively in the domain of bioethics. We show that a digital turn in bioethics research aligns with the established aims of empirical bioethics, integrating with normative analysis and expanding the scope of the discipline, thus offering ways to reinforce the capacity of bioethics to tackle the increasing complexity of present-day ethical issues in science and technology. We propose to call this domain of research in bioethicsdigital bioethics. (shrink)

This book introduces an events-based approach to understanding digital experience. Focusing on the event-ontologies of Bergson and Whitehead's process metaphysics, it explores subjective experience and objective reality as unified 'events' in the form of concrete slabs of existence. Such slabs are temporally defined by a term or period, in which all physical-chemical processes and personal subjective experience are included. Bringing together insights from a range of different specialisms, it urges us to consider a science of nature that includes (...) both physical and non-physical realities and, from this ontological position, draws on philosophy, media, and user experience practice to provide a new account of the technological or virtual world of today. An examination of the manner in which process philosophy may be applied to contemporary digital experience, this volume will appeal to scholars of philosophy, science and technology studies and information systems. (shrink)

The increasing concern about visual representation in science has been usually converged on representations – photographs, diagrams, graphs, maps –, while instruments of visualization have been usually neglected, even because of the concrete difficulty to grasp their effects on visualization. In this regard, the questions and concepts formulated in the debate on digital visualization deserve here as a starting point to analyze the change in instrumental mediation triggered by the introduction of computer-assisted imaging technologies in those laboratories that (...) traditionally have used and still use microscopes. Empirical materials gathered during an ethnographic investigation of Italian cytogenetics labs are here presented to show the visual spaces provided by microscopes and digital systems as activity fields, which are inhabited by and suggest in an either divergent or complementary way specific practices, materials, organizations, epistemological orientations and aesthetical preferences. (shrink)

As architecture intersects with computer science to engage with large-scale data sets and informational systems, this demands new skills, competencies and commitments. Informed by the findings of an online survey, this article explores how, who and to what extent those in the profession of architecture are investing in technology knowledge and skills, and under what material conditions this occurs. Survey data collected from five large-scale architecture practices in Sydney, Australia finds that while technology-related skills are highly valued in the profession, (...) more men than women are engaging with computationally intensive software and technology skills building remains a largely unstructured and often self-directed enterprise. Drawing on feminist technology studies and digital labour perspectives, it is argued that the drive to computationalise the profession of architecture rests heavily on discretionary, aspirational and invisible labour practices that disadvantage employees with lesser reserves of economic and social capital, and particularly women. This further contributes to revealing neo-liberalism’s influence on the concrete practices of the architecture workplace and highlights how the diminished structural role of employers breeds uneven opportunities and inequitable working conditions. (shrink)

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

Purpose This paper aims to look at shifts in internet-related content and services economies, from audience labour economies to Web 2.0 user-generated content, and the emerging model of user computing power utilisation, powered by blockchain technologies. The authors look at and test three models of user computing power utilisation based on distributed computing two of which use cryptocurrency mining through distributed pool mining techniques, while the third is based on distributed computing of calculations for scientific research. The three models promise (...) benefits to their users, which the authors discuss throughout the paper, studying how they interplay with the three levels of the digital divide. Design/methodology/approach The goal of this article is twofold as follows: first to discuss how using the mining hype may reduce digital inequalities, and secondly to demonstrate how these services offer a new business model based on value rewarding in exchange for computational power, which would allow more online opportunities for people, and thus reduce digital inequalities. Finally, this contribution discusses and proposes a method for a fair revenue model for content and online service providers that uses user device computing resources or computational power, rather than their data and attention. The method is represented by a model that allows for consensual use of user computing resources in exchange for accessing content and using software tools and services, acting essentially as an alternative online business model. Findings Allowing users to convert their devices’ computational power into value, whether through access to services or content or receiving cryptocurrency and payments in return for providing services or content or direct computational powers, contributes to bridging digital divides, even at fairly small levels. Secondly, the advent of blockchain technologies is shifting power relations between end-users and content developers and service providers and is a necessity for the decentralisation of internet and internet services. Originality/value The article studies the effect of services that rely on distributed computing and mining on digital inequalities, by looking at three different case studies – Coinhive, Gridcoin and Cryptotab – that promise to provide value in return for using computing resources. The article discusses how these services may reduce digital inequalities by affecting the three levels of the digital divide, namely, access to information and communication technologies, skills and motivations in using ICTs and capacities in using ICTs to get concrete benefits. (shrink)

This edited volume focuses on big data implications for computational social science and humanities from management to usage. The first part of the book covers geographic data, text corpus data, and social media data, and exemplifies their concrete applications in a wide range of fields including anthropology, economics, finance, geography, history, linguistics, political science, psychology, public health, and mass communications. The second part of the book provides a panoramic view of the development of big data in the fields of (...) computational social sciences and humanities. The following questions are addressed: why is there a need for novel data governance for this new type of data?, why is big data important for social scientists?, and how will it revolutionize the way social scientists conduct research? With the advent of the information age and technologies such as Web 2.0, ubiquitous computing, wearable devices, and the Internet of Things, digital society has fundamentally changed what we now know as "data", the very use of this data, and what we now call "knowledge". Big data has become the standard in social sciences, and has made these sciences more computational. Big Data in Computational Social Science and Humanities will appeal to graduate students and researchers working in the many subfields of the social sciences and humanities. (shrink)

Our lives are increasingly intertwined with the digital realm, and with new technology, new ethical problems emerge. The academic field that addresses these problems—which we tentatively call ‘digital ethics’—can be an important intellectual resource for policy making and regulation. This is why it is important to understand how the new ethical challenges of a digital society are being met by academic research. We have undertaken a scientometric analysis to arrive at a better understanding of the nature, scope (...) and dynamics of the field of digital ethics. Our approach in this paper shows how the field of digital ethics is distributed over various academic disciplines. By first having experts select a collection of keywords central to digital ethics, we have generated a dataset of articles discussing these issues. This approach allows us to generate a scientometric visualisation of the field of digital ethics, without being constrained by any preconceived definitions of academic disciplines. We have first of all found that the number of publications pertaining to digital ethics is exponentially increasing. We furthermore established that whereas one may expect digital ethics to be a species of ethics, we in fact found that the various questions pertaining to digital ethics are predominantly being discussed in computer science, law and biomedical science. It is in these fields, more than in the independent field of ethics, that ethical discourse is being developed around concrete and often technical issues. Moreover, it appears that some important ethical values are very prominent in one field (e.g., autonomy in medical science), while being almost absent in others. We conclude that to get a thorough understanding of, and grip on, all the hard ethical questions of a digital society, ethicists, policy makers and legal scholars will need to familiarize themselves with the concrete and practical work that is being done across a range of different scientific fields to deal with these questions. (shrink)

This commentary on Fresco's article "Information processing as an account of concrete digital computation" illuminates the two intertwined roles that the definition of the term "information" plays in Fresco's analysis. It provides analysis of the notion of actualizing control in information processing. The key point made is that not all control information in common computational devices cannot be processed.

High profile breaches of data security in government and other organizations are becoming an increasing concern amongst members of the public. Academic researchers have rarely discussed data security issues as they affect research, and this is especially the case for qualitative social researchers, who are sometimes disinclined to technical solutions. This paper describes 14 guidelines developed to help qualitative researchers improve the security of their digitally-created and stored data. We developed these procedures after the theft of a laptop computer containing (...) highly sensitive data from the home of a fieldworker. This paper introduces the ‘principle of proliferation’: digitally-created and stored files tend to proliferate during the course of a research project by virtue of fact that they can and are copied and shared as research progresses from data collection through to analysis and archive. Our guidelines were designed as concrete strategies that researchers embarking on a project can employ, particularly researchers working in teams, to accommodate this proliferation and reduce it where possible. (shrink)

Online audiovisual interaction (AVOI), though minimal, constitutes a form of embodiment. This implies that empathy can be fostered even in non-co-located individuals through online platforms. To address both the limitations and potential of online embodied interaction the article develops a framework for comprehending and cultivating empathy in the virtual realm. It argues that empathy is a skill that is fundamentally tied to our physical and sensory experiences, and therefore, dismisses the Theory of Mind (ToM) model for reducing empathy to mere (...) mental constructs with inherent limitations. Instead, it draws upon Embodied Cognitive Science (ECS) to show that the feeling of disembodiment experienced in AVOI can be conceptualised as a “shrinking of” the social space. With the aim of both widening and deepening this space, the article introduces concrete guidelines that can help enrich the embodied and interactive experience in AVOI. Thereby, highlighting ways of integrating spatiality and physical engagement in the audiovisual online sphere, in addition to nurturing awareness and digital tact. (shrink)

Scientists of many disciplines use theoretical models to explain and predict the dynamics of the world. They often have to rely on digital computer simulations to draw predictions fromthe model. But to deliver phenomenologically adequate results, simulations deviate from the assumptions of the theoretical model. Therefore the role of simulations in scientific explanation demands itself an explanation. This paper analyzes the relation between real-world system, theoretical model, and simulation. It is argued that simulations do not explain processes in the (...) real world directly. The way in which simulations help explaining real-world processes is conceived as indirect, mediated by the theoretical model. Simulacra are characterized further, and turn out to be a priori measurable. This gives a clue to a better understanding of the epistemic role of computer simulations in scientific research. (shrink)

Stevan Harnad correctly perceives a deep problem in computationalism, the hypothesis that cognition is computation, namely, that the symbols manipulated by a computational entity do not automatically mean anything. Perhaps, he proposes, transducers and neural nets will not have this problem. His analysis goes wrong from the start, because computationalism is not as rigid a set of theories as he thinks. Transducers and neural nets are just two kinds of computational system, among many, and any solution to the semantic (...) problem that works for them will work for most other computational systems. (shrink)

The concept of ‘digital phenotyping’ was originally developed by researchers in the mental health field, but it has travelled to other disciplines and areas. This commentary draws upon our experiences of working in two scientific projects that are based at the University of Oxford’s Big Data Institute – The RADAR-AD project and The Minerva Initiative – which are developing algorithmic phenotyping technologies. We describe and analyse the concepts of digital biomarkers and computational phenotyping that underlie these projects, explain (...) how they are linked to other research in digital phenotyping and compare and contrast some of their epistemological and ethical implications. In particular, we argue that the phenotyping paradigm in both projects is grounded on an assumption of ‘objectivity’ that is articulated in different ways depending on the role that is given to the computational/digital tools. Using the concept of ‘affordance’, we show how specific functionalities relate to potential uses and social implications of these technologies and argue that it is important to distinguish among them as the concept of digital phenotyping is increasingly being used with a variety of meanings. (shrink)

The distinction at the heart of van Gelder’s target article is one between digital computers and dynamical systems. But this distinction conflates two more fundamental distinctions in cognitive science that should be keep apart. When this conflation is undone, it becomes apparent that the “computational hypothesis” (CH) is not as dominant in contemporary cognitive science as van Gelder contends; nor has the “dynamical hypothesis” (DH) been neglected.

There are different ways to present a Presidential Address to the APA; the one I have chosen is simply to report on work that I am doing right now, on work in progress. I am going to present some of my further explorations into the computational model of the mind.\**.

It is fairly well-known that certain hard computational problems (that is, 'difficult' problems for a digital processor to solve) can in fact be solved much more easily with an analog machine. This raises questions about the true nature of the distinction between analog and digital computation (if such a distinction exists). I try to analyze the source of the observed difference in terms of (1) expanding parallelism and (2) more generally, infinite-state Turing machines. The issue of discreteness (...) vs continuity will also be touched upon, although it is not so important for analyzing these particular problems. (shrink)

As a device used by scientists in the course of performing research, the digital computer might be considered a scientific instrument. But if so, what is it an instrument for? This paper explores a number of answers to this question, focusing on the use of computers in a simulating mode.

This paper takes up a recent challenge to mechanistic approaches to computational implementation, the view that computational implementation is best explicated within a mechanistic framework. The challenge, what has been labelled “the abstraction problem”, claims that one of MAC’s central pillars – medium independence – is deeply confused when applied to the question of computational implementation. The concern is that while it makes sense to say that computational processes are abstract (i.e. medium-independent), it makes considerably less sense to say that (...) they are also concrete processes of a mechanism. After outlining the problem and its effect on MAC, I examine a recent response from Kuokkanen and Rusanen [2018. “Making Too Many Enemies: Hutto and Myin’s Attack on Computationalism.” Philosophical Explorations 21 (2): 282–294. doi:10.1080/13869795.2018.1477980]. I argue that Kuokkanen and Rusanen’s response comes up short insofar as it makes problematic trade-offs among various desiderata we have for a theory of implementation. This leads to a general dilemma for MAC: either give up being an objective theory of implementation or concede the abstraction problem and so reintroduce triviality concerns. In response, I argue that conceiving of computations as abstracta rather than illata provides a way to avoid the proposed dilemma and articulate a notion of medium independence that addresses the abstraction problem. (shrink)

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

ABSTRACTIs the brain a digital computer? What about your own brain? This article will examine these questions, some possible answers, and what persistent disagreement on the topic might indicate. Along the way we explore the metaphor at the heart of the question and assess how observer relativity features in it. We also reflect on the role of models in scientific endeavour. By the end you should have a sense of why the question matters, what some answers to it might (...) be, and why your preferred answer is highly pertinent to any discussion. (shrink)

What is nontrivial digital computation? It is the processing of discrete data through discrete state transitions in accordance with finite instructional information. The motivation for our account is that many previous attempts to answer this question are inadequate, and also that this account accords with the common intuition that digital computation is a type of information processing. We use the notion of reachability in a graph to defend this characterization in memory-based systems and underscore the importance (...) of instructional information for digital computation. We argue that our account evaluates positively against adequacy criteria for accounts of computation. (shrink)

Turing's analysis of the concept of computation is indisputably the foundation of computationalism, which is, in turn, the foundation of cognitive science. What is disputed is whether computationalism is explanatorily bankrupt. For Turing, all computers are digital computers and something becomes a computer just in case its 'behavior' is interpreted as implementing, executing, or satisfying some function 'f'. As 'computer' names a nonnatural kind, almost everyone agrees that a computational interpretation of this sort is necessary for something to (...) be a computer. But because everything in the universe satisfies at least one function, it is the sufficiency of such interpretations that is the problem. If, as anticomputationalists are fond of pointing out, computationalists are wedded to the view that a computational interpretation is sufficient for something to be a computer, then everything becomes a digital computer. This not only renders computer-talk vacuous, it strips computationalism of any empirical or explanatory import. My aim is to defend computationalism against charges that it is explanatorily bankrupt. I reexamine several fundamental questions about computers. One effect of this computation-related soul-searching will be a framework within which 'Is the brain a computer?' will be meaningful. Another effect will be a fracture in the supposed link between computationalism and symbolic-digital processing. (shrink)

I do not wish at this time to dispute either or. I do not believe, however, that the intermediate step can be adequately justified, and hence remain unconvinced by the purported conclusion. The most recent presentation of this argument is in Professor Dreyfus' article "Why Computers must have Bodies in order to be Intelligent," a discussion of which will serve to explain my lack of confidence in any argument of this general form.

Digital computers carry out algorithms coded in high level programs. These abstract entities determine what happens at the physical level: they control whether electrons flow through specific transistors at specific times or not, entailing downward causation in both the logical and implementation hierarchies. This paper explores how this is possible in the light of the alleged causal completeness of physics at the bottom level, and highlights the mechanism that enables strong emergence (the manifest causal effectiveness of application programs) to (...) occur. Although synchronic emergence of higher levels from lower levels is manifestly true, diachronic emergence is generically not the case; indeed we give specific examples where it cannot occur because of the causal effectiveness of higher level variables. (shrink)

Digital computers carry out algorithms coded in high level programs. These abstract entities determine what happens at the physical level: they control whether electrons flow through specific transistors at specific times or not, entailing downward causation in both the logical and implementation hierarchies. This paper explores how this is possible in the light of the alleged causal completeness of physics at the bottom level, and highlights the mechanism that enables strong emergence to occur. Although synchronic emergence of higher levels (...) from lower levels is manifestly true, diachronic emergence is generically not the case; indeed we give specific examples where it cannot occur because of the causal effectiveness of higher level variables. (shrink)

In 1965, John A. Pope presented a paper entitled 'Two-Dimensional Chart of Quantum Chemistry' to illustrate the inverse relationship between the sophistication of computational methods and the size of molecules under study. This chart, later called the 'hyperbola of quantum chemistry', succinctly summarized the growing tension between the proponents of two different approaches to computation–the ab initio method and semiempirical method–in the early years of electronic digital computers. Examining the development of quantum chemistry after World War II, I (...) focus on the role of computers in shaping disciplinary identity. The availability of high-speed computers in the early 1950s attracted much attention from quantum chemists, and their community took shape through a series of conferences and personal networking. However, this emerging community soon encountered the problem of communication between groups that differed in the degree of reliance they placed on computers. I show the complexity of interactions between computing technology and a scientific discipline, in terms of both forming and splitting the community of quantum chemistry. (shrink)

In the age of digitization, the world seems to be reducible to a digital computer. However, mathematically, modern quantum field theories do not only depend on discrete, but also continuous concepts. Ancient debates in natural philosophy on atomism versus the continuum are deeply involved in modern research on digital and computational physics. This example underlines that modern physics, in the tradition of Newton’s Principia Mathematica Philosophiae Naturalis, is a further development of natural philosophy with the rigorous methods of (...) mathematics, measuring, and computing. We consider fundamental concepts of natural philosophy with mathematical and computational methods and ask for their ontological and epistemic status. The following article refers to the author’s book, “The Digital and the Real World. Computational Foundations of Mathematics, Science, Technology, and Philosophy.”. (shrink)

Technologies of communication condition human sense-making. They do so by creating the social environment we inhabit and extending their structural biases and logics through human use. As such, this essay inquires into the prevailing habits of mind in the digital era. Employing a media ecology of communication, I argue that digital computers and microprocessors are defined by three structural properties and, hence, underlying logics: digitization (binary code), algorithmic execution (input/output), and efficiency (machine logic). Repeated exposure to these logics (...) cultivates a digital mind, a model of thinking, communicating, and sense-making characterized by intransigence, impertinence, and impulsivity. I conclude the essay by exploring the broader implications of a digital mind, paying particular attention to the challenges it poses to democratic politics. (shrink)