Search results for 'model-building' (try it on Scholar)

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  1. Jay Odenbaugh (2006). The Strategy of “the Strategy of Model Building in Population Biology”. Biology and Philosophy 21 (5):607-621.
    In this essay, I argue for four related claims. First, Richard Levins’ classic “The Strategy of Model Building in Population Biology” was a statement and defense of theoretical population biology growing out of collaborations between Robert MacArthur, Richard Lewontin, E. O. Wilson, and others. Second, I argue that the essay served as a response to the rise of systems ecology especially as pioneered by Kenneth Watt. Third, the arguments offered by Levins against systems ecology and in favor of his own (...)
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  2. Michael Weisberg (2006). Forty Years of 'the Strategy': Levins on Model Building and Idealization. [REVIEW] Biology and Philosophy 21 (5):623-645.
    This paper is an interpretation and defense of Richard Levins’ “The Strategy of Model Building in Population Biology,” which has been extremely influential among biologists since its publication 40 years ago. In this article, Levins confronted some of the deepest philosophical issues surrounding modeling and theory construction. By way of interpretation, I discuss each of Levins’ major philosophical themes: the problem of complexity, the brute-force approach, the existence and consequence of tradeoffs, and robustness analysis. I argue that Levins’ article is (...)
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  3.  75
    Axel Gelfert (2013). Strategies of Model-Building in Condensed Matter Physics: Trade-Offs as a Demarcation Criterion Between Physics and Biology? Synthese 190 (2):253-272.
    This paper contrasts and compares strategies of model-building in condensed matter physics and biology, with respect to their alleged unequal susceptibility to trade-offs between different theoretical desiderata. It challenges the view, often expressed in the philosophical literature on trade-offs in population biology, that the existence of systematic trade-offs is a feature that is specific to biological models, since unlike physics, biology studies evolved systems that exhibit considerable natural variability. By contrast, I argue that the development of ever more sophisticated (...)
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  4. Jay Odenbaugh (2003). Complex Systems, Trade‐Offs, and Theoretical Population Biology: Richard Levin's “Strategy of Model Building in Population Biology” Revisited. Philosophy of Science 70 (5):1496-1507.
    Ecologist Richard Levins argues population biologists must trade‐off the generality, realism, and precision of their models since biological systems are complex and our limitations are severe. Steven Orzack and Elliott Sober argue that there are cases where these model properties cannot be varied independently of one another. If this is correct, then Levins's thesis that there is a necessary trade‐off between generality, precision, and realism in mathematical models in biology is false. I argue that Orzack and Sober's arguments fail since (...)
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  5. Jay Odenbaugh (2006). Message in the Bottle: The Constraints of Experimentation on Model Building. Philosophy of Science 73 (5):720-729.
    (Total word count 2,647) I. Introduction. Given the work of Robert MacArthur and his followers, some skeptical ecologists charge that theoretical modeling building has gone evidentially unconstrained. That is, models are often constructed which resist empirical testing. In this essay, I argue that “bottle experiments” do provide model building with important evidential constraints using an example of chaos producing models that have been tested against the dynamics of flour beetle populations. Critics reply however that this and other bottle experiments are (...)
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  6. Jay Odenbaugh (2003). Complex Systems, Trade-Offs, and Theoretical Population Biology: Richard Levin's "Strategy of Model Building in Population Biology" Revisited. Philosophy of Science 70 (5):1496-1507.
    Ecologist Richard Levins (1966, 1968) argues population biologists must trade-off the generality, realism and precision of their models since biological systems are complex and our limitations are severe. Elliott Sober and Steven Orzack (1993) argue that there are cases where these model properties cannot be varied independently of one another. If this is correct, then Levins` thesis that there is a necessary trade-off between generality, precision, and realism in mathematical models in biology is false. I argue that Sober and Orzack`s (...)
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  7. Jay Odenbaugh (2006). Message in the Bottle: The Constraints of Experimentation on Model Building. Philosophy of Science 73 (5):720-729.
    Some ecologists have argued that theoretical model building in population and community ecology has gone evidentially unconstrained. In the essay, I argue that "bottle experiments" offer ecological model building evidential constraints and illustrate this by considering work on chaotic models tested by the dynamics of flour beetles. Critics reply that these experiments are importantly unlike nonmanipulated natural systems and thus do not constitute genuine tests of the models. I conclude by considering two responses to this worry and a suggestion on (...)
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  8.  47
    Anya Plutynski (2006). Strategies of Model Building in Population Genetics. Philosophy of Science 73 (5):755-764.
    In 1966, Richard Levins argued that there are different strategies in model building in population biology. In this paper, I reply to Orzack and Sober's (1993) critiques of Levins and argue that his views on modeling strategies apply also in the context of evolutionary genetics. In particular, I argue that there are different ways in which models are used to ask and answer questions about the dynamics of evolutionary change, prospectively and retrospectively, in classical versus molecular evolutionary genetics. Further, I (...)
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  9.  50
    John Matthewson (2011). Trade-Offs in Model-Building: A More Target-Oriented Approach. Studies in History and Philosophy of Science 42 (2):324-333.
    In his 1966 paper "The Strategy of model-building in Population Biology", Richard Levins argues that no single model in population biology can be maximally realistic, precise and general at the same time. This is because these desirable model properties trade-off against one another. Recently, philosophers have developed Levins' claims, arguing that trade-offs between these desiderata are generated by practical limitations on scientists, or due to formal aspects of models and how they represent the world. However this project is not (...)
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  10.  48
    Justin Bledin & Sharon Shewmake (2004). Research Programs, Model-Building and Actor-Network-Theory: Reassessing the Case of the Leontief Paradox. Journal of Economic Methodology 11 (4):455-476.
    Methodology of scientific research programs (MSRP), model-building and actor-network-theory (ANT) are woven together to provide a layered study of the Leontief paradox. Neil De Marchi's Lakatosian account examined the paradox within an Ohlin-Samuelson research program. A model-building approach rather highlights the ability of Leontief's input-output model to mediate between international trade theory and the world by facilitating an empirical application of the Heckscher-Ohlin Theorem. The epistemological implications of this model-building approach provide an alternative explanation of why Samuelson (...)
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  11.  25
    Anya Plutynski (2006). Strategies of Model Building in Population Genetics. Philosophy of Science 73 (5):755-764.
    In 1966, Richard Levins argued that there are different strategies in model building in population biology. In this paper, I reply to Orzack and Sober’s (1993) critiques of Levins, and argue that his views on modeling strategies apply also in the context of evolutionary genetics. In particular, I argue that there are different ways in which models are used to ask and answer questions about the dynamics of evolutionary change, prospectively and retrospectively, in classical versus molecular evolutionary genetics. Further, I (...)
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  12.  5
    C. Ulrich, E. S. Tillema, A. J. Hackenberg & A. Norton (2014). Constructivist Model Building: Empirical Examples From Mathematics Education. Constructivist Foundations 9 (3):328-339.
    Context: This paper outlines how radical constructivist theory has led to a particular methodological technique, developing second-order models of student thinking, that has helped mathematics educators to be more effective teachers of their students. Problem: The paper addresses the problem of how radical constructivist theory has been used to explain and engender more viable adaptations to the complexities of teaching and learning. Method: The paper presents empirical data from teaching experiments that illustrate the process of second-order model building. Results: The (...)
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  13.  2
    Jeremy Pitt & Jim Cunningham (1996). Theorem Proving and Model Building with the Calculus KE. Logic Journal of the IGPL 4 (1):129-150.
    A Prolog implementation of a new theorem-prover for first-order classical logic is described. The prover is based on the calculus KE and the rules used for analysing quantifiers in free variable semantic tableaux. A formal specification of the rules used in the implementation is described, for which soundness and completeness is straightforwardly verified. The prover has been tested on the first 47 problems of the Pelletier set, and its performance compared with a state of the art semantic tableaux theorem-prover. It (...)
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  14.  2
    P. J. Sánchez Gómez (2014). Methodological Issues of Second-Order Model Building. Constructivist Foundations 9 (3):344-346.
    Open peer commentary on the article “Constructivist Model Building: Empirical Examples From Mathematics Education” by Catherine Ulrich, Erik S. Tillema, Amy J. Hackenberg & Anderson Norton. Upshot: I argue that radical constructivism poses a series of deep methodological constraints on educational research. We focus on the work of Ulrich et al. to illustrate the practical implications of these constraints.
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  15.  1
    H. L. Johnson (2014). A Role of Context in Constructivist Model Building: What Problem is the Learner Solving? Constructivist Foundations 9 (3):339-341.
    Open peer commentary on the article “Constructivist Model Building: Empirical Examples From Mathematics Education” by Catherine Ulrich, Erik S. Tillema, Amy J. Hackenberg & Anderson Norton. Upshot: I concur with Ulrich et al. that second-order models can be powerful tools for investigating students’ mathematical learning. I argue for a role that a dynamic, learner-centered perspective on context could play in constructivist model building.
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  16.  2
    S. E. Kastberg (2014). The Power of What We Know: Further Directions for Exploring Constructivist Model Building. Constructivist Foundations 9 (3):352-354.
    Open peer commentary on the article “Constructivist Model Building: Empirical Examples From Mathematics Education” by Catherine Ulrich, Erik S. Tillema, Amy J. Hackenberg & Anderson Norton. Upshot: The suggestion that model building can be used by teachers to enable productive interactions with students is taken up. Challenges and possibilities in exploring constructivist model building by examining mathematics teacher educators’ interactions with teachers are investigated.
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  17.  1
    J. Proulx (2014). From Model Building to the Observer. Constructivist Foundations 9 (3):341-344.
    Open peer commentary on the article “Constructivist Model Building: Empirical Examples From Mathematics Education” by Catherine Ulrich, Erik S. Tillema, Amy J. Hackenberg & Anderson Norton. Upshot: The target article by Ulrich et al. is a good example of constructivist research in mathematics education, and illustrates how constructivism can ground a research endeavour toward modelling students’ mathematical understandings. I propose to delve into these issues of model building and reflect on Maturana’s notion of the observer. I do this through discussing (...)
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  18. J. Falter (1987). Pitfalls in Scientific Model Building: Unemployment, the Unemployed, and the Nazi Vote 1930-1933. In Herman O. A. Wold (ed.), Theoretical Empiricism: A General Rationale for Scientific Model-Building. Paragon House 183--200.
     
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  19. John Matthewson & Michael Weisberg (2009). The Structure of Tradeoffs in Model Building. Synthese 170 (1):169 - 190.
    Despite their best efforts, scientists may be unable to construct models that simultaneously exemplify every theoretical virtue. One explanation for this is the existence of tradeoffs: relationships of attenuation that constrain the extent to which models can have such desirable qualities. In this paper, we characterize three types of tradeoffs theorists may confront. These characterizations are then used to examine the relationships between parameter precision and two types of generality. We show that several of these relationships exhibit tradeoffs and discuss (...)
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  20.  56
    Michael Weisberg (2009). The Structure of Tradeoffs in Model Building. Synthese 170 (1):169 - 190.
    Despite their best efforts, scientists may be unable to construct models that simultaneously exemplify every theoretical virtue. One explanation for this is the existence of tradeoffs: relationships of attenuation that constrain the extent to which models can have such desirable qualities. In this paper, we characterize three types of tradeoffs theorists may confront. These characterizations are then used to examine the relationships between parameter precision and two types of generality. We show that several of these relationships exhibit tradeoffs and discuss (...)
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  21.  48
    Mary S. Morgan (2004). Imagination and Imaging in Model Building. Philosophy of Science 71 (5):753-766.
    Modelling became one of the primary tools of mathematical economic research in the twentieth century, but when we look at examples of how nonanalogical models were first built in economics, both the process of making representations and aspects of the representing relation remain opaque. Like early astronomers, economists have to imagine how the hidden parts of their world are arranged and to make images, that is, create models, to represent how they work. The case of the Edgeworth Box, a model (...)
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  22.  15
    Mary Morgan, Imagination and Imaging in Economic Model Building.
    Modelling became one of the primary tools of economic research in the 20th century and economists understand their mathematical models as giving some kind of representation of the economic world, one adequate enough for the purpose of reasoning about that world. But when we look at examples of how non-analogical models were first built in economics, both the process of making representations and aspects of the representing relation remain opaque. Like early astronomers, economists have to imagine how the hidden parts (...)
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  23.  21
    Xavier de Donato Rodríguez & Jesús Zamora Bonilla (2009). Credibility, Idealisation, and Model Building: An Inferential Approach. Erkenntnis 70 (1):101-118.
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  24.  33
    John Beatty (1980). Optimal-Design Models and the Strategy of Model Building in Evolutionary Biology. Philosophy of Science 47 (4):532-561.
    The prevalence of optimality models in the literature of evolutionary biology is testimony to their popularity and importance. Evolutionary biologist R. C. Lewontin, whose criticisms of optimality models are considered here, reflects that "optimality arguments have become extremely popular in the last fifteen years, and at present represent the dominant mode of thought." Although optimality models have received little attention in the philosophical literature, these models are very interesting from a philosophical point of view. As will be argued, optimality models (...)
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  25. Jay Odenbaugh (2001). Ecological Stability, Model Building, and Environmental Policy: A Reply to Some of the Pessimism. Philosophy of Science 68 (S1):S493-.
    Recently, there has been a rise in pessimism concerning what theoretical ecology can offer conservation biologists in the formation of reasonable environmental policies. In this paper, I look at one of the pessimistic arguments offered by Kristin Shrader-Frechette and E. D. McCoy (1993, 1994)--the argument from conceptual imprecision. I suggest that their argument rests on an inadequate account of the concepts of ecological stability and that there has been conceptual progress with respect to complexity-stability hypotheses. Such progress, I maintain, can (...)
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  26.  10
    Steven Hecht Orzack (2005). Discussion: What, If Anything, Is "The Strategy of Model Building in Population Biology?" A Comment on Levins (1966) and Odenbaugh (2003). [REVIEW] Philosophy of Science 72 (3):479-485.
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  27. Yoshihiko Hasegawa & Hitoshi Iba (2007). Probabilistic Model Building Genetic Programming Based on Estimation of Bayesian Network. Transactions of the Japanese Society for Artificial Intelligence 22:37-47.
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  28.  2
    Xavier de Donato Rodríguez & Jesús Zamora Bonilla (2009). Credibility, Idealisation, and Model Building: An Inferential Approach. Erkenntnis 70 (1):101-118.
    In this article we defend the inferential view of scientific models and idealisation. Models are seen as "inferential prostheses" construed by means of an idealisation-concretisation process, which we essentially understand as a kind of counterfactual deformation procedure . The value of scientific representation is understood in terms not only of the success of the inferential outcomes arrived at with its help, but also of the heuristic power of representation and their capacity to correct and improve our models. This provides us (...)
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  29.  19
    Peter Taylor (2000). Socio-Ecological Webs and Sites of Sociality:Levins' Strategy of Model Building Revisited. [REVIEW] Biology and Philosophy 15 (2):197-210.
    This essay extends Levins'' 1966 analysis of modelbuilding in ecology and evolutionary biology. Amodel, as the product of modeling, might bevalued according to its correspondence to reality. Yet Levins'' emphasis on provisionality and changeredirects attention to the processes ofmodeling, through which scientists select and generatetheir problems, define their categories, collect theirdata, compare competing models, and present theirfindings. I identify several points where decisionsare required that are not determined by nature. Thisinvites examination of the social considerationsmodelers are reacting to at the (...)
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  30.  7
    Thomas J. Fararo (2011). 5 Generative Process Model Building. In Pierre Demeulenaere (ed.), Analytical Sociology and Social Mechanisms. Cambridge University Press 99.
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  31.  37
    Xavier Donato Rodríguedez & Jesús Zamora Bonilla (2009). Credibility, Idealisation, and Model Building: An Inferential Approach. Erkenntnis 70 (1).
    In this article we defend the inferential view of scientific models and idealisation. Models are seen as “inferential prostheses” (instruments for surrogative reasoning) construed by means of an idealisation-concretisation process, which we essentially understand as a kind of counterfactual deformation procedure (also analysed in inferential terms). The value of scientific representation is understood in terms not only of the success of the inferential outcomes arrived at with its help, but also of the heuristic power of representation and their capacity to (...)
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  32.  9
    Leon J. Goldstein (1977). Causal Explanation and Model Building in History, Economics, and the New Economic History. International Studies in Philosophy 9:201-203.
  33. Shigeyoshi Tsutsui (2003). Probabilistic Model Building Based GAs in Permutation Domains Using Edge Histograms. Transactions of the Japanese Society for Artificial Intelligence 18:173-182.
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  34.  6
    Tim French, John McCabe-Dansted & Mark Reynolds (2013). Indiscrete Models: Model Building and Model Checking Over Linear Time. In Kamal Lodaya (ed.), Logic and its Applications. Springer 50--68.
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  35.  3
    Inge Hinterwaldner (2015). Model Building with Wind and Water: Friedrich Ahlborn's Photo-Optical Flow Analysis. Studies in History and Philosophy of Science Part A 49:1-17.
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  36.  9
    Thomas J. Dohmen (2002). Building and Using Economic Models: A Case Study Analysis of the IS-LL Model. Journal of Economic Methodology 9 (2):191-212.
    This paper critically assesses several model accounts written in the 1990s by epistemologists and philosophers of science by relating them to a specific but crucial example of model building, namely Hicks's (1937) construction of the first version of the IS-LM model, and examining in how far these accounts apply to this case. Thereby the paper contributes to answering why and how economists build models. The view crystallizes that economists build models not only to facilitate the conceptual exploration of theory, but (...)
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  37.  14
    Xavier de Donato Rodríguez & Jesús Zamora Bonilla (2009). Credibility, Idealisation, and Model Building: An Inferential Approach. [REVIEW] Erkenntnis 70 (1):101 - 118.
    In this article we defend the inferential view of scientific models and idealisation. Models are seen as "inferential prostheses" (instruments for surrogative reasoning) construed by means of an idealisation-concretisation process, which we essentially understand as a kind of counterfactual deformation procedure (also analysed in inferential terms). The value of scientific representation is understood in terms not only of the success of the inferential outcomes arrived at with its help, but also of the heuristic power of representation and their capacity to (...)
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  38.  12
    William B. Griffith (1991). The Methodology of Economic Model Building: Methodology After Samuelson, Lawrence A. Boland. London: Routledge, 1989, V + 194 Pages. [REVIEW] Economics and Philosophy 7 (1):119-122.
  39.  3
    Paul van Geert (1997). Que Será, Será: Determinism and Nonlinear Dynamic Model Building in Development. In Alan Fogel, Maria C. D. P. Lyra & Jaan Valsiner (eds.), Dynamics and Indeterminism in Developmental and Social Processes. L. Erlbaum
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  40.  1
    Harold Goodglass (1979). Is Model Building Advancing Neurolinguistics? Behavioral and Brain Sciences 2 (3):466.
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  41. Simona Amenta & Davide Crepaldi (2016). Editorial: The Variable Mind? How Apparently Inconsistent Effects Might Inform Model Building. Frontiers in Psychology 7.
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  42. David Braybrooke (1977). PETER D. MCCLELLAND, "Causal Explanation and Model Building in History, Economics, and the New Economic History". [REVIEW] History and Theory 16 (3):337.
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  43. V. Dobson & D. Rose (1985). Application of an Explicit Procedure for Model Building in the Visual Cortex. In David Rose & Vernon Dobson (eds.), Models of the Visual Cortex. New York: John Wiley & Sons 546--560.
     
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  44. Xavier De Donato Rodriguez & Jesus Zamora Bonilla (2009). Credibility, Idealisation, and Model Building: An Inferential Approach. Erkenntnis 70 (1):101-118.
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  45. Nader Dutta, Bhaskar Deo, Yangjun Liu, Krishna Ramani, Jerry Kapoor & Denes Vigh (2015). Pore-Pressure-Constrained, Rock-Physics-Guided Velocity Model Building Method: Alternate Solution to Mitigate Subsalt Geohazard. Interpretation 3 (1):SE1-SE11.
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  46. William B. Griffith (1991). Lawrence A. Boland's "The Methodology of Economic Model Building: Methodology After Samuelson". [REVIEW] Economics and Philosophy 7:119.
     
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  47. Jacques P. Leveille, Dave McCann, David Bartel, Scott Morton, Jerry Young, Brian Horn, Rob Wervelman, Sverre Brandsberg-Dahl, Adriana Ramirez, Gabriel Ritter, Paul Williamson, Mark Rhodes & Bill Hart (2014). Introduction to Special Section: Salt Basin Model Building, Imaging, and Interpretation. Interpretation 2 (4):SLi-SLi.
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  48. George F. Luger (1981). Mathematical Model Building in the Solution of Mechanics Problems: Human Protocols and the MECHO Trace. Cognitive Science 5 (1):55-77.
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  49. John Matthewson (2011). Trade-Offs in Model-Building: A More Target-Oriented Approach. Studies in History and Philosophy of Science Part A 42 (2):324-333.
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  50. Michael Ruse (1974). Ralph M. Stogdill , "The Process of Model-Building in the Behavioral Sciences". Theory and Decision 4 (3/4):401.
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