Computational reproducibility possesses its own dynamics and narratives of crisis. Alongside the difficulties of computing as an ubiquitous yet complex scientific activity, computational reproducibility suffers from a naive expectancy of total reproducibility and a moral imperative to embrace the principles of free software as a non-negotiable epistemic virtue. We argue that the epistemic issues at stake in actual practices of computational reproducibility are best unveiled by focusing on software as a pivotal concept, one (...) that is surprisingly often overlooked in accounts of reproducibility issues. Software is not only about designing and coding but also about maintaining, supporting, distributing, licensing, and governance; it is not only about developers but also about users. We focus on openness debates among computational chemists involved in molecular modeling software packages as empirical grounding for our argument. We then identify and analyse four epistemic characteristics as key to the role of software in computational reproducibility. (shrink)

Replicability and reproducibility of computational models has been somewhat understudied by “the replication movement.” In this paper, we draw on methodological studies into the replicability of psychological experiments and on the mechanistic account of explanation to analyze the functions of model replications and model reproductions in computational neuroscience. We contend that model replicability, or independent researchers' ability to obtain the same output using original code and data, and model reproducibility, or independent researchers' ability to recreate a (...) model without original code, serve different functions and fail for different reasons. This means that measures designed to improve model replicability may not enhance (and, in some cases, may actually damage) model reproducibility. We claim that although both are undesirable, low model reproducibility poses more of a threat to long-term scientific progress than low model replicability. In our opinion, low model reproducibility stems mostly from authors' omitting to provide crucial information in scientific papers and we stress that sharing all computer code and data is not a solution. Reports of computational studies should remain selective and include all and only relevant bits of code. (shrink)

BackgroundThe trend toward open science increases the pressure on authors to provide access to the source code and data they used to compute the results reported in their scientific papers. Since sharing materials reproducibly is challenging, several projects have developed solutions to support the release of executable analyses alongside articles.MethodsWe reviewed 11 applications that can assist researchers in adhering to reproducibility principles. The applications were found through a literature search and interactions with the reproducible research community. An application was (...) included in our analysis if it was actively maintained at the time the data for this paper was collected, supports the publication of executable code and data, is connected to the scholarly publication process. By investigating the software documentation and published articles, we compared the applications across 19 criteria, such as deployment options and features that support authors in creating and readers in studying executable papers.ResultsFrom the 11 applications, eight allow publishers to self-host the system for free, whereas three provide paid services. Authors can submit an executable analysis using Jupyter Notebooks or R Markdown documents. All approaches provide features to assist readers in studying the materials, e.g., one-click reproducible results or tools for manipulating the analysis parameters. Six applications allow for modifying materials after publication.ConclusionsThe applications support authors to publish reproducible research predominantly with literate programming. Concerning readers, most applications provide user interfaces to inspect and manipulate the computational analysis. The next step is to investigate the gaps identified in this review, such as the costs publishers have to expect when hosting an application, the consideration of sensitive data, and impacts on the review process. (shrink)

Current fears of a “reproducibility crisis” have led researchers, sources of scientific funding, and the public to question both the efficacy and trustworthiness of science. Suggested policy changes have been focused on statistical problems, such as p-hacking, and issues of experimental design and execution. However, “reproducibility” is a broad concept that includes a number of issues. Furthermore, reproducibility failures occur even in fields such as mathematics or computer science that do not have statistical problems or issues with (...) experimental design. Most importantly, these proposed policy changes ignore a core feature of the process of scientific inquiry that occurs after reproducibility failures: the integration of conflicting observations and ideas into a coherent theory. (shrink)

Computers have transformed science and help to extend the boundaries of human knowledge. However, does the validation and diffusion of results of computational inquiries and computer simulations call for a novel epistemological analysis? I discuss how the notion of novelty should be cashed out to investigate this issue meaningfully and argue that a consequentialist framework similar to the one used by Goldman to develop social epistemologySocial epistemology can be helpful at this point. I highlight computational, mathematical, representational, and (...) social stages on which the validity of simulation-based belief-generating processes hinges, and emphasize that their epistemic impact depends on the scientific practices that scientists adopt at these different stages. I further argue that epistemologists cannot ignore these partially novel issues and conclude that the epistemology of computational inquiries needs to go beyond that of models and scientific representations and has cognitive, social, and in the present case computational, dimensions. (shrink)

Evolutionary and organismal biology have become inundated with data. At the same rate, we are experiencing a surge in broader evolutionary and ecological syntheses for which tree-thinking is the staple for a variety of post-tree analyses. To fully take advantage of this wealth of data to discover and understand large-scale evolutionary and ecological patterns, computational data integration, i.e., the use of machines to link data at large scale, is crucial. The most common shared entity by which evolutionary and ecological (...) data need to be linked is the taxon to which they belong. We propose a set of requirements that a system for defining such taxa should meet for computational data science: taxon definitions should maintain conceptual consistency, be reproducible via a known algorithm, be computationally automatable, and be applicable across the Tree of Life. We argue that Linnaean names, the most prevalent means of linking data to taxa, fail to meet these requirements due to fundamental theoretical and practical shortfalls. We argue that for the purposes of data-integration we should instead use phylogenetic definitions transformed into formal logic expressions. We call such expressions “phyloreferences”, and argue that, unlike Linnaean names, they meet all requirements for effective data-integration. (shrink)

Most people encounter art images as digital reproductions on a computer screen instead of as originals in a museum or gallery. With the development of digital technologies, high-resolution artworks can be accessed anywhere and anytime by a large number of viewers. Since these digital images depict the same content and are attributed to the same artist as the original, it is often implicitly assumed that their aesthetic evaluation will be similar. When it comes to the digital reproductions of art, however, (...) it is also obvious that reproductions do differ from the originals in various aspects. Besides image quality, resolution, and format, the most obvious change is in the representation of color. The effects of subjectively varying surface-level image features on art evaluation have not been clearly assessed. To address this gap, we compare the evaluation of digital reproductions of 16 expressionist and impressionist paintings manipulated to have a high color saturation vs. a saturation similar to the original. We also investigate the impact of viewing time and expertise, two other aspects that may impact the perception of art in online contexts. Moreover, we link these dimensions to a recent model of aesthetic experience [the Vienna Integrated Model of Top-Down and Bottom-Up Processes in Art Perception ]. Results suggest that color saturation does not exert a major influence on liking. Cognitive and emotional aspects, however, are affected – to different extents for experts and laypersons. For laypersons, the increase in color saturation led to more positive assessments of an artwork, whereas it resulted in increased confusion for art experts. This insight is particularly important when it comes to reproducing artworks digitally. Depending on the intended use, increasing or decreasing the color saturation of the digitally reproduced image might be most appropriate. We conclude with a discussion of these findings and address the question of why empirical aesthetics requires more precise dimensions to better understand the subtle processes that take place in the perception of today’s digitally reproduced art environment. (shrink)

We consider processes of emergence within the conceptual framework of the Information Loss principle and the concepts of systems conserving information; systems compressing information; and systems amplifying information. We deal with the supposed incompatibility between emergence and computability tout-court. We distinguish between computational emergence, when computation acquires properties, and emergent computation, when computation emerges as a property. The focus is on emergence processes occurring within computational processes. Violations of Turing-computability such as non-explicitness and incompleteness are intended to represent (...) partially the properties of phenomenological emergence, such as logical openness, given by the observer’s cognitive role; structural dynamics where change regards rules rather than only values; and multi-modelling where multiple non-equivalent models are required to model such structural dynamics. In this way, we validate, from an epistemological viewpoint, models and simulations of phenomenological emergence where the sequence of events constitutes the natural, analogical non-Turing computation which a cognitive complex system can reproduce through learning. Reproducibility through learning is different from Turing-like computational iteration. This paper aims to open a new, non-reductionist understanding of the conceptual relationship between emergence and computability. (shrink)

This paper discusses some philosophical aspects related to the recent publication of the experimental results of the 2017 black hole experiment, namely the first image of the supermassive black hole at the center of galaxy M87. In this paper I present a philosophical analysis of the 2017 Event Horizon Telescope (EHT) black hole experiment. I first present Hacking’s philosophy of experimentation. Hacking gives his taxonomy of elements of laboratory science and distinguishes a list of elements. I show that the EHT (...) experiment conforms to major elements from Hacking’s list. I then describe with the help of Galison’s Philosophy of the Shadow how the EHT Collaboration created the famous black hole image. Galison outlines three stages for the reconstruction of the black hole image: Socio-Epistemology, Mechanical Objectivity, after which there is an additional SocioEpistemology stage. I subsequently present my own interpretation of the reconstruction of the black hole image and I discuss model fitting to data. I suggest that the main method used by the EHT Collaboration to assure trust in the results of the EHT experiment is what philosophers call the Argument from Coincidence. I show that using this method for the above purpose is problematic. I present two versions of the Argument from Coincidence: Hacking’s Coincidence and Cartwright’s Reproducibility by which I analyse the EHT experiment. The same estimation of the mass of the black hole is reproduced in four different procedures. The EHT Collaboration concludes: the value we have converged upon is robust. I analyse the mass measurements of the black hole with the help of Cartwright’s notion of robustness. I show that the EHT Collaboration construe Coincidence/Reproducibility as Technological Agnosticism and I contrast this interpretation with van Fraassen’s scientific agnosticism. (shrink)

A sociological revision of Aron Gurwitsch provides a helpful layered theory of conscious experience as a four-domain structure: _the theme_, _the thematic field_, _the halo_, and _the social horizon_. The social horizon—the totality of the social world that is unknown, vaguely known, taken for granted, or ignored by the subject despite objectively influencing the thoughts and actions of the subject—, helps conceptualize how everyday human–computer interaction (HCI) can obscure social structures. Two examples illustrate the usefulness of this framework: (1) illuminating (...) new forms of social control known as “surveillance capitalism” that influence the other domains of consciousness despite being invisible to the subject and (2) explaining how computer use tends to “hide” the ecological impacts of digital-technological systems from the attention of subjects. A sociological four-domain theory of consciousness supplements ethnomethodological HCI research by preserving the field’s long-standing focus on micro-level interactions and user experience while simultaneously drawing attention to a “commonsense ignorance of social structure”—in this case, the invisibility of the social structures behind personal computer use. Social structure can be reproduced even when actors are unaware of engaging in its reproduction. (shrink)

Most discussions of the reproducibility crisis focus on its epistemic aspect: the fact that the scientific community fails to follow some norms of scientific investigation, which leads to high rates of irreproducibility via a high rate of false positive findings. The purpose of this paper is to argue that there is a heretofore underappreciated and understudied dimension to the reproducibility crisis in experimental psychology and neuroscience that may prove to be at least as important as the epistemic dimension. (...) This is the communication dimension. The link between communication and reproducibility is immediate: independent investigators would not be able to recreate an experiment whose design or implementation were inadequately described. I exploit evidence of a replicability and reproducibility crisis in computational science, as well as research into quality of reporting to support the claim that a widespread failure to adhere to reporting standards, especially the norm of descriptive completeness, is an important contributing factor in the current reproducibility crisis in experimental psychology and neuroscience. (shrink)

One of the most debated questions in psychology and cognitive science is the nature and the functioning of the mental processes involved in deductive reasoning. However, all existing theories refer to a specific deductive domain, like syllogistic, propositional or relational reasoning.Our goal is to unify the main types of deductive reasoning into a single set of basic procedures. In particular, we bring together the microtheories developed from a mental models perspective in a single theory, for which we provide a formal (...) foundation. We validate the theory through a computational model (UNICORE) which allows fine‐grained predictions of subjects' performance in different reasoning domains.The performance of the model is tested against the performance of experimental subjects—as reported in the relevant literature—in the three areas of syllogistic, relational and propositional reasoning. The computational model proves to be a satisfactory artificial subject, reproducing both correct and erroneous performance of the human subjects. Moreover, we introduce a developmental trend in the program, in order to simulate the performance of subjects of different ages, ranging from children (3–6) to adolescents (8–12) to adults (>21). The simulation model performs similarly to the subjects of different ages.Our conclusion is that the validity of the mental model approach is confirmed for the deductive reasoning domain, and that it is possible to devise a unique mechanism able to deal with the specific subareas. The proposed computational model (UNICORE) represents such a unifying structure. (shrink)

Although artificial intelligence techniques have been successfully applied to reproduce many rational features of human behaviour, a great barrier has been encountered in simulating human activities where intuition and emotion are involved. Art making and viewing are processes where typically rational and mechanical aspects interact with aesthetic and cognitive criteria. Can you make a computer understand and autonomously produce art?The main purpose of this paper is to present the most relevant approaches in the study of art perception and creation via (...) computer, focusing on the results achieved in artistic computer graphics. (shrink)

This paper addresses a common challenge with computational cognitive models: identifying parameter values that are both theoretically plausible and generate predictions that match well with empirical data. While computational models can offer deep explanations of cognition, they are computationally complex and often out of reach of traditional parameter fitting methods. Weak methodology may lead to premature rejection of valid models or to acceptance of models that might otherwise be falsified. Mathematically robust fitting methods are, therefore, essential to the (...) progress of computational modeling in cognitive science. In this article, we investigate the capability and role of modern fitting methods—including Bayesian optimization and approximate Bayesian computation—and contrast them to some more commonly used methods: grid search and Nelder–Mead optimization. Our investigation consists of a reanalysis of the fitting of two previous computational models: an Adaptive Control of Thought—Rational model of skill acquisition and a computational rationality model of visual search. The results contrast the efficiency and informativeness of the methods. A key advantage of the Bayesian methods is the ability to estimate the uncertainty of fitted parameter values. We conclude that approximate Bayesian computation is (a) efficient, (b) informative, and (c) offers a path to reproducible results. (shrink)

Scientificity is essentially methodology. The use of information technology as methodological instruments in science has been increasing for decades, this raises the question: Does this transform science? This question is the subject of the Special Issue in Minds and Machines “The epistemological significance of methods in computer simulation and machine learning”. We show that there is a technological change in this area that has three methodological and epistemic consequences: methodological opacity, reproducibility issues, and altered forms of justification.

Defense of Scientific Hypothesis: From Reproducibility Crisis to Big Data sets out to explain and defend the scientific hypothesis. Alger's mission is to counteract the misinformation and misunderstanding about the hypothesis that even seasoned scientists have concerning its nature and place in modern science. Most biological scientists receive little or no formal training in scientific thinking. Further, the hypothesis is under attack by critics who claim that it is irrelevant to science. In order to appreciate and evaluate scientific controversies (...) like global climate change, vaccine safety, etc., the public first needs to understand the hypothesis. Defense of Scientific Hypothesis begins by describing and analyzing the scientific hypothesis in depth and examining its relationships to various kinds of science. Alger then guides readers through a review of the hypothesis in the context of the Reproducibility Crisis and presents survey data on how scientists perceive and employ hypotheses. He assesses cognitive factors that influence our ability to use the hypothesis and makes practical and policy recommendations for teaching and learning about it. Finally, Alger considers two possible futures of the hypothesis in science as the Big Data revolution looms: in one scenario, the hypothesis is displaced by the Big Data Mindset that forgoes understanding in favor of correlation and prediction. In the other, robotic science incorporates the hypotheses into mechanized laboratories guided by artificial intelligence. But in his illuminating epilogue, Alger envisions a third way, the Centaur Scientist, a symbiotic relationship between human scientists and computers. (shrink)

Powerful new social science data resources are emerging. One particularly important source is administrative data, which were originally collected for organisational purposes but often contain information that is suitable for social science research. In this paper we outline the concept of reproducible research in relation to micro-level administrative social science data. Our central claim is that a planned and organised workflow is essential for high quality research using micro-level administrative social science data. We argue that it is essential for researchers (...) to share research code, because code sharing enables the elements of reproducible research. First, it enables results to be duplicated and therefore allows the accuracy and validity of analyses to be evaluated. Second, it facilitates further tests of the robustness of the original piece of research. Drawing on insights from computer science and other disciplines that have been engaged in e-Research we discuss and advocate the use of Git repositories to provide a useable and effective solution to research code sharing and rendering social science research using micro-level administrative data reproducible. (shrink)

The aims of this paper are threefold: To show that game-playing (GP), the discipline of Artificial Intelligence (AI) concerned with the development of automated game players, has a strong epistemological relevance within both AI and the vast area of cognitive sciences. In this context games can be seen as a way of securely reducing (segmenting) real-world complexity, thus creating the laboratory environment necessary for testing the diverse types and facets of intelligence produced by computer models. This paper aims to promote (...) the belief that games represent an excellent tool for the project of computational psychology (CP). To underline how, despite this, GP has mainly adopted an engineering-inspired methodology and in doing so has distorted the framework of cognitive functionalism. Many successes (i.e. chess, checkers) have been achieved refusing human-like reasoning. The AI has appeared to work well despite ignoring an intrinsic motivation, that of creating an explanatory link between machines and mind. To assert that substantial improvements in GP may be obtained in the future only by renewed interest in human-inspired models of reasoning and in other cognitive studies. In fact, if we increase the complexity of games (from NP-Completeness to AI-Completeness) in order to reproduce real-life problems, computer science techniques enter an impasse. Many of AI’s recent GP experiences can be shown to validate this. The lack of consistent philosophical foundations for cognitive AI and the minimal philosophical commitment of AI investigation are two of the major reasons that play an important role in explaining why CP has been overlooked. (shrink)

Devices that record from and stimulate the brain are currently available for consumer use. The increasing sophistication and resolution of these devices provide consumers with the opportunity to engage in do-it-yourself brain research and contribute to neuroscience knowledge. The rise of do-it-yourself (DIY) neuroscience may provide an enriched fund of neural data for researchers, but also raises difficult questions about data quality, standards, and the boundaries of scientific practice. We administered an online survey to brain–computer interface (BCI) researchers to gather (...) their perspectives on DIY brain research. While BCI researcher concerns about data quality and reproducibility were high, the possibility of expert validation of data generated by citizen neuroscientists mitigated concerns. We discuss survey results in the context of an established ethical framework for citizen science, and describe the potential of constructive collaboration between citizens and researchers to both increase data collection and advance understanding of how the brain operates outside the confines of the lab. (shrink)

The experience of the use of applied containerized biomedical software tools in cloud environment is summarized. The reproducibility of scientific computing in relation with modern technologies of scientific calculations is discussed. The main approaches to biomedical data preprocessing and integration in the framework of the intelligent analytical system are described. At the conditions of pandemic, the success of health care system depends significantly on the regular implementation of effective research tools and population monitoring. The earlier the risks of disease (...) can be identified, the more effective process of preventive measures or treatments can be. This publication is about the creation of a prototype for such a tool within the project «Development of methods, algorithms and intelligent analytical system for processing and analysis of heterogeneous clinical and biomedical data to improve the diagnosis of complex diseases», implementted by the V.M. Glushkov Institute of Cybernetics, National Academy of Sciences of Ukraine, together with the United Institute of Informatics Problems, National Academy of Sciences of Belarus. The insurers, entering the market, can insure mostly low risks by facilitating more frequent changes of insurers by consumers and mixing the overall health insurance market. Socio-demographic variables can be risk adjusters. Since age and gender have a relatively small explanatory power, other socio-demographic variables were studied – marital status, retirement status, disability status, educational level, income level. Because insurers have an interest in beneficial diagnoses for their policyholders, they are also interested in the ability to interpret relevant information – upcoding: insurers can encourage their policyholders to consult with doctors more often to select as many diagnoses as possible. Many countries and health care systems use diagnostic information to determine the reimbursement to a service provider, revealing the necessary data. For processing and analysis of these data, software implementations of construction for classifiers, allocation of informative features, processing of heterogeneous medical and biological variables for carrying out scientific research in the field of clinical medicine are developed. The experience of the use of applied containerized biomedical software tools in cloud environment is summarized. The reproducibility of scientific computing in relation with modern technologies of scientific calculations is discussed. Particularly, attention is paid to containerization of biomedical applications, this permits to get reproducibility of the conditions in which the calculations took place, technologies of software pipelining of calculations, that allows to organize flow calculations, and technologies for parameterization of software environment, that allows to reproduce, if necessary, an identical computing environment. The main approaches to biomedical data preprocessing and integration in the framework of the intelligent analytical system are described. The experience of using the developed linear classifier, gained during its testing on artificial and real data, allows us to conclude about several advantages provided by the containerized form of the created application: it permits to provide access to real data located in cloud environment; it is possible to perform calculations to solve research problems on cloud resources both with the help of developed tools and with the help of cloud services; such a form of research organization makes numerical experiments reproducible, i.e. any other researcher can compare the results of their developments on specific data that have already been studied by others, in order to verify the conclusions and technical feasibility of new results; there exists a universal opportunity to use the developed tools on technical devices of various classes from a personal computer to powerful cluster. (shrink)

This paper argues for the possibility of 'artificial life' and computational evolution, first by discussing (via a highly simplified version) John von Neumann's self-reproducing automaton and then by presenting some recent work focusing on computational evolution, in which 'cellular automata', a form of parallel and decentralized computing system, are evolved via 'genetic algorithms'. It is argued that such in silico experiments can help to make sense of the question of whether we can eventually build computers that are intelligent (...) and alive. (shrink)

Computational psychology refers to the effort to create computational mechanisms that, in some way, mimic mechanisms within the brain. More specifically, the goal in creating these mechanisms is to show that they can systematically reproduce patterns of human behaviour elicited under specific conditions. From this it is inferred that these mechanisms bare some similarity to the brain mechanisms that produced the human behaviours. In most cases this involves mimicking the results of psychology experiments, although it is good to (...) see in this book, two chapters discussing the application of this approach to non experimental areas. (shrink)

Farming computer games enable the ‘desk chair countryside’—millions of people actively engaged in performing farming and rural activities on-line—to co-produce their desired representations of rural life, in line with the parameters set by game creators. In this paper, I critique the narratives and images of farming life expressed in the popular computer game ‘Stardew Valley’. Stardew is based on a scenario whereby players leave a [meaningless] urban desk job to revitalize the family farm. Player are given a choice to invest (...) in the Community Center or to support ‘JojaMart’, a ‘big-box’ development. The farming narrative demonstrates the hallmarks of classical American agrarianism: farming as the basic profession on which other occupations depend, the virtue of hard work, the ‘natural’ and moral nature of agricultural life, and the economic independence of the farmer. More recent discourses of critical agrarianism are noticeably absent, particularly in relation to environmental protection. Conflict is centred on urban-based big business, whereas the farm is represented as a ‘bolt-hole’ or sanctuary from urban life. I argue that embedding issues of big-box development in gameplay enrols players in active reflection and debate on desirable responses, whereas the emphasis on reproducing classical agrarian tropes risks desensitizing game players to contemporary agrarian social and environmental justice issues. However, Stardew Valley gameplay implicitly reinforces the ideal that low input farming is the way that agriculture should be practiced. The success of the game in eliciting on-line debates, and the requirement for active performance and decision-making, demonstrates the specific potential of computer games as mediums for influencing and intervening in ongoing reworking of farming imaginaries, and enabling more critically engagement of the ‘desk chair countryside’ in important global debates. (shrink)

In the first year of life, infants' speech perception becomes attuned to the sounds of their native language. This process of early phonetic learning has traditionally been framed as phonetic category acquisition. However, recent studies have hypothesized that the attunement may instead reflect a perceptual space learning process that does not involve categories. In this article, we explore the idea of perceptual space learning by implementing five different perceptual space learning models and testing them on three phonetic contrasts that have (...) been tested in the infant speech perception literature. We reproduce and extend previous results showing that a perceptual space learning model that uses only distributional information about the acoustics of short time slices of speech can account for at least some crosslinguistic differences in infant perception. Moreover, we find that a second perceptual space learning model, which benefits from word‐level guidance. performs equally well in capturing crosslinguistic differences in infant speech perception. These results provide support for the general idea of perceptual space learning as a theory of early phonetic learning but suggest that more fine‐grained data are needed to distinguish between different formal accounts. Finally, we provide testable empirical predictions of the two most promising models and show that these are not identical, making it possible to independently evaluate each model in experiments with infants in future research. (shrink)

Akin''s determinism paradox involves a physical system that predicts its own behavior, and then spitefully defies it. Here this paradox is reformulated in purely computational language, in terms of virtual machines. The paradox is related to the theory of self-reproducing automata; and a mathematical conjecture is given which, if verified, would resolve the paradox.

Since the emergence of computing as a mode of investigation in the sciences, computational approaches have revolutionised many fields of inquiry. Recently in philosophy, the question has begun rendering bit by bit—could computation be considered a deeper fundamental building block to all of reality? This paper proposes a continuum computing construct, predicated on a set of core computational principles: computability, discretisation, stability and optimisation. The construct is applied to the set of most fundamental physical laws, in the form (...) of non-relativistic conservation equations which underpin our governing physics. The discretisation approach divides all of space into a mesh of discrete computational cells, and evolution of data in those cells must obey pre-defined stability conditions from established continuum computing theory, namely, the Courant–Friedrichs–Lewy condition. Evolving cell-state data in a manner which logically optimises computational efficiency, combined with the defined stability condition, the construct derives a central coupling of space, time, and the fastest speed of information propagation. This coupling formed at the lowest level by the computing construct, naturally and inherently produces aspects of special relativity and general relativity at the macroscale. This paper therefore proposes a new explanation of why the nature of space and time may be fused, and explores simple emergent congruities with relativity. The theory invites us to reverse the philosophical premise of the original Simulation Argument. This argument currently leads us to consider: how might all of the observed physics of our reality be reproduced computationally in a simulation of our existence? This work asks instead: could foundations of the physics of our universe—namely spacetime coupling—emerge inherently, and necessarily, from fundamental principles of computation? (shrink)

I show that Eric Winsberg’s principles of model-building given in Science in the Age of Computer Simulation are insufficient to argue for the external validation of simulation data in cases in which simulation results conflict, and that laboratory experiments have an advantage over simulations because conflicting experimental results can be decided between on the basis of reproducibility. I also argue that robustness of predictions serves the same function for simulations as repeatability does for laboratory experiments in either adjudicating between (...) conflicting results or allowing us to say that we do not have sufficient justification to validate the results. Finally, I argue for an interpretation of the argument from robustness that appeals to the convergence of many well-built and diverse models rather than the more common interpretation which appeals to the probability that one of a set of models is likely to be true. (shrink)

One recursively enumerable real α dominates another one β if there are nondecreasing recursive sequences of rational numbers (a[n] : n ∈ ω) approximating α and (b[n] : n ∈ ω) approximating β and a positive constant C such that for all n, C(α − a[n]) ≥ (β − b[n]). See [R. M. Solovay, Draft of a Paper (or Series of Papers) on Chaitin’s Work, manuscript, IBM Thomas J. Watson Research Center, Yorktown Heights, NY, 1974, p. 215] and [G. J. (...) Chaitin, IBM J. Res. Develop., 21 (1977), pp. 350–359]. We show that every recursively enumerable random real dominates all other recursively enumerable reals. We conclude that the recursively enumerable random reals are exactly the Ω-numbers [G. J. Chaitin, IBM J. Res. Develop., 21 (1977), pp. 350–359]. Second, we show that the sets in a universal Martin-Lof test for randomness have random measure, and every recursively enumerable random number is the sum of the measures represented in a universal Martin-Lof test. (shrink)

Since its conception nearly 20 years ago, Logic Programming - the idea of using logic as a programming language - has been developed to the point where it now plays an important role in areas such as database theory, artificial intelligence and software engineering. However, there are still many challenging research issues to be addressed and the UK branch of the Association for Logic Programming was set up to provide a forum where the flourishing research community could discuss important issues (...) of Logic Programming which were often by-passed at the large international conferences. This volume contains the twelve papers which were presented at the ALPUK's 3rd conference which was held in Edinburgh, 10-12 April 1991. The aim of the conference was to give a broad but detailed technical insight into the work currently being done in this field, both in the UK and by researchers as far afield as Canada and Bulgaria. The breadth of interest in this area of Computer Science is reflected in the range of the papers which cover - amongst other areas - massively parallel implementation, constraint logic programming, circuit modelling, algebraic proof of program properties, deductive databases, specialised editors and standardisation. The resulting volume gives a good overview of the current progress being made in the field and will be of interest to researchers and students of any aspects of logic programming, parallel computing or database techniques and management. (shrink)

This book constitutes the strictly refereed post-workshop proceedings of the 11th International Workshop on Computer Science Logic, CSL '97, held as the 1997 Annual Conference of the European Association on Computer Science Logic, EACSL, in Aarhus, Denmark, in August 1997. The volume presents 26 revised full papers selected after two rounds of refereeing from initially 92 submissions; also included are four invited papers. The book addresses all current aspects of computer science logics and its applications and thus presents the state (...) of the art in the area. (shrink)

This paper examines a looming reproducibility crisis in the core of the hard sciences. Namely, it concentrates on molecular modeling and simulation (MMS), a family of methods that predict properties of substances through computing interactions on a molecular level and that is widely popular in physics, chemistry, materials science, and engineering. The paper argues that in order to make quantitative predictions, sophisticated models are needed which have to be evaluated with complex simulation procedures that amalgamate theoretical, technological, and social (...) factors – leading to problems with reproducibility. Thus, for methodological reasons, the predictive success causes a reproducibility problem. (shrink)

How humans and technology evolve together in a creative partnership. In this book, Edward Ashford Lee makes a bold claim: that the creators of digital technology have an unsurpassed medium for creativity. Technology has advanced to the point where progress seems limited not by physical constraints but the human imagination. Writing for both literate technologists and numerate humanists, Lee makes a case for engineering—creating technology—as a deeply intellectual and fundamentally creative process. Explaining why digital technology has been so transformative and (...) so liberating, Lee argues that the real power of technology stems from its partnership with humans. Lee explores the ways that engineers use models and abstraction to build inventive artificial worlds and to give us things that we never dreamed of—for example, the ability to carry in our pockets everything humans have ever published. But he also attempts to counter the runaway enthusiasm of some technology boosters who claim everything in the physical world is a computation—that even such complex phenomena as human cognition are software operating on digital data. Lee argues that the evidence for this is weak, and the likelihood that nature has limited itself to processes that conform to today's notion of digital computation is remote. Lee goes on to argue that artificial intelligence's goal of reproducing human cognitive functions in computers vastly underestimates the potential of computers. In his view, technology is coevolving with humans. It augments our cognitive and physical capabilities while we nurture, develop, and propagate the technology itself. Complementarity is more likely than competition. (shrink)

Digital artifacts — humanly-constructed items that inhabit our computers and networks — suffer an unfortunate reputation as being virtual and therefore unreal, and all too easy to reproduce on the cheap. These features together prompt the question of this article: if digital artifacts can be reproduced for free, and if they are unreal, why do they have economic value at all? Using a focal case study of bitcoin — the most unreal digital artifact of them all, and one that has (...) been copied and pasted a thousand times over — I answer the question. Some digital artifacts can't be copied on the cheap, as it turns out, and they are real enough to be useful. (shrink)

I argue that our judgements regarding the locally causal models that are compatible with a given constraint implicitly depend, in part, on the context of inquiry. It follows from this that certain quantum no-go theorems, which are particularly striking in the traditional foundational context, have no force when the context switches to a discussion of the physical systems we are capable of building with the aim of classically reproducing quantum statistics. I close with a general discussion of the possible implications (...) of this for our understanding of the limits of classical description, and for our understanding of the fundamental aim of physical investigation. _1_ Introduction _2_ No-Go Results _2.1_ The CHSH inequality _2.2_ The GHZ equality _3_ Classically Simulating Quantum Statistics _3.1_ GHZ statistics _3.2_ Singlet statistics _4_ What Is a Classical Computer Simulation? _5_ Comparing the All-or-Nothing GHZ with Statistical equalities _6_ General Discussion _7_ Conclusion. (shrink)

This article holds that governmental investments in quantum technologies speak to the imaginable futures of digital sovereignty and digital infrastructures, two major areas of change driven by related technologies like AI and Big Data, among other things, in international law today. Under intense development today for future interpolation into digital systems that they may alter, quantum technologies occupy a sort of liminal position, rooted in existing assemblages of computational technologies while pointing to new horizons for them. The possibilities they (...) raise are neither certain nor determinate, but active investments in them (legal, political and material investments) offer perspective on digital technology-driven influences on an international legal imagination. In contributing to visions of the future that are guiding ambitions for digital sovereignty and digital infrastructures, quantum technologies condition digital technology-driven changes to international law and legal imagination in the present. Privileging observation and description, I adapt and utilize a diffractive method with the aim to discern what emerges out of the interference among the several related things assembled for this article, including material technologies and legal institutions. In conclusion, I observe ambivalent changes to an international legal imagination, changes which promise transformation but appear nonetheless to reproduce current distributions of power and resources. (shrink)

The current assumptions of knowledge acquisition brought about the crisis in the reproducibility of experiments. A complementary perspective should account for the specific causality characteristic of life by integrating past, present, and future. A “second Cartesian revolution,” informed by and in awareness of anticipatory processes, should result in scientific methods that transcend the theology of determinism and reductionism. In our days, science, itself an expression of anticipatory activity, makes possible alternative understandings of reality and its dynamics. For this purpose, (...) the study advances G-complexity for defining and comparing decidable and undecidable knowledge. AI and related computational expressions of knowledge could benefit from the awareness of what distinguishes the dynamics of life from any other expressions of change. (shrink)

This essay hypothesizes that the Universe contains a self-reproducing neural network of Black Holes with computational abilities—i.e., the Universe can “think”! It then rephrases the Final Anthropic Principle to state: “Intelligent information-processing must come into existence in each new Universe to assure the birth of intelligent successor universes”. Continued research into the theory of Early Universe and Black Hole information storage, processing and retrieval is recommended, as are observational searches for time-correlated electromagnetic and gravitational wave emission patterns from widely (...) separated Black Hole transient events indicative of the existence of a universal inter-Black Hole faster-than-light communications network. (shrink)

Agreement attraction is a cross-linguistic phenomenon where a verb occasionally agrees not with its subject, as required by grammar, but instead with an unrelated noun (“The key to the cabinets were…”). Despite the clear violation of grammatical rules, comprehenders often rate these sentences as acceptable. Contenders for explaining agreement attraction fall into two broad classes: Morphosyntactic accounts specifically designed to explain agreement attraction, and more general sentence processing models, such as the Lewis and Vasishth model, which explain attraction as a (...) consequence of how linguistic structure is stored and accessed in content-addressable memory. In the present research, we disambiguate between these two classes by testing a surprising prediction made by the Lewis and Vasishth model but not by the morphosyntactic accounts, namely, that attraction should not be limited to morphosyntax, but that semantic features of unrelated nouns equally induce attraction. A recent study by Cunnings and Sturt provided initial evidence that this may be the case. Here, we report three single-trial experiments in English that compared semantic and agreement attraction and tested whether and how the two interact. All three experiments showed strong semantically induced attraction effects closely mirroring agreement attraction effects. We complement these results with computational simulations which confirmed that the Lewis and Vasishth model can faithfully reproduce the observed results. In sum, our findings suggest that attraction is a more general phenomenon than is commonly believed, and therefore favor more general sentence processing models, such as the Lewis and Vasishth model. (shrink)

Against Maudlin, I argue that machines which merely reproduce a pre-programmed series of changes ought to be classed with Turing’s O-Machines even if they would counterfactually show Turing Machine-like activity. This can be seen on an interventionist picture of computational architectures, on which basic operations are the primitive loci for interventions. While constructions like Maudlin’s Olympia still compute, then, claims about them do not threaten philosophical arguments that depend on Turing Machine architectures and their computational equivalents.

This paper concerns Alan Turing’s ideas about machines, mathematical methods of proof, and intelligence. By the late 1930s, Kurt Gödel and other logicians, including Turing himself, had shown that no finite set of rules could be used to generate all true mathematical statements. Yet according to Turing, there was no upper bound to the number of mathematical truths provable by intelligent human beings, for they could invent new rules and methods of proof. So, the output of a human mathematician, for (...) Turing, was not a computable sequence (i.e., one that could be generated by a Turing machine). Since computers only contained a finite number of instructions (or programs), one might argue, they could not reproduce human intelligence. Turing called this the “mathematical
objection” to his view that machines can think. Logico-mathematical reasons, stemming from his own work, helped to convince Turing that it should be possible to reproduce human intelligence, and eventually compete with it, by developing the appropriate kind of digital computer. He felt it
should be possible to program a computer so that it could learn or discover new rules, overcoming the limitations imposed by the incompleteness and undecidability results in the same way that human
mathematicians presumably do. (shrink)

This article presents research into human mental spatial reasoning with orientation knowledge. In particular, we look at reasoning problems about cardinal directions that possess multiple valid solutions , at human preferences for some of these solutions, and at representational and procedural factors that lead to such preferences. The article presents, first, a discussion of existing, related conceptual and computational approaches; second, results of empirical research into the solution preferences that human reasoners actually have; and, third, a novel computational (...) model that relies on a parsimonious and flexible spatio-analogical knowledge representation structure to robustly reproduce the behavior observed with human reasoners. (shrink)

At first thought, iteration seems banal. It is about repeating the existing; nothing is changing. But this special issue shows that, in an era obsessed with the new, it is often the repetition of the old that creates social change. Iteration fosters persuasion. It affords opportunities for critical and creative engagement with meaning, values and knowledge. It invites collaboration, though its apparent simplicity often belies a tremendous amount of individual and collective labour involved in the practices of iteration. Through its (...) repetition of the existing, however, iteration also can be a mechanism for reproducing the status quo. Its pervasiveness and banality naturalises power, and its mimetic qualities shrink spaces for critical distance and care. The editors of this special issue have brought together a delightful and fascinating diversity of articles focussed on iteration in cultural production in the digital age. We hop across geographies to examine lockdown diaries, artists’ books, socialist memes, fake news, the design of social media platforms and artificial intelligence, activism, film, social media forum moderation, news website reader comments and more. Iterating through the collection as a whole, across its many disciplines, is a commitment to theorising through empirical evidence, to explaining with critique, and to providing pathways to praxis. These characteristics of this special issue, and the many concepts and arguments it puts forward, make this collection of work exceptionally rich material for seeing iteration and how it shapes the world we live in today, as well as the world we want it to be. In this preface, I take a media sociology approach to show how iteration can be usefully understood as collaborative communication for change. I see this understanding of iteration, whose ascendancy is related to the ascendancy of computer science, as baked into the form of communication technologies—and thus as shaping the kinds of iteration that are possible when we use these technologies. This understanding also prompts us to focus on the connection between iteration and social change. To explore how this works, I analytically slow down the practice of iteration to show that it is a communication practice of transmission. That transmission practice is itself constituted of cognate communication practices—the reception, evaluation and production of knowledge—in which visibility and persuasion are key. In the latter parts of the preface, I illustrate this through the example of witnessing as iteration, as the high-stakes nature of witnessing make it a canary in the coalmine, more generally, for mediated communication in the digital age. I show how breaking the witnessing practice down into its various parts allows us to see how power enters and inflects who and what are iterated, when—and who and what are not. Thinking critically with iteration and against unequal power relations, the praxis this preface suggests is one—much in line with the rest of this special issue—of explaining how iteration might move the grassroots towards their goals. (shrink)

We explore the interaction between oculomotor control and language comprehension on the sentence level using two well-tested computational accounts of parsing difficulty. Previous work (Boston, Hale, Vasishth, & Kliegl, 2011) has shown that surprisal (Hale, 2001; Levy, 2008) and cue-based memory retrieval (Lewis & Vasishth, 2005) are significant and complementary predictors of reading time in an eyetracking corpus. It remains an open question how the sentence processor interacts with oculomotor control. Using a simple linking hypothesis proposed in Reichle, Warren, (...) and McConnell (2009), we integrated both measures with the eye movement model EMMA (Salvucci, 2001) inside the cognitive architecture ACT-R (Anderson et al., 2004). We built a reading model that could initiate short “Time Out regressions” (Mitchell, Shen, Green, & Hodgson, 2008) that compensate for slow postlexical processing. This simple interaction enabled the model to predict the re-reading of words based on parsing difficulty. The model was evaluated in different configurations on the prediction of frequency effects on the Potsdam Sentence Corpus. The extension of EMMA with postlexical processing improved its predictions and reproduced re-reading rates and durations with a reasonable fit to the data. This demonstration, based on simple and independently motivated assumptions, serves as a foundational step toward a precise investigation of the interaction between high-level language processing and eye movement control. (shrink)