It is often claimed that scientists can obtain new knowledge about nature by running computer simulations. How is this possible? I answer this question by arguing that computer simulations are arguments. This view parallels Norton’s argument view about thought experiments. I show that computer simulations can be reconstructed as arguments that fully capture the epistemic power of the simulations. Assuming the extended mind hypothesis, I furthermore argue that running the computer simulation is to execute the reconstructing argument. I discuss some (...) objections and reject the view that computer simulations produce knowledge because they are experiments. I conclude by comparing thought experiments and computer simulations, assuming that both are arguments. (shrink)

Consider the argument: Circus-1 Men in clown suits are handing out tickets. So, probably: Circus-2 There’s a circus in town. So: Circus-3 There’s an entertainment venue in town. Presumably you’d be able to warrantedly believe Circus-2 on the basis of Circus-1. And we can suppose you’re reasonably certain that wherever there are circuses, there are entertainment venues. So you’d seem to be in a position to reasonably go on to infer Circus-3.

Software is a ubiquitous artifact, yet not much has been done to understand its ontological nature. There are a few accounts offered so far about the nature of software. I argue that none of those accounts give a plausible picture of the nature of software. I draw attention to the striking similarities between software and musical works. These similarities motivate to look more closely on the discussions regarding the nature of the musical works. With the lessons drawn from the ontology (...) of musical works I offer a novel account of the nature of software. In this account, software is an abstract artifact. I elaborate the conditions under which software comes into existence; how it persists; how and on which entities its existence depends. (shrink)

This paper argues that the difference between contemporary software intensive scientific practice and more traditional non-software intensive varieties results from the characteristically high conditionality of software. We explain why the path complexity of programs with high conditionality imposes limits on standard error correction techniques and why this matters. While it is possible, in general, to characterize the error distribution in inquiry that does not involve high conditionality, we cannot characterize the error distribution in inquiry that depends on software. Software intensive (...) science presents distinctive error and uncertainty modalities that pose new challenges for the epistemology of science. (shrink)

Artefacts do not always do what they are supposed to, due to a variety of reasons, including manufacturing problems, poor maintenance, and normal wear-and-tear. Since software is an artefact, it should be subject to malfunctioning in the same sense in which other artefacts can malfunction. Yet, whether software is on a par with other artefacts when it comes to malfunctioning crucially depends on the abstraction used in the analysis. We distinguish between “negative” and “positive” notions of malfunction. A negative malfunction, (...) or dysfunction, occurs when an artefact token either does not or cannot do what it is supposed to. A positive malfunction, or misfunction, occurs when an artefact token may do what is supposed to but, at least occasionally, it also yields some unintended and undesirable effects. We argue that software, understood as type, may misfunction in some limited sense, but cannot dysfunction. Accordingly, one should distinguish software from other technical artefacts, in view of their design that makes dysfunction impossible for the former, while possible for the latter. (shrink)

After showing how Deborah Mayo’s error-statistical philosophy of science might be applied to address important questions about the evidential status of computer simulation results, I argue that an error-statistical perspective offers an interesting new way of thinking about computer simulation models and has the potential to significantly improve the practice of simulation model evaluation. Though intended primarily as a contribution to the epistemology of simulation, the analysis also serves to fill in details of Mayo’s epistemology of experiment.

This essay warns of eroding accountability in computerized societies. It argues that assumptions about computing and features of situations in which computers are produced create barriers to accountability. Drawing on philosophical analyses of moral blame and responsibility, four barriers are identified: 1) the problem of many hands, 2) the problem of bugs, 3) blaming the computer, and 4) software ownership without liability. The paper concludes with ideas on how to reverse this trend.

Thought experiments and simulation experiments are compared and contrasted with each other. While the former rely on epistemic transparency as a working condition, in the latter complexity of model dynamics leads to epistemic opacity. The difference is elucidated by a discussion of the different kinds of iteration that are at work in both sorts of experiment.

Structural and functional descriptions of technical artefacts play an important role in engineering practice. A complete description of a technical artefact involves a description of both functional and structural features. Engineers, moreover, assume that there is an intimate relationship between the function and structure of technical artefacts and they reason from functional properties to structural ones and vice versa. This raises the question of how structural and functional descriptions are related. The kind of inference patterns that establish coherence between structural (...) and functional descriptions are explored in this paper, using the analysis of coherence creating relations of Thagard et al. Explanatory, analogical and practical inference patterns are discussed and it is argued that of these three, practical inferences may be the most important. Practical inferences, however, cannot provide a full underpinning of the coherence of structural and functional descriptions of technical artefacts. The paper ends with the suggestion that any account of the coherence of the structural and functional descriptions of technical artefacts must involve reference to their intentional features.Keywords: Technical artefact; Structural description; Functional description; Coherence relation; Practical inference. (shrink)

Over the course of the twentieth century, hydraulic engineering has come to rely primarily on the use of computational models. Disco and van den Ende hint towards the reasons for widespread adoption of computational models by pointing out that such models fulfill a crucial role as management tools in Dutch water management, and meet a more general desire to quantify water-related phenomena. The successful application of computational models implies blackboxing : “[w]hen a machine runs efficiently … one need focus only (...) on its inputs and outputs and not on its internal complexity. Thus, paradoxically, the more science and technology succeed, the more opaque and obscure they become”.. (shrink)

Reasons are given to justify the claim that computer simulations and computational science constitute a distinctively new set of scientific methods and that these methods introduce new issues in the philosophy of science. These issues are both epistemological and methodological in kind.

This paper provides a reply to articles by Nicola Angius and Guiseppe Primiero responding to our paper “Software Intensive Science”.

Several philosophical issues in connection with computer simulations rely on the assumption that results of simulations are trustworthy. Examples of these include the debate on the experimental role of computer simulations :483–496, 2009; Morrison in Philos Stud 143:33–57, 2009), the nature of computer data Computer simulations and the changing face of scientific experimentation, Cambridge Scholars Publishing, Barcelona, 2013; Humphreys, in: Durán, Arnold Computer simulations and the changing face of scientific experimentation, Cambridge Scholars Publishing, Barcelona, 2013), and the explanatory power of (...) computer simulations :277–292, 2008; Durán in Int Stud Philos Sci 31:27–45, 2017). The aim of this article is to show that these authors are right in assuming that results of computer simulations are to be trusted when computer simulations are reliable processes. After a short reconstruction of the problem of epistemic opacity, the article elaborates extensively on computational reliabilism, a specified form of process reliabilism with computer simulations located at the center. The article ends with a discussion of four sources for computational reliabilism, namely, verification and validation, robustness analysis for computer simulations, a history of successful implementations, and the role of expert knowledge in simulations. (shrink)

Simulation techniques, especially those implemented on a computer, are frequently employed in natural as well as in social sciences with considerable success. There is mounting evidence that the "model-building era" (J. Niehans) that dominated the theoretical activities of the sciences for a long time is about to be succeeded or at least lastingly supplemented by the "simulation era". But what exactly are models? What is a simulation and what is the difference and the relation between a model and a simulation? (...) These are some of the questions addressed in this article. I maintain that the most significant feature of a simulation is that it allows scientists to imitate one process by another process. "Process" here refers solely to a temporal sequence of states of a system. Given the observation that processes are dealt with by all sorts of scientists, it is apparent that simulations prove to be a powerful interdisciplinarily acknowledged tool. Accordingly, simulations are best suited to investigate the various research strategies in different sciences more carefully. To this end, I focus on the function of simulations in the research process. Finally, a somewhat detailed case-study from nuclear physics is presented which, in my view, illustrates elements of a typical simulation in physics. (shrink)