When did the concept of model begin to be used in mathematics? This question appears at first somewhat surprising since “model” is such a standard term now in the discourse on mathematics and “modelling” such a standard activity that it seems to be well established since long. The paper shows that the term— in the intended epistemological meaning—emerged rather recently and tries to reveal in which mathematical contexts it became established. The paper discusses various layers of argumentations and reflections in (...) order to unravel and reach the pertinent kernel of the issue. The specific points of this paper are the difference in the epistemological concept to the usually discussed notions of model and the difference between conceptions implied in mathematical practices and their becoming conscious in proper reflections of mathematicians. (shrink)

A number of recent discussions comparing computer simulation and traditional experimentation have focused on the significance of “materiality.” I challenge several claims emerging from this work and suggest that computer simulation studies are material experiments in a straightforward sense. After discussing some of the implications of this material status for the epistemology of computer simulation, I consider the extent to which materiality (in a particular sense) is important when it comes to making justified inferences about target systems on the basis (...) of experimental results. (shrink)

This book defends a two-tiered account of epistemic representation--the sort of representation relation that holds between representations such as maps and scientific models and their targets. It defends a interpretational account of epistemic representation and a structural similarity account of overall faithful epistemic representation.

This contribution examines the media history of swarm research and the significance of swarming techniques to current socio-technological processes. It explores how the procedures of swarm intelligence should be understood in relation to the concept of cultural techniques. This brings the concept into proximity with recent debates in posthuman (media) theory, animal studies and software studies. Swarms are conceptualized as zootechnologies that resist methods of analytical investigation. Synthetic swarms first emerged as operational collective structures by means of the reciprocal computerization (...) of biology and biologization of computer science. In a recursive loop, swarms inspired agent-based modelling, which in turn provided biological researchers with enduring knowledge about dynamic collectives. This conglomerate led to the development of advanced, software-based ‘particle systems’. Swarm intelligence has become a fundamental cultural technique related to dynamic processes and an effective metaphor for the collaborative efforts of society. (shrink)

ArgumentThis paper is the first part of a two-part examination of computer modeling practice and philosophy. It discusses electrical engineer Jay Forrester's work on Industrial Dynamics, later called System Dynamics. Forrester developed Industrial Dynamics after being recruited to the newly-established School of Industrial Management at the Massachusetts Institute of Technology (MIT), which had been seeking a novel pedagogical program for management for five years before Forrester's arrival. We argue that Industrial Dynamics should be regarded in light of this institutional context. (...) Unlike economics, as well as operations research and management science (twin fields which were also gaining traction at that time), Industrial Dynamics was not meant to be applied by mathematicians and technical specialists in consultation with managers, but by managers themselves. This concern shifted the emphasis in modeling from the specialist act of analysis of a crucial problem to the managerial act of choosing which problems were crucial and developing effective policies around them. (shrink)

Both the irreducible complexity of biological phenomena and the aim of a universalized biology (life-as-it-could-be) have lead to a deep methodological shift in the study of life; represented by the appearance of ALife, with its claim that computational modelling is the main tool for studying the general principles of biological phenomenology. However this methodological shift implies important questions concerning the aesthetic, engineering and specially the epistemological status of computational models in scientific research: halfway between the well established categories of theory (...) and experiment. ALife models become powerful epistemic artefacts allowing the simulation of emergent phenomena, the interaction between different levels of organization and the integration of different causal factors in the very same manipulable object. The use of computational models in ALife can be classified in four main categories depending on their position between theoretical and empirical practices: generic, conceptual, functional and mechanistic. For each of these categories we analyse their epistemic value and select paradigmatic examples that illustrate how ALife models can be fruitfully inserted in the study of life. (shrink)

A chronicled approach to the notion of computer simulations shows that there are two predominant interpretations in the specialized literature. According to the first interpretation, computer simulations are techniques for finding the set of solutions to a mathematical model. I call this first interpretation the problem-solving technique viewpoint. In its second interpretation, computer simulations are considered to describe patterns of behavior of a target system. I call this second interpretation the description of patterns of behavior viewpoint of computer simulations. This (...) article explores these two interpretations of computer simulations from three different angles. First, I collect a series of definitions of computer simulation from the historical record. I track back definitions to the early 1960s and show how each viewpoint shares similar interpretations of computer simulations—ultimately clustering into the two viewpoints aforementioned. This reconstruction also includes the most recent literature. Second, I unpack the philosophical assumptions behind each viewpoint, with a special emphasis on their differences. Third, I discuss the philosophical implications of each viewpoint in the context of the recent discussion on the logic of scientific explanation for computer simulations. (shrink)

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)

Computer simulations are an exciting tool that plays important roles in many scientific disciplines. This has attracted the attention of a number of philosophers of science. The main tenor in this literature is that computer simulations not only constitute interesting and powerful new science , but that they also raise a host of new philosophical issues. The protagonists in this debate claim no less than that simulations call into question our philosophical understanding of scientific ontology, the epistemology and semantics of (...) models and theories, and the relation between experimentation and theorising, and submit that simulations demand a fundamentally new philosophy of science in many respects. The aim of this paper is to critically evaluate these claims. Our conclusion will be sober. We argue that these claims are overblown and that simulations, far from demanding a new metaphysics, epistemology, semantics and methodology, raise few if any new philosophical problems. The philosophical problems that do come up in connection with simulations are not specific to simulations and most of them are variants of problems that have been discussed in other contexts before. (shrink)

Simulation research has become a growing area of interest in the social studies in recent years. Problematically, the term simulation is used without consistency among practitioners and researchers. The conceptual confusion regarding what simulations are (or are not) muddies the field and makes it difficult for scholars to make sense of this phenomenon or to talk about simulations across findings. In order to bring clarity to the field, this paper is framed around two conceptual and analytic constructs: conceptual analysis and (...) the theory of language games. In this paper, I will provide a rationale for why the social studies field requires a specific definition for simulations. Next, simulations will be defined using four specific criteria: verisimilitude, dynamism, active human agents, and pedagogical mediation. Finally, simulations will be differentiated from three related phenomena with which they are often conflated: games, role-plays, and models. (shrink)

This article addresses the role of science and science advisory bodies in modeling practices for the support of policy-making procedures in the Netherlands in the field of health care. The authors show, based on a detailed investigation of a prestigious interdisciplinary modeling project in which an economic care model was developed for governmental use, that science advisory bodies are entangled with the policy actors they advise in what we call boundary configurations. Boundary configurations are strongly situated interconnections between science advisory (...) institutes and policy institutions that share a specific approach to problem definitions and methods and that are embedded in specific social, discursive, and material elements. Importantly, as the case study shows, such boundary configurations shape the kind of science, and related, the kind of social and political theories about the world, that is effectuated in modeling practices. In this case study, the boundary configuration in which economic experts and policy makers participated contributed to the articulation of health care in terms of a market-based policy program for the health care sector. (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)

How climate models came to gain and exercise epistemic authority has been a key concern of recent climate change historiography. Using newly released archival materials and recently conducted interviews with key actors, we reconstruct negotiations between UK climate scientists and policymakers which led to the opening of the Hadley Centre for Climate Prediction and Research in 1990. We historicize earlier arguments about the unique institutional culture of the Hadley Centre, and link this culture to broader characteristics of UK regulatory practice (...) and environmental politics. A product of a particular time and place, the Hadley Centre was shaped not just by scientific ambition, but by a Conservative governmental preference for ‘sound science’ and high evidential standards in environmental policymaking. Civil servants sought a prediction programme which would appeal to such sensibilities, with transient and regional climate simulation techniques seemingly offering both scientific prestige and persuasive power. Beyond the national level, we also offer new insights into the early role of the Intergovernmental Panel on Climate Change and an evolving international political context in the shaping of scientific practices and institutions. (shrink)

Complexity sciences have become famous worldwide thanks to several popular books that served as echo chambers of their promises. These consisted in departing from “classical science” defined as deterministic, reductionist, analytic and mono-disciplinary. Their founders and supporters declared that complexity sciences were going to give rise (or that they have given rise) to a post-Laplacian, antireductionist, holistic and interdisciplinary approach. By taking a closer look at their content and practices, I argue in this article that, because of their physics-oriented, computationalist, (...) and mathematical assumptions, complexity sciences have paradoxically produced knowledge at odds with these four tenets. (shrink)

Since their inception in the 1980s, complexity sciences have been described as a revolutionary new domain of research. By describing some of the practices and assumptions of its representatives, the present article shows that this field is an association of subdisciplines laying on existing disciplinary footholds. The general question guiding us here is: On what basis do complexity scientists consider their inquiry methods and results as valuable? To answer it, I describe five “epistemic argumentative regimes,” namely the ways in which (...) complexity scientists argue the credibility of their research, and five “ontological views,” that is the ways in which they interpret the material and formal causes of their study objects and models. Finally, the article proposes the term of “regime of evidence” to designate the specific combination of one ontological view with one or more epistemic argumentative regimes. (shrink)

Computer models and simulations have become, since the 1960s, an essential instrument for scientific inquiry and political decision making in several fields, from climate to life and social sciences. Philosophical reflection has mainly focused on the ontological status of the computational modeling, on its epistemological validity and on the research practices it entails. But in computational sciences, the work on models and simulations are only two steps of a longer and richer process where operations on data are as important as, (...) and even more time and energy-consuming than modeling itself. Drawing on two study cases—computational embryology and computational epidemiology—this article contributes to filling the gap by focusing on the operations of producing and re-using data in computational sciences. The different phases of the scientific and artisanal work of modelers include data collection, aggregation, homogenization, assemblage, analysis and visualization. The article deconstructs the ideas that data are self-evident informational aggregates and that data-driven approaches are exempted from theoretical work. More importantly, the paper stresses the fact that data are constructed and theory laden not only in their fabrication, but also in their reusing. (shrink)

I propose that a sociological and historical examination of nanotechnologists can contribute more to an understanding of nanotechnology than an ontological definition. Nanotechnology emerged from the convergent evolution of numerous "technical knowledge communities"-networks of tightly-interconnected people who operate between disciplines and individual research groups. I demonstrate this proposition by sketching the co-evolution of computational chemistry and computational nanotechnology. Computational chemistry arose in the 1950s but eventually segregated into an ab initio, basic research, physics-oriented flavor and an industry-oriented, molecular modeling and (...) visualization, biochemical flavor. Computational nanotechnology arose in the 1990s as a synthesis of these two subgroups, infused by people and practices from computational materials science, engineering, computer science, and elsewhere. I show that to understand the aims and outcomes of computational nanotechnology-and nanotechnology more generally-we need to understand relationships between different, but related, nano knowledge communities and their dependence on particular practices, artifacts, and theories. (shrink)

It is often said that computer simulations generate new knowledge about the empirical world in the same way experiments do. My aim is to make sense of such a claim. I first show that the similarities between computer simulations and experiments do not allow them to generate new knowledge but invite the simulationist to interact with simulations in an experimental manner. I contend that, nevertheless, computer simulations and experiments yield new knowledge under the same epistemic circumstances, independently of any features (...) they may share. (shrink)

The following article treats the 'applicational turn' of modern fluid dynamics as it set in at the beginning of the 20th century with Ludwig Prandtl's concept of the boundary layer. It seeks to show that there is much more to applying a theory in a highly mathematical field like fluid dynamics than deriving a special case from a general explanatory theory under particular antecedent conditions. In Prandtl's case, the decisive move was to introduce a model that provided a physical/causal conception (...) of viscous flow at high Reynolds numbers. It facilitated an approximate solution to the Navier-Stokes equations, which in turn gave rise to many special applications. After a detailed account of Prandtl's achievement, the article discusses the role of the physical model and its experimental and mathematical significance. It is shown that the mathematical simplification provided by the physical model greatly expanded the explanatory capacity of the theory which the Navier-Stokes equations alone could not provide. (shrink)

The translation of a mathematical model into a numerical one employs various modifications in order to make the model accessible for computation. Such modifications include discretizations, approximations, heuristic assumptions, and other methods. The paper investigates the divergent styles of mathematical and numerical models in the case of a specific piece of code in a current atmospheric model. Cognizance of these modifications means that the question of the role and function of scientific models has to be reworked. Neither are numerical models (...) pure intermediaries between theory and data, nor are they autonomous tools of inquiry. Instead, theory and data are transformed into a new symbolic form of research due to the fact that computation has become an essential requirement for every scientific practice. Therefore the question is posed: What do numerical (climate) models really represent? (shrink)

Computer simulations are an exciting tool that plays important roles in many scientific disciplines. This has attracted the attention of a number of philosophers of science. The main tenor in this literature is that computer simulations not only constitute interesting and powerful new science, but that they also raise a host of new philosophical issues. The protagonists in this debate claim no less than that simulations call into question our philosophical understanding of scientific ontology, the epistemology and semantics of models (...) and theories, and the relation between experimentation and theorising, and submit that simulations demand a fundamentally new philosophy of science in many respects. The aim of this paper is to critically evaluate these claims. Our conclusion will be sober. We argue that these claims are overblown and that simulations, far from demanding a new metaphysics, epistemology, semantics and methodology, raise few if any new philosophical problems. The philosophical problems that do come up in connection with simulations are not specific to simulations and most of them are variants of problems that have been discussed in other contexts before. (shrink)

A chronicled approach to the notion of computer simulations shows that there are two predominant interpretations in the specialized literature. According to the first interpretation, computer simulations are techniques for finding the set of solutions to a mathematical model. I call this first interpretation the problem-solving technique viewpoint. In its second interpretation, computer simulations are considered to describe patterns of behavior of a target system. I call this second interpretation the description of patterns of behavior viewpoint of computer simulations. This (...) article explores these two interpretations of computer simulations from three different angles. First, I collect a series of definitions of computer simulation from the historical record. I track back definitions to the early 1960s and show how each viewpoint shares similar interpretations of computer simulations—ultimately clustering into the two viewpoints aforementioned. This reconstruction also includes the most recent literature. Second, I unpack the philosophical assumptions behind each viewpoint, with a special emphasis on their differences. Third, I discuss the philosophical implications of each viewpoint in the context of the recent discussion on the logic of scientific explanation for computer simulations. (shrink)

In the 1940s and 1950s, British and American journals published a flood of papers by doctors, pathologists, geneticists and anthropologists debating the virtues of two competing nomenclatures used to denote the Rhesus blood groups. Accounts of this prolonged and often bitter episode have tended to focus on the main protagonists' personalities and theoretical commitments. Here I take a different approach and use the literature generated by the dispute to recover the practical and epistemic functions of nomenclatures in genetics. Drawing on (...) recent work that views inscriptions as part of the material culture of science, I use the Rhesus controversy to think about the ways in which geneticists visualized and negotiated their objects of research, and how they communicated and collaborated with workers in other settings. Extending recent studies of relations between different media, I consider the material forms of nomenclatures, as they were jotted in notebooks, printed in journals, scribbled on blackboards and spoken out loud. The competing Rhesus nomenclatures had different virtues as they were expressed in different media and made to embody commitments to laboratory practices. In exploring the varied practical and epistemic qualities of nomenclatures I also suggest a new understanding of the Rhesus controversy itself. (shrink)

In this paper, I would like to show that considering technological models as they arise in engineering disciplines can greatly enrich the philosophical perspective on models. In fluid mechanics, (at least) three types of models are distinguished: mathematical, computer and physical models. Very often, the choice of a particular mathematical, computer or physical model highly affects the type of solutions and the computational time needed for it. Technological models not only aim at a correct description of the physical phenomena, but (...) also for an efficient and accurate simulation. The problem arises how heterogeneous models of an engineering problem can be brought together and be compared to each other as regards their function and technological efficiency. There are two developments in the history of fluid mechanics that have greatly influenced the use of models in the field: The introduction of the concept of the boundary layer by Ludwig Prandtl in 1904 made it possible to apply ideal analytical solutions, which at the time were almost entirely based on Euler’s equations for inviscid fluids, to interesting real cases and to approximate the theoretical Navier-Stokes equations to practical engineering problems, i.e. to cases at high Reynolds numbers. This made it possible to link the empirical tradition of hydraulics with the theoretical tradition of analytical mechanics and therefore lead to a kind of equilibrium in the use of mathematical and physical models. In the 1970s the introduction of the computer has greatly pushed back the importance of both physical and mathematical (analytic) models alike without making them superfluous. There remain, however, three different ways to conceive of physical models in fluid mechanics, and thus of the experimental ingredient, depending on whether they are devised from an analytical, computational or measurement theoretical point of view. Yet even inside the tradition of computer simulation, different practices have formed according to the programming methods used. The choice of method either depends on considerations of efficiency in terms of costs and time, or on historically contingent factors, like availability of instruments and programming packages or the arbitrary choice of a forerunner. Seen from a technological point of view the factors that make models “autonomous agents” and thus (relatively) independent from theory depend on efficiency constraints. Models are means to solve problems in a certain practical perspective by the most efficient means available. To develop a model is a “fast and frugal way” to get to grips with a certain region of reality, whereas the theoretical approach stresses the importance of universal features. (shrink)