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  1. Simulation and Similarity: Using Models to Understand the World.Michael Weisberg - 2013 - Oxford University Press.
    one takes to be the most salient, any pair could be judged more similar to each other than to the third. Goodman uses this second problem to showthat there can be no context-free similarity metric, either in the trivial case or in a scientifically ...
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  • The Evolution of Cooperation.Robert M. Axelrod - 1984 - Basic Books.
    The 'Evolution of Cooperation' addresses a simple yet age-old question; If living things evolve through competition, how can cooperation ever emerge? Despite the abundant evidence of cooperation all around us, there existed no purely naturalistic answer to this question until 1979, when Robert Axelrod famously ran a computer tournament featuring a standard game-theory exercise called The Prisoner's Dilemma. To everyone's surprise, the program that won the tournament, named Tit for Tat, was not only the simplest but the most "cooperative" entrant. (...)
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  • Imagination Extended and Embedded: Artifactual Versus Fictional Accounts of Models.Tarja Knuuttila - 2017 - Synthese 198 (Suppl 21):5077-5097.
    This paper presents an artifactual approach to models that also addresses their fictional features. It discusses first the imaginary accounts of models and fiction that set model descriptions apart from imagined-objects, concentrating on the latter :251–268, 2010; Frigg and Nguyen in The Monist 99:225–242, 2016; Godfrey-Smith in Biol Philos 21:725–740, 2006; Philos Stud 143:101–116, 2009). While the imaginary approaches accommodate surrogative reasoning as an important characteristic of scientific modeling, they simultaneously raise difficult questions concerning how the imagined entities are related (...)
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  • Are Computer Simulations Experiments? And If Not, How Are They Related to Each Other?Claus Beisbart - 2018 - European Journal for Philosophy of Science 8 (2):171-204.
    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, (...)
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  • Science in the Age of Computer Simulation.Eric Winsberg - 2010 - University of Chicago Press.
    Introduction -- Sanctioning models : theories and their scope -- Methodology for a virtual world -- A tale of two methods -- When theories shake hands -- Models of climate : values and uncertainties -- Reliability without truth -- Conclusion.
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  • Causal Mechanisms in the Social Sciences.Peter Hedström & Petri Ylikoski - 2010 - Annual Review of Sociology 36:49–67.
    During the past decade, social mechanisms and mechanism-based ex- planations have received considerable attention in the social sciences as well as in the philosophy of science. This article critically reviews the most important philosophical and social science contributions to the mechanism approach. The first part discusses the idea of mechanism- based explanation from the point of view of philosophy of science and relates it to causation and to the covering-law account of explanation. The second part focuses on how the idea (...)
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  • Computer Simulations in Science.Eric Winsberg - forthcoming - Stanford Encyclopedia of Philosophy.
  • Philosophy and Climate Science.Eric Winsberg - 2018 - Cambridge: Cambridge University Press.
    There continues to be a vigorous public debate in our society about the status of climate science. Much of the skepticism voiced in this debate suffers from a lack of understanding of how the science works - in particular the complex interdisciplinary scientific modeling activities such as those which are at the heart of climate science. In this book Eric Winsberg shows clearly and accessibly how philosophy of science can contribute to our understanding of climate science, and how it can (...)
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  • Reconstructing Reality: Models, Mathematics, and Simulations.Margaret Morrison - 2015 - Oup Usa.
    The book examines issues related to the way modeling and simulation enable us to reconstruct aspects of the world we are investigating. It also investigates the processes by which we extract concrete knowledge from those reconstructions and how that knowledge is legitimated.
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  • Foundations of Social Theory.James S. Coleman - 1990 - Belknap Press.
    Combining principles of individual rational choice with a sociological conception of collective action, James Coleman recasts social theory in a bold new way. The result is a landmark in sociological theory, capable of describing both stability and change in social systems. This book provides for the first time a sound theoretical foundation for linking the behavior of individuals to organizational behavior and then to society as a whole. The power of the theory is especially apparent when Coleman analyzes corporate actors, (...)
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  • Philosophy and Computer Science.Timothy Colburn - 2000 - Routledge.
    Colburn has a doctorate in philosophy and an advanced degree in computer science; he's worked as a philosophy professor, a computer programmer, and a research scientist in artificial intelligence. Here he discusses the philosophical foundations of artificial intelligence; the new encounter of science and philosophy ; and the philosophy of computer science.
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  • Extending Ourselves: Computational Science, Empiricism, and Scientific Method.Paul Humphreys - 2004 - Oxford University Press.
    Computational methods have become the dominant technique in many areas of science. This book contains the first systematic philosophical account of these new methods and their consequences for scientific method. This book will be of interest to philosophers of science and to anyone interested in the role played by computers in modern science.
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  • Validation and Verification in Social Simulation: Patterns and Clarification of Terminology.Nuno David - 2009 - Epistemological Aspects of Computer Simulation in the Social Sciences, EPOS 2006, Revised Selected and Invited Papers, Lecture Notes in Artificial Intelligence, Squazzoni, Flaminio (Ed.) 5466:117-129.
    The terms ‘verification’ and ‘validation’ are widely used in science, both in the natural and the social sciences. They are extensively used in simulation, often associated with the need to evaluate models in different stages of the simulation development process. Frequently, terminological ambiguities arise when researchers conflate, along the simulation development process, the technical meanings of both terms with other meanings found in the philosophy of science and the social sciences. This article considers the problem of verification and validation in (...)
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  • Disagreement in Discipline-Building Processes.David Anzola - 2019 - Synthese 198 (Suppl 25):6201-6224.
    Successful instances of interdisciplinary collaboration can eventually enter a process of disciplinarisation. This article analyses one of those instances: agent-based computational social science, an emerging disciplinary field articulated around the use of computational models to study social phenomena. The discussion centres on how, in knowledge transfer dynamics from traditional disciplinary areas, practitioners parsed several epistemic resources to produce new foundational disciplinary shared commitments, and how disagreements operated as a mechanism of differentiation in their production. Two parsing processes are examined to (...)
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  • Knowledge Transfer in Agent-Based Computational Social Science.David Anzola - 2019 - Studies in History and Philosophy of Science Part A 77:29-38.
  • Advancing the Art of Simulation in the Social Sciences.Robert Axelrod - 1997 - Complexity 3 (2):16-22.
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  • Agent‐Based Computational Models and Generative Social Science.Joshua M. Epstein - 1999 - Complexity 4 (5):41-60.
  • Experiments, Simulations, and Epistemic Privilege.Emily C. Parke - 2014 - Philosophy of Science 81 (4):516-536.
    Experiments are commonly thought to have epistemic privilege over simulations. Two ideas underpin this belief: first, experiments generate greater inferential power than simulations, and second, simulations cannot surprise us the way experiments can. In this article I argue that neither of these claims is true of experiments versus simulations in general. We should give up the common practice of resting in-principle judgments about the epistemic value of cases of scientific inquiry on whether we classify those cases as experiments or simulations, (...)
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  • Appraising Models Nonrepresentationally.Till Grüne-Yanoff - 2013 - Philosophy of Science 80 (5):850-861.
    Many scientific models lack an established representation relation to actual targets and instead refer to merely possible processes, background conditions, and results. This article shows how such models can be appraised. On the basis of the discussion of how-possibly explanations, five types of learning opportunities are distinguished. For each of these types, an example—from economics, biology, psychology, and sociology—is discussed. Contexts and purposes are identified in which the use of a model offers a genuine opportunity to learn. These learning opportunities (...)
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  • Computing the Perfect Model: Why Do Economists Shun Simulation?Aki Lehtinen & Jaakko Kuorikoski - 2007 - Philosophy of Science 74 (3):304-329.
    Like other mathematically intensive sciences, economics is becoming increasingly computerized. Despite the extent of the computation, however, there is very little true simulation. Simple computation is a form of theory articulation, whereas true simulation is analogous to an experimental procedure. Successful computation is faithful to an underlying mathematical model, whereas successful simulation directly mimics a process or a system. The computer is seen as a legitimate tool in economics only when traditional analytical solutions cannot be derived, i.e., only as a (...)
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  • Computer Simulation: The Cooperation Between Experimenting and Modeling.Johannes Lenhard - 2007 - Philosophy of Science 74 (2):176-194.
    The goal of the present article is to contribute to the epistemology and methodology of computer simulations. The central thesis is that the process of simulation modeling takes the form of an explorative cooperation between experimenting and modeling. This characteristic mode of modeling turns simulations into autonomous mediators in a specific way; namely, it makes it possible for the phenomena and the data to exert a direct influence on the model. The argumentation will be illustrated by a case study of (...)
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  • Scientific Representation, Interpretation, and Surrogative Reasoning.Gabriele Contessa - 2007 - Philosophy of Science 74 (1):48-68.
    In this paper, I develop Mauricio Suárez’s distinction between denotation, epistemic representation, and faithful epistemic representation. I then outline an interpretational account of epistemic representation, according to which a vehicle represents a target for a certain user if and only if the user adopts an interpretation of the vehicle in terms of the target, which would allow them to perform valid (but not necessarily sound) surrogative inferences from the model to the system. The main difference between the interpretational conception I (...)
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  • Verification, Validation, and Confirmation of Numerical Models in the Earth Sciences.Naomi Oreskes, Kristin Shrader-Frechette & Kenneth Belitz - 1994 - Science 263 (5147):641-646.
    Verification and validation of numerical models of natural systems is impossible. This is because natural systems are never closed and because model results are always nonunique. Models can be confirmed by the demonstration of agreement between observation and prediction, but confirmation is inherently partial. Complete confirmation is logically precluded by the fallacy of affirming the consequent and by incomplete access to natural phenomena. Models can only be evaluated in relative terms, and their predictive value is always open to question. The (...)
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  • What is a Computer Simulation? A Review of a Passionate Debate.Nicole Saam - 2017 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 48 (2):293-309.
    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.
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  • How Can Computer Simulations Produce New Knowledge?Claus Beisbart - 2012 - European Journal for Philosophy of Science 2 (3):395-434.
    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 (...)
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  • A New Kind of Science.Stephen Wolfram - 2002 - Bulletin of Symbolic Logic 10 (1):112-114.
     
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  • Does Matter Really Matter? Computer Simulations, Experiments, and Materiality.Wendy S. Parker - 2009 - Synthese 169 (3):483-496.
    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 (...)
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  • Elephant and Ant: The Social and Cognitive Organization of Computer Simulation.Johannes Lenhard - 2017 - Social Science Information 56 (3):375-392.
    The text comprises two main parts. The first part reflects on the status of simulation as scientific instrument and clarifies the relationship between modeling and instrumentation. The second part aims at investigating how simulation is organized. My main claim is that a turning point for simulation occurred around 1990 that signals a new quality of simulation in terms of its social and cognitive organization. Computational chemistry will serve as primary example. So-called density functional theory has not yet received much attention (...)
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  • The Philosophy of Simulation: Hot New Issues or Same Old Stew?Roman Frigg & Julian Reiss - 2009 - Synthese 169 (3):593-613.
    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 (...)
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  • Franklin, Holmes, and the Epistemology of Computer Simulation.Wendy S. Parker - 2008 - International Studies in the Philosophy of Science 22 (2):165 – 183.
    Allan Franklin has identified a number of strategies that scientists use to build confidence in experimental results. This paper shows that Franklin's strategies have direct analogues in the context of computer simulation and then suggests that one of his strategies—the so-called 'Sherlock Holmes' strategy—deserves a privileged place within the epistemologies of experiment and simulation. In particular, it is argued that while the successful application of even several of Franklin's other strategies (or their analogues in simulation) may not be sufficient for (...)
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  • Computer Simulations as Experiments.Anouk Barberousse, Sara Franceschelli & Cyrille Imbert - 2009 - Synthese 169 (3):557 - 574.
    Whereas computer simulations involve no direct physical interaction between the machine they are run on and the physical systems they are used to investigate, they are often used as experiments and yield data about these systems. It is commonly argued that they do so because they are implemented on physical machines. We claim that physicality is not necessary for their representational and predictive capacities and that the explanation of why computer simulations generate desired information about their target system is only (...)
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  • Grounds for Trust: Essential Epistemic Opacity and Computational Reliabilism.Juan M. Durán & Nico Formanek - 2018 - Minds and Machines 28 (4):645-666.
    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 (...)
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  • Computer Simulations and Experiments: The Case of the Higgs Boson.Michela Massimi & Wahid Bhimji - 2015 - Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics 51:71-81.
  • Modeling and Experimenting.Isabelle Peschard - 2009 - In Paul Humphreys & Cyrille Imbert (eds.), Models, Simulations, and Representations. Routledge.
    Experimental activity is traditionally identified with testing the empirical implications or numerical simulations of models against data. In critical reaction to the ‘tribunal view’ on experiments, this essay will show the constructive contribution of experimental activity to the processes of modeling and simulating. Based on the analysis of a case in fluid mechanics, it will focus specifically on two aspects. The first is the controversial specification of the conditions in which the data are to be obtained. The second is conceptual (...)
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  • What is a Simulation Model?Juan M. Durán - 2020 - Minds and Machines 30 (3):301-323.
    Many philosophical accounts of scientific models fail to distinguish between a simulation model and other forms of models. This failure is unfortunate because there are important differences pertaining to their methodology and epistemology that favor their philosophical understanding. The core claim presented here is that simulation models are rich and complex units of analysis in their own right, that they depart from known forms of scientific models in significant ways, and that a proper understanding of the type of model simulations (...)
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  • How Experiments End.P. Galison - 1990 - Synthese 82 (1):157-162.
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  • Agent-Based Modeling and the Fallacies of Individualism.Brian Epstein - 2011 - In Paul Humphreys & Cyrille Imbert (eds.), Models, Simulations, and Representations. Routledge. pp. 115444.
    Agent-​​based modeling is showing great promise in the social sciences. However, two misconceptions about the relation between social macroproperties and microproperties afflict agent-based models. These lead current models to systematically ignore factors relevant to the properties they intend to model, and to overlook a wide range of model designs. Correcting for these brings painful trade-​​offs, but has the potential to transform the utility of such models.
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  • Computer Simulation, Measurement, and Data Assimilation.Wendy S. Parker - 2017 - British Journal for the Philosophy of Science 68 (1):273-304.
    This article explores some of the roles of computer simulation in measurement. A model-based view of measurement is adopted and three types of measurement—direct, derived, and complex—are distinguished. It is argued that while computer simulations on their own are not measurement processes, in principle they can be embedded in direct, derived, and complex measurement practices in such a way that simulation results constitute measurement outcomes. Atmospheric data assimilation is then considered as a case study. This practice, which involves combining information (...)
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  • Models: Parables V Fables.Nancy Cartwright - 2010 - In Roman Frigg & Matthew Hunter (eds.), Beyond Mimesis and Convention: Representation in Art and Science. Springer.
    A good many models used in physics and economics offer descriptions of imaginary situations, using a combination of mathematics and natural language. The descriptions are both thin - not much about the situation is filled in - and unrealistic - what is filled in is not true of many real situations. Yet we want to use the results of these models to inform our conclusions about a range of actually occurring situations. I propose we interpret many of these models as (...)
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  • Models: Parables V Fables.Nancy Cartwright - 2008 - Insights 1 (11).
    A good many models used in physics and economics offer descriptions of imaginary situations, using a combination of mathematics and natural language. The descriptions are both thin - not much about the situation is filled in - and unrealistic - what is filled in is not true of many real situations. Yet we want to use the results of these models to inform our conclusions about a range of actually occurring situations. I propose we interpret many of these models as (...)
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  • Models as Make-Believe.Adam Toon - 2010 - In Roman Frigg & Matthew Hunter (eds.), Beyond Mimesis and Convention: Representation in Art and Science. Boston Studies in Philosophy of Science.
    In this paper I propose an account of representation for scientific models based on Kendall Walton’s ‘make-believe’ theory of representation in art. I first set out the problem of scientific representation and respond to a recent argument due to Craig Callender and Jonathan Cohen, which aims to show that the problem may be easily dismissed. I then introduce my account of models as props in games of make-believe and show how it offers a solution to the problem. Finally, I demonstrate (...)
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  • Uncertainty in Climate Science and Climate Policy.Jonathan Rougier & Michel Crucifix - 2018 - In Elisabeth A. Lloyd & Eric Winsberg (eds.), Climate Modelling: Philosophical and Conceptual Issues. Springer Verlag. pp. 361-380.
    In this chapter, we argue for and describe the gap that exists between current practice in mainstream academic climate science, and the practical needs of policymakers charged with exploring possible interventions in the context of climate change. By ‘mainstream academic climate science’ we mean the type of climate science that dominates in universities and research centres. We argue that academic climate science does not equip climate scientists to be as helpful as they might be, when involved in climate policy assessment. (...)
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  • Validation Benchmarks and Related Metrics.Nicole J. Saam - 2019 - In Claus Beisbart & Nicole J. Saam (eds.), Computer Simulation Validation - Fundamental Concepts, Methodological Frameworks, and Philosophical Perspectives. Springer. pp. 433-461.
    This chapter proposes benchmarking as an important, versatile and promising method in the process of validating simulation models with an empirical target. This excludes simulation models which only explore consequences of theoretical assumptions. A conceptual framework and descriptive theory of benchmarking in simulation validation is developed. Sources of benchmarks are outstanding experimental or observational dataObservational data, stylized facts or other characteristics of the target. They are outstanding because they are more effective, more reliable or more efficient than other such data, (...)
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  • Computer Simulations and the Trading Zone.Peter Galison - 1996 - In Peter Galison & David J. Stump (eds.), The Disunity of Science: Boundaries, Contexts, and Power. Stanford University Press. pp. 118--157.
     
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  • Why Computer Simulations Are Not Inferences, and in What Sense They Are Experiments.Florian J. Boge - 2018 - European Journal for Philosophy of Science 9 (1):1-30.
    The question of where, between theory and experiment, computer simulations locate on the methodological map is one of the central questions in the epistemology of simulation. The two extremes on the map have them either be a kind of experiment in their own right, 317–329, 2005; Morrison Philosophical Studies, 143, 33–57, 2009; Morrison 2015; Massimi and Bhimji Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics, 51, 71–81, 2015; Parker Synthese, 169, 483–496, (...)
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  • The World as a Process: Simulations in the Natural and Social Sciences.Stephan Hartmann - 1996 - In Rainer Hegselmann (ed.), Modelling and Simulation in the Social Sciences from the Philosophy of Science Point of View.
    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? (...)
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  • Credible Worlds, Capacities and Mechanisms.Robert Sugden - 2009 - Erkenntnis 70 (1):3-27.
    This paper asks how, in science in general and in economics in particular, theoretical models aid the understanding of real-world phenomena. Using specific models in economics and biology as test cases, it considers three alternative answers: that models are tools for isolating the ‘capacities’ of causal factors in the real world; that modelling is ‘conceptual exploration’ which ultimately contributes to the development of genuinely explanatory theories; and that models are credible counterfactual worlds from which inductive inferences can be made. The (...)
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  • Towards a Philosophy of Software Development: 40 Years After the Birth of Software Engineering.Mandy Northover, Derrick G. Kourie, Andrew Boake, Stefan Gruner & Alan Northover - 2008 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 39 (1):85-113.
    Over the past four decades, software engineering has emerged as a discipline in its own right, though it has roots both in computer science and in classical engineering. Its philosophical foundations and premises are not yet well understood. In recent times, members of the software engineering community have started to search for such foundations. In particular, the philosophies of Kuhn and Popper have been used by philosophically-minded software engineers in search of a deeper understanding of their discipline. It seems, however, (...)
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  • Replication, Replication and Replication: Some Hard Lessons From Model Alignment.Bruce Edmonds - unknown
    A published simulation model Riolo et al. 2001 ) was replicated in two independent implementations so that the results as well as the conceptual design align. This double replication allowed the original to be analysed and critiqued with confidence. In this case, the replication revealed some weaknesses in the original model, which otherwise might not have come to light. This shows that unreplicated simulation models and their results can not be trusted - as with other kinds of experiment, simulations need (...)
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  • Is Simulation an Epistemic Substitute for Experimentation?Isabelle Peschard - unknown
    It is sometimes said that simulation can serve as epistemic substitute for experimentation. Such a claim might be suggested by the fast-spreading use of computer simulation to investigate phenomena not accessible to experimentation. But what does that mean? The paper starts with a clarification of the terms of the issue and then focuses on two powerful arguments for the view that simulation and experimentation are ‘epistemically on a par’. One is based on the claim that, in experimentation, no less than (...)
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