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)

Computer simulations and experiments share many important features. One way of explaining the similarities is to say that computer simulations just are experiments. This claim is quite popular in the literature. The aim of this paper is to argue against the claim and to develop an alternative explanation of why computer simulations resemble experiments. To this purpose, experiment is characterized in terms of an intervention on a system and of the observation of the reaction. Thus, if computer simulations are experiments, (...) either the computer hardware or the target system must be intervened on and observed. I argue against the first option using the non-observation argument, among others. The second option is excluded by e.g. the over-control argument, which stresses epistemological differences between experiments and simulations. To account for the similarities between experiments and computer simulations, I propose to say that computer simulations can model possible experiments and do in fact often do so. (shrink)

Epistemic Responsibility means that scientists are responsible for their research being suitable to contribute to our understanding of the world, or at least some part of the world. As will be shown with the example of computer simulations in social sciences, this is unfortunately far from being understood as a matter of course. Rather, there exist whole research traditions in which the bulk of the contributions is quite free from any tangible purpose of enhancing our knowledge about anything. This essay (...) is concerned with the causes of this phenomenon and pleads for taking epistemic responsibility as a scientific virtue serious. Science should be organized in such a way that it is possible and likely that scientists will assume epistemic responsibility for their research tasks. (shrink)

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Over the course of the last three decades, computer simulations have become a major tool of doing science and engaging with the world, not least in an effort to predict and intervene in a future to come. Born in the context of the Second World War and the discipline of physics, simulations have long spread into most diverse fields of enquiry and technological application. This paper introduces a topical collection focussing on simulations in the life sciences. Echoing the current state (...) of tinkering, fast developments, segmentation of knowledge and interdisciplinary collaboration, and in an effort to bridge the science-humanities divide, the contributors to this collection come from multiple disciplinary backgrounds, including information studies, cognitive sciences, philosophy and biology. The ambiguous character of simulations, their cutting across scientific disciplines, analysis and prediction, understanding and doing, gave rise to their success in contemporary life sciences and has been the object of much scientific debate. One of the main aims of this topical collection, by contrast, is to call into question the assumption of an obvious use and easy transfer of methods between fields of knowledge as diverse as, e.g. physics and biology. The collection presents historical case studies from various biological sub-fields. The articles study how simulations are used and the ways they contribute specifically to our understanding of life. Taking up Sergio Sismondo’s description of simulations as “compromises” and “glue”, they also critically engage with the question of what exactly the life sciences have been gluing together over the last two decades. (shrink)

Where should computer simulations be located on the ‘usual methodological map’ which distinguishes experiment from theory? Specifically, do simulations ultimately qualify as experiments or as thought experiments? Ever since Galison raised that question, a passionate debate has developed, pushing many issues to the forefront of discussions concerning the epistemology and methodology of computer simulation. This review article illuminates the positions in that debate, evaluates the discourse and gives an outlook on questions that have not yet been addressed.

Although the widespread use of the term “Big Data” is comparatively recent, it invokes a phenomenon in the developments of database technology with distinct historical contexts. The database engineer Jim Gray, known as “Mr. Database” in Silicon Valley before his disappearance at sea in 2007, was involved in many of the crucial developments since the 1970s that constitute the foundation of exceedingly large and distributed databases. Jim Gray was involved in the development of relational database systems based on the concepts (...) of Edgar F. Codd at IBM in the 1970s before he went on to develop principles of Transaction Processing that enable the parallel and highly distributed performance of databases today. He was also involved in creating forums for discourse between academia and industry, which influenced industry performance standards as well as database research agendas. As a co-founder of the San Francisco branch of Microsoft Research, Gray increasingly turned toward scientific applications of database technologies, e. g. leading the TerraServer project, an online database of satellite images. Inspired by Vannevar Bush’s idea of the memex, Gray laid out his vision of a Personal Memex as well as a World Memex, eventually postulating a new era of data-based scientific discovery termed “Fourth Paradigm Science”. This article gives an overview of Gray’s contributions to the development of database technology as well as his research agendas and shows that central notions of Big Data have been occupying database engineers for much longer than the actual term has been in use. (shrink)

Although the widespread use of the term “Big Data” is comparatively recent, it invokes a phenomenon in the developments of database technology with distinct historical contexts. The database engineer Jim Gray, known as “Mr. Database” in Silicon Valley before his disappearance at sea in 2007, was involved in many of the crucial developments since the 1970s that constitute the foundation of exceedingly large and distributed databases. Jim Gray was involved in the development of relational database systems based on the concepts (...) of Edgar F. Codd at IBM in the 1970s before he went on to develop principles of Transaction Processing that enable the parallel and highly distributed performance of databases today. He was also involved in creating forums for discourse between academia and industry, which influenced industry performance standards as well as database research agendas. As a co-founder of the San Francisco branch of Microsoft Research, Gray increasingly turned toward scientific applications of database technologies, e. g. leading the TerraServer project, an online database of satellite images. Inspired by Vannevar Bush’s idea of the memex, Gray laid out his vision of a Personal Memex as well as a World Memex, eventually postulating a new era of data-based scientific discovery termed “Fourth Paradigm Science”. This article gives an overview of Gray’s contributions to the development of database technology as well as his research agendas and shows that central notions of Big Data have been occupying database engineers for much longer than the actual term has been in use. (shrink)

Bruno Latour once argued that science laboratories actively modify the wider society by displacing crucial actors outside the laboratory into the “field.” This article turns this idea on its head by using the case of geothermal energy utilization to demonstrate that in many cases it is the experimental setup outside the laboratory that is there first, with the activities normally associated with a laboratory setting only being decided upon and implemented post hoc. As soon as the actors involved perceive unknowns (...) and uncertainties, these are relocated to various kinds of closed laboratories to be dealt with in a more controlled environment. This is done, for instance, by inviting stakeholders to laboratory-like settings or by analyzing the geochemical composition of fluids in laboratories. Thus, the risk-laden production of new knowledge by means of real-world experimentation amounts to a practice of relocating the context of discovery in society to laboratories of justification sometimes defined as such post hoc. Experimental processes in society can then be conceptualized as “real” experiments and laboratory activities as merely temporarily subordinated components of the larger experiment. (shrink)

Continuous culture techniques were developed in the early twentieth century to replace cumbersome studies of cell growth in batch cultures. In contrast to batch cultures, they constituted an open concept, as cells are forced to proliferate by adding new medium while cell suspension is constantly removed. During the 1940s and 1950s new devices have been designed—called “automatic syringe mechanism,” “turbidostat,” “chemostat,” “bactogen,” and “microbial auxanometer”—which allowed increasingly accurate quantitative measurements of bacterial growth. With these devices cell growth came under the (...) external control of the experimenters and thus accessible for developing a mathematical theory of growth kinetics—developed mainly by Jacques Monod, Aron Novick and Leo Szilard in the early 1950s and still in use today. The paper explores the development of continuous culture devices and claims that these devices are simulators for standard cells following specific requirements, in particular involving mathematical constraints in the design and setting of the devices as well as experiments. These requirements have led to contemporary designs of continuous culture techniques realizing a specific event-based flow algorithm able to simulate directed evolution and produce artificial cells and microorganisms. This current development is seen as an alternative approach to today’s synthetic biology. (shrink)

Data-intensive techniques, now widely referred to as 'big data', allow for novel ways to address complexity in science. I assess their impact on the scientific method. First, big-data science is distinguished from other scientific uses of information technologies, in particular from computer simulations. Then, I sketch the complex and contextual nature of the laws established by data-intensive methods and relate them to a specific concept of causality, thereby dispelling the popular myth that big data is only concerned with correlations. The (...) modeling in data-intensive science is characterized as 'horizontal'—lacking the hierarchical, nested structure familiar from more conventional approaches. The significance of the transition from hierarchical to horizontal modeling is underlined by a concurrent paradigm shift in statistics from parametric to non-parametric methods. (shrink)

There is an ongoing debate on whether or to what degree computer simulations can be likened to experiments. Many philosophers are sceptical whether a strict separation between the two categories is possible and deny that the materiality of experiments makes a difference (Morrison 2009, Parker 2009, Winsberg 2010). Some also like to describe computer simulations as a “third way” between experimental and theoretical research (Rohrlich 1990, Axelrod 2003, Kueppers/Lenhard 2005). In this article I defend the view that computer simulations are (...) not experiments but that they are tools for evaluating the consequences of theories and theoretical assumptions. In order to do so the (alleged) similarities and differences between simulations and experiments are examined. It is found that three fundamental differences between simulations and experiments remain: 1) Only experiments can generate new empirical data. 2) Only Experiments can operate directly on the target system. 3) Experiments alone can be employed for testing fundamental hypotheses. As a consequence, experiments enjoy a distinct epistemic role in science that cannot completely be superseded by computer simulations. This finding in connection with a discussion of border cases such as hybrid methods that combine measurement with simulation shows that computer simulations can clearly be distinguished from empirical methods. It is important to understand that computer simulations are not experiments, because otherwise there is a danger of systematically underestimating the need for empirical validation of simulations. (shrink)