Results for 'Modeling'

998 found
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  1. Michael Wooldridge.Modeling Distributed Artificial - 1996 - In N. Jennings & G. O'Hare (eds.), Foundations of Distributed Artificial Intelligence. Wiley. pp. 269.
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  2. Modeling future indeterminacy in possibility semantics.Fabrizio Cariani - manuscript
    Possibility semantics offers an elegant framework for a semantic analysis of modal logic that does not recruit fully determinate entities such as possible worlds. The present papers considers the application of possibility semantics to the modeling of the indeterminacy of the future. Interesting theoretical problems arise in connection to the addition of object-language determinacy operator. We argue that adding a two-dimensional layer to possibility semantics can help solve these problems. The resulting system assigns to the two-dimensional determinacy operator a (...)
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  3. Wayward Modeling: Population Genetics and Natural Selection.Bruce Glymour - 2006 - Philosophy of Science 73 (4):369-389.
    Since the introduction of mathematical population genetics, its machinery has shaped our fundamental understanding of natural selection. Selection is taken to occur when differential fitnesses produce differential rates of reproductive success, where fitnesses are understood as parameters in a population genetics model. To understand selection is to understand what these parameter values measure and how differences in them lead to frequency changes. I argue that this traditional view is mistaken. The descriptions of natural selection rendered by population genetics models are (...)
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  4. Causal Modeling Semantics for Counterfactuals with Disjunctive Antecedents.Giuliano Rosella & Jan Sprenger - manuscript
    Causal Modeling Semantics (CMS, e.g., Galles and Pearl 1998; Pearl 2000; Halpern 2000) is a powerful framework for evaluating counterfactuals whose antecedent is a conjunction of atomic formulas. We extend CMS to an evaluation of the probability of counterfactuals with disjunctive antecedents, and more generally, to counterfactuals whose antecedent is an arbitrary Boolean combination of atomic formulas. Our main idea is to assign a probability to a counterfactual (A ∨ B) > C at a causal model M as a (...)
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  5. 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 (...)
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  6. Modeling Information.Patrick Grim - 2016 - In Luciano Floridi (ed.), Routledge Handbook of Philosophy of Information. Routledge. pp. 137-152.
    The topics of modeling and information come together in at least two ways. Computational modeling and simulation play an increasingly important role in science, across disciplines from mathematics through physics to economics and political science. The philosophical questions at issue are questions as to what modeling and simulation are adding, altering, or amplifying in terms of scientific information. What changes with regard to information acquisition, theoretical development, or empirical confirmation with contemporary tools of computational modeling? In (...)
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  7. Role Modeling is Beneficial in Moral Character Education: A Commentary on Carr (2023).Nafsika Athanassoulis & Hyemin Han - 2023 - Philosophical Inquiry in Education 30 (3):240-243.
  8. Modeling epistemic communities.Samuli Reijula & Jaakko Kuorikoski - 2019 - In M. Fricker, N. J. L. L. Pedersen, D. Henderson & P. J. Graham (eds.), The Routledge Handbook of Social Epistemology. Routledge.
    We review the most prominent modeling approaches in social epistemology aimed at understand- ing the functioning of epistemic communities and provide a philosophy of science perspective on the use and interpretation of such simple toy models, thereby suggesting how they could be integrated with conceptual and empirical work. We highlight the need for better integration of such models with relevant findings from disciplines such as social psychology and organization studies.
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  9. Modeling without models.Arnon Levy - 2015 - Philosophical Studies 172 (3):781-798.
    Modeling is an important scientific practice, yet it raises significant philosophical puzzles. Models are typically idealized, and they are often explored via imaginative engagement and at a certain “distance” from empirical reality. These features raise questions such as what models are and how they relate to the world. Recent years have seen a growing discussion of these issues, including a number of views that treat modeling in terms of indirect representation and analysis. Indirect views treat the model as (...)
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  10.  95
    Computational Modeling in Cognitive Science: A Manifesto for Change.Caspar Addyman & Robert M. French - 2012 - Topics in Cognitive Science 4 (3):332-341.
    Computational modeling has long been one of the traditional pillars of cognitive science. Unfortunately, the computer models of cognition being developed today have not kept up with the enormous changes that have taken place in computer technology and, especially, in human-computer interfaces. For all intents and purposes, modeling is still done today as it was 25, or even 35, years ago. Everyone still programs in his or her own favorite programming language, source code is rarely made available, accessibility (...)
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  11. Metaphysics as modeling: the handmaiden’s tale.L. A. Paul - 2012 - Philosophical Studies 160 (1):1-29.
    Critics of contemporary metaphysics argue that it attempts to do the hard work of science from the ease of the armchair. Physics, not metaphysics, tells us about the fundamental facts of the world, and empirical psychology is best placed to reveal the content of our concepts about the world. Exploring and understanding the world through metaphysical reflection is obsolete. In this paper, I will show why this critique of metaphysics fails, arguing that metaphysical methods used to make claims about the (...)
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  12. Experimental Modeling in Biology: In Vivo Representation and Stand-ins As Modeling Strategies.Marcel Weber - 2014 - Philosophy of Science 81 (5):756-769.
    Experimental modeling in biology involves the use of living organisms (not necessarily so-called "model organisms") in order to model or simulate biological processes. I argue here that experimental modeling is a bona fide form of scientific modeling that plays an epistemic role that is distinct from that of ordinary biological experiments. What distinguishes them from ordinary experiments is that they use what I call "in vivo representations" where one kind of causal process is used to stand in (...)
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  13.  15
    Modeling the relationship between perceived service quality, tourist satisfaction, and tourists’ behavioral intentions amid COVID-19 pandemic: Evidence of yoga tourists’ perspectives.Ahmed Hassan Abdou, Shaimaa Abo Khanger Mohamed, Ayman Ahmed Farag Khalil, Azzam Ibrahem Albakhit & Ali Jukhayer Nader Alarjani - 2022 - Frontiers in Psychology 13:1003650.
    PurposeThis study aims to investigate the impact of perceived service quality on tourist satisfaction and behavioral intentions and explore the potential mediating role of tourist satisfaction in the relationship between service quality and behavioral intentions in the yoga tourism context during the COVID-19 pandemic. Further, this is to examine to what extent yoga tourist satisfaction directly affects their behavioral intentions.Design/methodology/approachBased on a review of literature, the study proposes a conceptual model to test four hypothesized relationships among the constructs of perceived (...)
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  14. Modeling the Emergence of Lexicons in Homesign Systems.Russell Richie, Charles Yang & Marie Coppola - 2014 - Topics in Cognitive Science 6 (1):183-195.
    It is largely acknowledged that natural languages emerge not just from human brains but also from rich communities of interacting human brains (Senghas, ). Yet the precise role of such communities and such interaction in the emergence of core properties of language has largely gone uninvestigated in naturally emerging systems, leaving the few existing computational investigations of this issue at an artificial setting. Here, we take a step toward investigating the precise role of community structure in the emergence of linguistic (...)
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  15.  30
    Modeling behavioral adaptations.Colin W. Clark - 1991 - Behavioral and Brain Sciences 14 (1):85-93.
    Optimization models have often been useful in attempting to understand the adaptive significance of behavioral traits. Originally such models were applied to isolated aspects of behavior, such as foraging, mating, or parental behavior. In reality, organisms live in complex, ever-changing environments, and are simultaneously concerned with many behavioral choices and their consequences. This target article describes a dynamic modeling technique that can be used to analyze behavior in a unified way. The technique has been widely used in behavioral studies (...)
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  16.  38
    Modeling: Neutral, Null, and Baseline.William C. Bausman - 2018 - Philosophy of Science 85 (4):594-616.
    Two strategies for using a model as “null” are distinguished. Null modeling evaluates whether a process is causally responsible for a pattern by testing it against a null model. Baseline modeling measures the relative significance of various processes responsible for a pattern by detecting deviations from a baseline model. When these strategies are conflated, models are illegitimately privileged as accepted until rejected. I illustrate this using the neutral theory of ecology and draw general lessons from this case. First, (...)
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  17. Optimality modeling in a suboptimal world.Angela Potochnik - 2009 - Biology and Philosophy 24 (2):183-197.
    The fate of optimality modeling is typically linked to that of adaptationism: the two are thought to stand or fall together (Gould and Lewontin, Proc Relig Soc Lond 205:581–598, 1979; Orzack and Sober, Am Nat 143(3):361–380, 1994). I argue here that this is mistaken. The debate over adaptationism has tended to focus on one particular use of optimality models, which I refer to here as their strong use. The strong use of an optimality model involves the claim that selection (...)
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  18.  89
    Modeling causal structures: Volterra’s struggle and Darwin’s success.Raphael Scholl & Tim Räz - 2013 - European Journal for Philosophy of Science 3 (1):115-132.
    The Lotka–Volterra predator-prey-model is a widely known example of model-based science. Here we reexamine Vito Volterra’s and Umberto D’Ancona’s original publications on the model, and in particular their methodological reflections. On this basis we develop several ideas pertaining to the philosophical debate on the scientific practice of modeling. First, we show that Volterra and D’Ancona chose modeling because the problem in hand could not be approached by more direct methods such as causal inference. This suggests a philosophically insightful (...)
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  19. Teleosemantic modeling of cognitive representations.Marc Artiga - 2016 - Biology and Philosophy 31 (4):483-505.
    Naturalistic theories of representation seek to specify the conditions that must be met for an entity to represent another entity. Although these approaches have been relatively successful in certain areas, such as communication theory or genetics, many doubt that they can be employed to naturalize complex cognitive representations. In this essay I identify some of the difficulties for developing a teleosemantic theory of cognitive representations and provide a strategy for accommodating them: to look into models of signaling in evolutionary game (...)
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  20. Modeling artificial agents’ actions in context – a deontic cognitive event ontology.Miroslav Vacura - 2020 - Applied ontology 15 (4):493-527.
    Although there have been efforts to integrate Semantic Web technologies and artificial agents related AI research approaches, they remain relatively isolated from each other. Herein, we introduce a new ontology framework designed to support the knowledge representation of artificial agents’ actions within the context of the actions of other autonomous agents and inspired by standard cognitive architectures. The framework consists of four parts: 1) an event ontology for information pertaining to actions and events; 2) an epistemic ontology containing facts about (...)
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  21. Computer modeling and the fate of folk psychology.John A. Barker - 2002 - In James Moor & Terrell Ward Bynum (eds.), Cyberphilosophy: the intersection of philosophy and computing. Malden, MA: Blackwell.
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  22.  93
    Holistic modeling: an objection to Weisberg’s weighted feature-matching account.Wei Fang - 2017 - Synthese 194 (5):1743–1764.
    Michael Weisberg’s account of scientific models concentrates on the ways in which models are similar to their targets. He intends not merely to explain what similarity consists in, but also to capture similarity judgments made by scientists. In order to scrutinize whether his account fulfills this goal, I outline one common way in which scientists judge whether a model is similar enough to its target, namely maximum likelihood estimation method. Then I consider whether Weisberg’s account could capture the judgments involved (...)
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  23. Computational modeling in philosophy: introduction to a topical collection.Simon Scheller, Christoph Merdes & Stephan Hartmann - 2022 - Synthese 200 (2):1-10.
    Computational modeling should play a central role in philosophy. In this introduction to our topical collection, we propose a small topology of computational modeling in philosophy in general, and show how the various contributions to our topical collection fit into this overall picture. On this basis, we describe some of the ways in which computational models from other disciplines have found their way into philosophy, and how the principles one found here still underlie current trends in the field. (...)
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  24. Causal Modeling and the Efficacy of Action.Holly Andersen - 2022 - In Michael Brent & Lisa Miracchi Titus (eds.), Mental Action and the Conscious Mind. Routledge.
    This paper brings together Thompson's naive action explanation with interventionist modeling of causal structure to show how they work together to produce causal models that go beyond current modeling capabilities, when applied to specifically selected systems. By deploying well-justified assumptions about rationalization, we can strengthen existing causal modeling techniques' inferential power in cases where we take ourselves to be modeling causal systems that also involve actions. The internal connection between means and end exhibited in naive action (...)
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  25. Perspectival Modeling.Michela Massimi - 2018 - Philosophy of Science 85 (3):335-359.
    The goal of this article is to address the problem of inconsistent models and the challenge it poses for perspectivism. I analyze the argument, draw attention to some hidden premises behind it, and deflate them. Then I introduce the notion of perspectival models as a distinctive class of modeling practices whose primary function is exploratory. I illustrate perspectival modeling with two examples taken from contemporary high-energy physics at the Large Hadron Collider at the European Organization for Nuclear Research, (...)
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  26.  72
    Modeling without Mathematics.Martin Thomson-Jones - 2012 - Philosophy of Science 79 (5):761-772.
    Inquiries into the nature of scientific modeling have tended to focus their attention on mathematical models and, relatedly, to think of nonconcrete models as mathematical structures. The arguments of this article are arguments for rethinking both tendencies. Nonmathematical models play an important role in the sciences, and our account of scientific modeling must accommodate that fact. One key to making such accommodations, moreover, is to recognize that one kind of thing we use the term ‘model’ to refer to (...)
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  27.  21
    Modeling Human Syllogistic Reasoning: The Role of “No Valid Conclusion”.Nicolas Riesterer, Daniel Brand, Hannah Dames & Marco Ragni - 2020 - Topics in Cognitive Science 12 (1):446-459.
    After 100+ years of studying syllogistic reasoning, what have we learned? Well, Riesterer and colleagues suggest that we have learned to throw away most of the data! If that seems like a bad idea to you then, be assured, that the authors agree with you. The sad fact is that the conclusion of “No Valid Conclusion” (NVC) is one of the most frequently selected responses in syllogistic reasoning but these “majority data” have been ignored by most researchers. Riesterer and colleagues (...)
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  28.  98
    Modeling Rational Players: Part II.Ken Binmore - 1988 - Economics and Philosophy 4 (1):9-55.
    This is the second part of a two-part paper. It can be read independently of the first part provided that the reader is prepared to go along with the unorthodox views on game theory which were advanced in Part I and are summarized below. The body of the paper is an attempt to study some of the positive implications of such a viewpoint. This requires an exploration of what is involved in modeling “rational players” as computing machines.
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  29.  14
    Modeling Misretrieval and Feature Substitution in Agreement Attraction: A Computational Evaluation.Dario Paape, Serine Avetisyan, Sol Lago & Shravan Vasishth - 2021 - Cognitive Science 45 (8):e13019.
    We present computational modeling results based on a self‐paced reading study investigating number attraction effects in Eastern Armenian. We implement three novel computational models of agreement attraction in a Bayesian framework and compare their predictive fit to the data using k‐fold cross‐validation. We find that our data are better accounted for by an encoding‐based model of agreement attraction, compared to a retrieval‐based model. A novel methodological contribution of our study is the use of comprehension questions with open‐ended responses, so (...)
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  30.  57
    Multilevel Modeling and the Explanatory Autonomy of Psychology.Wei Fang - 2020 - Philosophy of the Social Sciences 50 (3):175-194.
    This article argues for the explanatory autonomy of psychology drawing on cases from the multilevel modeling practice. This is done by considering a multilevel linear model in personality and social psychology, and discussing its philosophical implications for the reductionism debate in philosophy of psychology. I argue that this practice challenges the reductionist position in philosophy of psychology, and supports the explanatory autonomy of psychology.
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  31. Mathematical Modeling and the Nature of Problem Solving.C. W. Castillo-Garsow - 2014 - Constructivist Foundations 9 (3):373-375.
    Open peer commentary on the article “Examining the Role of Re-Presentation in Mathematical Problem Solving: An Application of Ernst von Glasersfeld’s Conceptual Analysis” by Victor V. Cifarelli & Volkan Sevim. Upshot: Problem solving is an enormous field of study, where so-called “problems” can end up having very little in common. One of the least studied categories of problems is open-ended mathematical modeling research. Cifarelli and Sevim’s framework - although not developed for this purpose - may be a useful lens (...)
     
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  32. Modeling Rational Players: Part I.Ken Binmore - 1987 - Economics and Philosophy 3 (2):179-214.
    Game theory has proved a useful tool in the study of simple economic models. However, numerous foundational issues remain unresolved. The situation is particularly confusing in respect of the non-cooperative analysis of games with some dynamic structure in which the choice of one move or another during the play of the game may convey valuable information to the other players. Without pausing for breath, it is easy to name at least 10 rival equilibrium notions for which a serious case can (...)
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  33. Modeling as a Case for the Empirical Philosophy of Science.Ekaterina Svetlova - 2015 - In Hanne Andersen, Nancy J. Nersessian & Susann Wagenknecht (eds.), Empirical Philosophy of Science: Introducing Qualitative Methods into Philosophy of Science. Cham: Springer International Publishing. pp. 65-82.
    In recent years, the emergence of a new trend in contemporary philosophy has been observed in the increasing usage of empirical research methods to conduct philosophical inquiries. Although philosophers primarily use secondary data from other disciplines or apply quantitative methods (experiments, surveys, etc.), the rise of qualitative methods (e.g., in-depth interviews, participant observations and qualitative text analysis) can also be observed. In this paper, I focus on how qualitative research methods can be applied within philosophy of science, namely within the (...)
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  34. Modeling without representation.Alistair M. C. Isaac - 2013 - Synthese 190 (16):3611-3623.
    How can mathematical models which represent the causal structure of the world incompletely or incorrectly have any scientific value? I argue that this apparent puzzle is an artifact of a realist emphasis on representation in the philosophy of modeling. I offer an alternative, pragmatic methodology of modeling, inspired by classic papers by modelers themselves. The crux of the view is that models developed for purposes other than explanation may be justified without reference to their representational properties.
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  35. Modeling and corpus methods in experimental philosophy.Louis Chartrand - 2022 - Philosophy Compass 17 (6).
    Research in experimental philosophy has increasingly been turning to corpus methods to produce evidence for empirical claims, as they open up new possibilities for testing linguistic claims or studying concepts across time and cultures. The present article reviews the quasi-experimental studies that have been done using textual data from corpora in philosophy, with an eye for the modeling and experimental design that enable statistical inference. I find that most studies forego comparisons that could control for confounds, and that only (...)
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  36.  65
    Role Modeling and Reasons.Robert Audi - 2017 - Journal of Moral Philosophy 14 (6):646-668.
    _ Source: _Page Count 23 It is uncontroversial that virtues and reasons are connected. But moral theorists differ widely regarding just what the connections are, and so far there has not been a fully adequate response to the question whether, in some important way, the category of reasons is more basic than that of virtues. This paper pursues that question. It begins with developmental considerations concerning what constitutes role modeling of the kind that best contributes to virtue. In this (...)
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  37.  44
    Information modeling aspects of software development.Timothy R. Colburn - 1998 - Minds and Machines 8 (3):375-393.
    The distinction between the modeling of information and the modeling of data in the creation of automated systems has historically been important because the development tools available to programmers have been wedded to machine oriented data types and processes. However, advances in software engineering, particularly the move toward data abstraction in software design, allow activities reasonably described as information modeling to be performed in the software creation process. An examination of the evolution of programming languages and development (...)
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  38.  57
    On Modeling Cognition and Culture: Why cultural evolution does not require replication of representations.Robert Boyd - 2002 - Journal of Cognition and Culture 2 (2):87-112.
    Formal models of cultural evolution analyze how cognitive processes combine with social interaction to generate the distributions and dynamics of ‘representations.’ Recently, cognitive anthropologists have criticized such models. They make three points: mental representations are non-discrete, cultural transmission is highly inaccurate, and mental representations are not replicated, but rather are ‘reconstructed’ through an inferential process that is strongly affected by cognitive ‘attractors.’ They argue that it follows from these three claims that: 1) models that assume replication or replicators are inappropriate, (...)
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  39.  18
    The Modeling of Nature: Philosophy of Science and Philosophy of Nature in Synthesis.William A. Wallace - 1996 - Catholic University of Amer Press.
    The Modeling of Nature provides an excellent introduction to the fundamentals of natural philosophy, psychology, logic, and epistemology.
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  40.  25
    Modeling the Structure and Dynamics of Semantic Processing.Armand S. Rotaru, Gabriella Vigliocco & Stefan L. Frank - 2018 - Cognitive Science 42 (8):2890-2917.
    The contents and structure of semantic memory have been the focus of much recent research, with major advances in the development of distributional models, which use word co‐occurrence information as a window into the semantics of language. In parallel, connectionist modeling has extended our knowledge of the processes engaged in semantic activation. However, these two lines of investigation have rarely been brought together. Here, we describe a processing model based on distributional semantics in which activation spreads throughout a semantic (...)
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  41. Modeling practical thinking.Matthew Mosdell - 2018 - Mind and Language 34 (4):445-464.
    Intellectualists about knowledge how argue that knowing how to do something is knowing the content of a proposition (i.e, a fact). An important component of this view is the idea that propositional knowledge is translated into behavior when it is presented to the mind in a peculiarly practical way. Until recently, however, intellectualists have not said much about what it means for propositional knowledge to be entertained under thought's practical guise. Carlotta Pavese fills this gap in the intellectualist view by (...)
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  42.  76
    Interdisciplinary modeling: a case study of evolutionary economics.Collin Rice & Joshua Smart - 2011 - Biology and Philosophy 26 (5):655-675.
    Biologists and economists use models to study complex systems. This similarity between these disciplines has led to an interesting development: the borrowing of various components of model-based theorizing between the two domains. A major recent example of this strategy is economists’ utilization of the resources of evolutionary biology in order to construct models of economic systems. This general strategy has come to be called evolutionary economics and has been a source of much debate among economists. Although philosophers have developed literatures (...)
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  43. Optimality modeling and explanatory generality.Angela Potochnik - 2007 - Philosophy of Science 74 (5):680-691.
    The optimality approach to modeling natural selection has been criticized by many biologists and philosophers of biology. For instance, Lewontin (1979) argues that the optimality approach is a shortcut that will be replaced by models incorporating genetic information, if and when such models become available. In contrast, I think that optimality models have a permanent role in evolutionary study. I base my argument for this claim on what I think it takes to best explain an event. In certain contexts, (...)
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  44.  69
    Modeling rationality, morality, and evolution.Peter Danielson (ed.) - 1998 - New York: Oxford University Press.
    This collection focuses on questions that arise when morality is considered from the perspective of recent work on rational choice and evolution. Linking questions like "Is it rational to be moral?" to the evolution of cooperation in "The Prisoners Dilemma," the book brings together new work using models from game theory, evolutionary biology, and cognitive science, as well as from philosophical analysis. Among the contributors are leading figures in these fields, including David Gauthier, Paul M. Churchland, Brian Skyrms, Ronald de (...)
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  45.  19
    Modeling the Development of Children's Use of Optional Infinitives in Dutch and English Using MOSAIC.Daniel Freudenthal, Julian M. Pine & Fernand Gobet - 2006 - Cognitive Science 30 (2):277-310.
    In this study we use a computational model of language learning called model of syntax acquisition in children (MOSAIC) to investigate the extent to which the optional infinitive (OI) phenomenon in Dutch and English can be explained in terms of a resource-limited distributional analysis of Dutch and English child-directed speech. The results show that the same version of MOSAIC is able to simulate changes in the pattern of finiteness marking in 2 children learning Dutch and 2 children learning English as (...)
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  46. Modeling the interaction of computer errors by four-valued contaminating logics.Roberto Ciuni, Thomas Macaulay Ferguson & Damian Szmuc - 2019 - In Rosalie Iemhoff, Michael Moortgat & Ruy de Queiroz (eds.), Logic, Language, Information, and Computation. Berlín, Alemania: pp. 119-139.
    Logics based on weak Kleene algebra (WKA) and related structures have been recently proposed as a tool for reasoning about flaws in computer programs. The key element of this proposal is the presence, in WKA and related structures, of a non-classical truth-value that is “contaminating” in the sense that whenever the value is assigned to a formula ϕ, any complex formula in which ϕ appears is assigned that value as well. Under such interpretations, the contaminating states represent occurrences of a (...)
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  47.  80
    Modeling Organogenesis from Biological First Principles.Maël Montévil & Ana M. Soto - 2023 - In Matteo Mossio (ed.), Organization in Biology. Springer. pp. 263-283.
    Unlike inert objects, organisms and their cells have the ability to initiate activity by themselves and thus change their properties or states even in the absence of an external cause. This crucial difference led us to search for principles suitable for the study organisms. We propose that cells follow the default state of proliferation with variation and motility, a principle of biological inertia. This means that in the presence of sufficient nutrients, cells will express their default state. We also propose (...)
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  48.  20
    Computational Modeling of Cognition and Behavior.Simon Farrell & Stephan Lewandowsky - 2017 - Cambridge University Press.
    Computational modeling is now ubiquitous in psychology, and researchers who are not modelers may find it increasingly difficult to follow the theoretical developments in their field. This book presents an integrated framework for the development and application of models in psychology and related disciplines. Researchers and students are given the knowledge and tools to interpret models published in their area, as well as to develop, fit, and test their own models. Both the development of models and key features of (...)
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  49.  74
    Modeling Strategies for Measuring Phenomena In- and Outside the Laboratory.Marcel Boumans - 2009 - In Henk W. de Regt (ed.), Epsa Philosophy of Science: Amsterdam 2009. Springer. pp. 1--11.
    The Representational Theory of Measurement conceives measurement as establishing homomorphisms from empirical relational structures into numerical relation structures, called models. There are two different approaches to deal with the justification of a model: an axiomatic and an empirical approach. The axiomatic approach verifies whether a given relational structure satisfies certain axioms to secure homomorphic mapping. The empirical approach conceives models to function as measuring instruments by transferring observations of a phenomenon under investigation into quantitative facts about that phenomenon. These facts (...)
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    Modeling the precautionary principle with lexical utilities.Paul Bartha & C. Tyler DesRoches - 2021 - Synthese 199 (3-4):8701-8740.
    Confronted with the possibility of severe environmental harms, such as catastrophic climate change, some researchers have suggested that we should abandon the principle at the heart of standard decision theory—the injunction to maximize expected utility—and embrace a different one: the Precautionary Principle. Arguably, the most sophisticated philosophical treatment of the Precautionary Principle is due to Steel. Steel interprets PP as a qualitative decision rule and appears to conclude that a quantitative decision-theoretic statement of PP is both impossible and unnecessary. In (...)
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