Results for 'Models and modelling'

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  1. Modeling Measurement: Error and Uncertainty.Alessandro Giordani & Luca Mari - 2014 - In Marcel Boumans, Giora Hon & Arthur C. Petersen (eds.), Error and Uncertainty in Scientific Practice. Pickering & Chatto. pp. 79-96.
    In the last few decades the role played by models and modeling activities has become a central topic in the scientific enterprise. In particular, it has been highlighted both that the development of models constitutes a crucial step for understanding the world and that the developed models operate as mediators between theories and the world. Such perspective is exploited here to cope with the issue as to whether error-based and uncertainty-based modeling of measurement are incompatible, and thus (...)
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  2. Coherence and correspondence in the network dynamics of belief suites.Patrick Grim, Andrew Modell, Nicholas Breslin, Jasmine Mcnenny, Irina Mondescu, Kyle Finnegan, Robert Olsen, Chanyu An & Alexander Fedder - 2017 - Episteme 14 (2):233-253.
    Coherence and correspondence are classical contenders as theories of truth. In this paper we examine them instead as interacting factors in the dynamics of belief across epistemic networks. We construct an agent-based model of network contact in which agents are characterized not in terms of single beliefs but in terms of internal belief suites. Individuals update elements of their belief suites on input from other agents in order both to maximize internal belief coherence and to incorporate ‘trickled in’ elements of (...)
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  3. Mathematical Modeling in Biology: Philosophy and Pragmatics.Rasmus Grønfeldt Winther - 2012 - Frontiers in Plant Evolution and Development 2012:1-3.
    Philosophy can shed light on mathematical modeling and the juxtaposition of modeling and empirical data. This paper explores three philosophical traditions of the structure of scientific theory—Syntactic, Semantic, and Pragmatic—to show that each illuminates mathematical modeling. The Pragmatic View identifies four critical functions of mathematical modeling: (1) unification of both models and data, (2) model fitting to data, (3) mechanism identification accounting for observation, and (4) prediction of future observations. Such facets are explored using a recent exchange between two (...)
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  4. Synthetic Modeling and Mechanistic Account: Material Recombination and Beyond.Tarja Knuuttila & Andrea Loettgers - 2013 - Philosophy of Science 80 (5):874-885.
    Recently, Bechtel and Abrahamsen have argued that mathematical models study the dynamics of mechanisms by recomposing the components and their operations into an appropriately organized system. We will study this claim through the practice of combinational modeling in circadian clock research. In combinational modeling, experiments on model organisms and mathematical/computational models are combined with a new type of model—a synthetic model. We argue that the strategy of recomposition is more complicated than what Bechtel and Abrahamsen indicate. Moreover, synthetic (...)
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  5. 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 (...)
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  6. Modeling and Inferring in Science.Emiliano Ippoliti, Thomas Nickles & Fabio Sterpetti - 2016 - In Emiliano Ippoliti, Fabio Sterpetti & Thomas Nickles (eds.), Models and Inferences in Science. Cham: Springer. pp. 1-9.
    Science continually contributes new models and rethinks old ones. The way inferences are made is constantly being re-evaluated. The practice and achievements of science are both shaped by this process, so it is important to understand how models and inferences are made. But, despite the relevance of models and inference in scientific practice, these concepts still remain contro-versial in many respects. The attempt to understand the ways models and infer-ences are made basically opens two roads. The (...)
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  7. Diversity and Democracy: Agent-Based Modeling in Political Philosophy.Bennett Holman, William Berger, Daniel J. Singer, Patrick Grim & Aaron Bramson - 2018 - Historical Social Research 43:259-284.
    Agent-based models have played a prominent role in recent debates about the merits of democracy. In particular, the formal model of Lu Hong and Scott Page and the associated “diversity trumps ability” result has typically been seen to support the epistemic virtues of democracy over epistocracy (i.e., governance by experts). In this paper we first identify the modeling choices embodied in the original formal model and then critique the application of the Hong-Page results to philosophical debates on the relative (...)
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  8. Economic and mathematical modeling of integration influence of information and communication technologies on the development of e-commerce of industrial enterprises.Igor Kryvovyazyuk, Igor Britchenko, Liubov Kovalska, Iryna Oleksandrenko, Liudmyla Pavliuk & Olena Zavadska - 2023 - Journal of Theoretical and Applied Information Technology 101 (11):3801-3815.
    This research aims at establishing the impact of information and communication technologies (ICT) on e-commerce development of industrial enterprises by means of economic and mathematical modelling. The goal was achieved using the following methods: theoretical generalization, analysis and synthesis (to critically analyse the scientific approaches of scientists regarding the expediency of using mathematical models in the context of enterprises’ e-commerce development), target, comparison and grouping (to reveal innovative methodological approach to assessing ICT impact on e-commerce development of industrial (...)
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  9. Standard Aberration: Cancer Biology and the Modeling Account of Normal Function.Seth Goldwasser - 2023 - Biology and Philosophy 38 (1):(4) 1-33.
    Cancer biology features the ascription of normal functions to parts of cancers. At least some ascriptions of function in cancer biology track local normality of parts within the global abnormality of the aberration to which those parts belong. That is, cancer biologists identify as functions activities that, in some sense, parts of cancers are supposed to perform, despite cancers themselves having no purpose. The present paper provides a theory to accommodate these normal function ascriptions—I call it the Modeling Account of (...)
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  10. Modeling and experimenting.Isabelle Peschard - 2011 - In Paul Humphreys & Cyrille Imbert (eds.), Models, Simulations, and Representations. New York: 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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  11. 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 a (...)
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  12. Biological Control Variously Materialized: Modeling, Experimentation and Exploration in Multiple Media.Tarja Knuuttila & Andrea Loettgers - 2021 - Perspectives on Science 29 (4):468-492.
    This paper examines two parallel discussions of scientific modeling which have invoked experimentation in addressing the role of models in scientific inquiry. One side discusses the experimental character of models, whereas the other focuses on their exploratory uses. Although both relate modeling to experimentation, they do so differently. The former has considered the similarities and differences between models and experiments, addressing, in particular, the epistemic value of materiality. By contrast, the focus on exploratory modeling has highlighted the (...)
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  13. Modeling Epistemology: Examples and Analysis in Computational Philosophy of Science.Patrick Grim - 2019 - In A. Del Barrio, C. J. Lynch, F. J. Barros & X. Hu (eds.), IEEE SpringSim Proceedings 2019. IEEE. pp. 1-12.
    What structure of scientific communication and cooperation, between what kinds of investigators, is best positioned to lead us to the truth? Against an outline of standard philosophical characteristics and a recent turn to social epistemology, this paper surveys highlights within two strands of computational philosophy of science that attempt to work toward an answer to this question. Both strands emerge from abstract rational choice theory and the analytic tradition in philosophy of science rather than postmodern sociology of science. The first (...)
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  14. Causal Modeling and the Efficacy of Action.Holly Andersen - 2022 - In Michael Brent & Lisa Miracchi Titus (eds.), Mental Action and the Conscious Mind. New York, NY: 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 explanation has a (...)
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  15. Agent-based modeling and the fallacies of individualism.Brian Epstein - 2011 - In Paul Humphreys & Cyrille Imbert (eds.), Models, Simulations, and Representations. New York: 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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  16. Epistemic artifacts and the modal dimension of modeling.Tarja Knuuttila - 2021 - European Journal for Philosophy of Science 11 (3):1-18.
    The epistemic value of models has traditionally been approached from a representational perspective. This paper argues that the artifactual approach evades the problem of accounting for representation and better accommodates the modal dimension of modeling. From an artifactual perspective, models are viewed as erotetic vehicles constrained by their construction and available representational tools. The modal dimension of modeling is approached through two case studies. The first portrays mathematical modeling in economics, while the other discusses the modeling practice of (...)
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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. Ecological-enactive scientific cognition: modeling and material engagement.Giovanni Rolla & Felipe Novaes - 2020 - Phenomenology and the Cognitive Sciences 1:1-19.
    Ecological-enactive approaches to cognition aim to explain cognition in terms of the dynamic coupling between agent and environment. Accordingly, cognition of one’s immediate environment (which is sometimes labeled “basic” cognition) depends on enaction and the picking up of affordances. However, ecological-enactive views supposedly fail to account for what is sometimes called “higher” cognition, i.e., cognition about potentially absent targets, which therefore can only be explained by postulating representational content. This challenge levelled against ecological-enactive approaches highlights a putative explanatory gap between (...)
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  19. Modeling social and evolutionary games.Angela Potochnik - 2012 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 43 (1):202-208.
    When game theory was introduced to biology, the components of classic game theory models were replaced with elements more befitting evolutionary phenomena. The actions of intelligent agents are replaced by phenotypic traits; utility is replaced by fitness; rational deliberation is replaced by natural selection. In this paper, I argue that this classic conception of comprehensive reapplication is misleading, for it overemphasizes the discontinuity between human behavior and evolved traits. Explicitly considering the representational roles of evolutionary game theory brings to (...)
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  20. Imagination in scientific modeling.Adam Toon - 2016 - In Amy Kind (ed.), The Routledge Handbook of the Philosophy of Imagination. New York: Routledge. pp. 451-462.
    Modeling is central to scientific inquiry. It also depends heavily upon the imagination. In modeling, scientists seem to turn their attention away from the complexity of the real world to imagine a realm of perfect spheres, frictionless planes and perfect rational agents. Modeling poses many questions. What are models? How do they relate to the real world? Recently, a number of philosophers have addressed these questions by focusing on the role of the imagination in modeling. Some have also drawn (...)
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  21. Using models to correct data: paleodiversity and the fossil record.Alisa Bokulich - 2018 - Synthese 198 (Suppl 24):5919-5940.
    Despite an enormous philosophical literature on models in science, surprisingly little has been written about data models and how they are constructed. In this paper, I examine the case of how paleodiversity data models are constructed from the fossil data. In particular, I show how paleontologists are using various model-based techniques to correct the data. Drawing on this research, I argue for the following related theses: first, the ‘purity’ of a data model is not a measure of (...)
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  22. Computer modeling and the fate of folk psychology.John A. Barker - 2002 - Metaphilosophy 33 (1-2):30-48.
    Although Paul Churchland and Jerry Fodor both subscribe to the so-called theory-theory– the theory that folk psychology (FP) is an empirical theory of behavior – they disagree strongly about FP’s fate. Churchland contends that FP is a fundamentally flawed view analogous to folk biology, and he argues that recent advances in computational neuroscience and connectionist AI point toward development of a scientifically respectable replacement theory that will give rise to a new common-sense psychology. Fodor, however, wagers that FP will be (...)
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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. Moreover, (...)
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  24. Modeling of Biological and Social Phases of Big History.Leonid Grinin, Andrey V. Korotayev & Alexander V. Markov - 2015 - In Leonid Grinin & Andrey Korotayev (eds.), Evolution: From Big Bang to Nanorobots. Uchitel Publishing House. pp. 111-150.
    In the first part of this article we survey general similarities and differences between biological and social macroevolution. In the second (and main) part, we consider a concrete mathematical model capable of describing important features of both biological and social macroevolution. In mathematical models of historical macrodynamics, a hyperbolic pattern of world population growth arises from non-linear, second-order positive feedback between demographic growth and technological development. Based on diverse paleontological data and an analogy with macrosociological models, we suggest (...)
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  25. Mathematical Modeling of Biological and Social Evolutionary Macrotrends.Leonid Grinin, Alexander V. Markov & Andrey V. Korotayev - 2014 - In Leonid Grinin & Andrey Korotayev (eds.), History & Mathematics: Trends and Cycles. Volgograd: "Uchitel" Publishing House. pp. 9-48.
    In the first part of this article we survey general similarities and differences between biological and social macroevolution. In the second (and main) part, we consider a concrete mathematical model capable of describing important features of both biological and social macroevolution. In mathematical models of historical macrodynamics, a hyperbolic pattern of world population growth arises from non-linear, second-order positive feedback between demographic growth and technological development. Based on diverse paleontological data and an analogy with macrosociological models, we suggest (...)
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  26. Modeling Morality.Walter Veit - 2019 - In Matthieu Fontaine, Cristina Barés-Gómez, Francisco Salguero-Lamillar, Lorenzo Magnani & Ángel Nepomuceno-Fernández (eds.), Model-Based Reasoning in Science and Technology: Inferential Models for Logic, Language, Cognition and Computation. Springer Verlag. pp. 83–102.
    Unlike any other field, the science of morality has drawn attention from an extraordinarily diverse set of disciplines. An interdisciplinary research program has formed in which economists, biologists, neuroscientists, psychologists, and even philosophers have been eager to provide answers to puzzling questions raised by the existence of human morality. Models and simulations, for a variety of reasons, have played various important roles in this endeavor. Their use, however, has sometimes been deemed as useless, trivial and inadequate. The role of (...)
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  27. Models as make-believe: imagination, fiction, and scientific representation.Adam Toon - 2012 - New York: Palgrave-Macmillan.
    Models as Make-Believe offers a new approach to scientific modelling by looking to an unlikely source of inspiration: the dolls and toy trucks of children's games of make-believe.
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  28. Predicitive modeling, empowering women, and COVID-19 in South Sumatra, Indonesia.Yeni Yeni, Najmah Najmah & Davies Sharyn Graham - 2020 - ASEAN Journal of Community Engagement 4 (1):104-133.
    The Coronavirus disease (COVID-19) has spread to almost all provinces in Indonesia, including South Sumatra. Epidemiological models are required to provide evidence for public health policymakers to mitigate the virus. The aim of this study is: 1) to create a prediction model for COVID-19 cases in South Sumatra to help inform about public health policy and 2) to reflect on women’s experiences to provide solutions for mitigating the impact of COVID-19. This study uses quantitative and qualitative methods. A quantitative (...)
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  29. Variables of Scientific Concept Modeling and Their Formalization.Vladimir Kuznetsov - 2009 - In В.И Маркин (ed.), Philosophy of mathematics: current problems. Proceedings of the second international conference (Философия математики: актуальные проблемы. Тезисы второй международной конференции). pp. 268-270.
    There are no universally adopted answers to the natural questions about scientific concepts: What are they? What is their structure? What are their functions? How many kinds of them are there? Do they change? Ironically, most if not all scientific monographs or articles mention concepts, but the scientific studies of scientific concepts are rare in occurrence. It is well known that the necessary stage of any scientific study is constructing the model of objects in question. Many years logical modeling was (...)
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  30. Extending, changing, and explaining the brain.Mazviita Chirimuuta - 2013 - Biology and Philosophy 28 (4):613-638.
    This paper addresses concerns raised recently by Datteri (Biol Philos 24:301–324, 2009) and Craver (Philos Sci 77(5):840–851, 2010) about the use of brain-extending prosthetics in experimental neuroscience. Since the operation of the implant induces plastic changes in neural circuits, it is reasonable to worry that operational knowledge of the hybrid system will not be an accurate basis for generalisation when modelling the unextended brain. I argue, however, that Datteri’s no-plasticity constraint unwittingly rules out numerous experimental paradigms in behavioural and (...)
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  31. Minimal models of consciousness: Understanding consciousness in human and non-human systems.Wanja Wiese - manuscript
    Should models of consciousness be detailed _mechanistic_ models of particular types of systems, or should they be _minimal_ models that abstract away from the underlying mechanistic details and provide generalisations? Detailed mechanistic models may afford a complete and precise account of consciousness in human beings and other, physiologically similar mammals. But they do not provide a good model of consciousness in other animals, such as non-vertebrates, let alone artificial systems. Minimal models can be applicable to (...)
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  32. The Sum of the Parts: Large-Scale Modeling in Systems Biology.Fridolin Gross & Sara Green - 2017 - Philosophy, Theory, and Practice in Biology 9 (10).
    Systems biologists often distance themselves from reductionist approaches and formulate their aim as understanding living systems “as a whole.” Yet, it is often unclear what kind of reductionism they have in mind, and in what sense their methodologies would offer a superior approach. To address these questions, we distinguish between two types of reductionism which we call “modular reductionism” and “bottom-up reductionism.” Much knowledge in molecular biology has been gained by decomposing living systems into functional modules or through detailed studies (...)
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  33. Minimal Models and the Generalized Ontic Conception of Scientific Explanation.Mark Povich - 2018 - British Journal for the Philosophy of Science 69 (1):117-137.
    Batterman and Rice ([2014]) argue that minimal models possess explanatory power that cannot be captured by what they call ‘common features’ approaches to explanation. Minimal models are explanatory, according to Batterman and Rice, not in virtue of accurately representing relevant features, but in virtue of answering three questions that provide a ‘story about why large classes of features are irrelevant to the explanandum phenomenon’ ([2014], p. 356). In this article, I argue, first, that a method (the renormalization group) (...)
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  34. Kuznetsov V. From studying theoretical physics to philosophical modeling scientific theories: Under influence of Pavel Kopnin and his school.Volodymyr Kuznetsov - 2017 - ФІЛОСОФСЬКІ ДІАЛОГИ’2016 ІСТОРІЯ ТА СУЧАСНІСТЬ У НАУКОВИХ РОЗМИСЛАХ ІНСТИТУТУ ФІЛОСОФІЇ 11:62-92.
    The paper explicates the stages of the author’s philosophical evolution in the light of Kopnin’s ideas and heritage. Starting from Kopnin’s understanding of dialectical materialism, the author has stated that category transformations of physics has opened from conceptualization of immutability to mutability and then to interaction, evolvement and emergence. He has connected the problem of physical cognition universals with an elaboration of the specific system of tools and methods of identifying, individuating and distinguishing objects from a scientific theory domain. The (...)
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  35. The Modeling and Control of Visual Perception.Ronald A. Rensink - 2007 - In Wayne D. Gray (ed.), Integrated Models of Cognitive Systems. Oxford University Press. pp. 132-148.
    Recent developments in vision science have resulted in several major changes in our understanding of human visual perception. For example, attention no longer appears necessary for "visual intelligence"--a large amount of sophisticated processing can be done without it. Scene perception no longer appears to involve static, general-purpose descriptions, but instead may involve dynamic representations whose content depends on the individual and the task. And vision itself no longer appears to be limited to the production of a conscious "picture"--it may also (...)
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  36. Model Diversity and the Embarrassment of Riches.Walter Veit - unknown
    In a recent special issue dedicated to Dani Rodrik’s (2015) influential monograph Economics Rules, Grüne-Yanoff and Marchionni (2018) raise a potentially damning problem for Rodrik’s suggestion that progress in economics should be understood and measured laterally, by a continuous expansion of new models. They argue that this could lead to an “embarrassment of riches”, i.e. the rapid expansion of our model library to such an extent that we become unable to choose between the available models, and thus needs (...)
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  37. Models and Explanation.Alisa Bokulich - 2017 - In Magnani Lorenzo & Bertolotti Tommaso Wayne (eds.), Springer Handbook of Model-Based Science. Springer. pp. 103-118.
    Detailed examinations of scientific practice have revealed that the use of idealized models in the sciences is pervasive. These models play a central role in not only the investigation and prediction of phenomena, but in their received scientific explanations as well. This has led philosophers of science to begin revising the traditional philosophical accounts of scientific explanation in order to make sense of this practice. These new model-based accounts of scientific explanation, however, raise a number of key questions: (...)
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  38. Improved model exploration for the relationship between moral foundations and moral judgment development using Bayesian Model Averaging.Hyemin Han & Kelsie J. Dawson - 2022 - Journal of Moral Education 51 (2):204-218.
    Although some previous studies have investigated the relationship between moral foundations and moral judgment development, the methods used have not been able to fully explore the relationship. In the present study, we used Bayesian Model Averaging (BMA) in order to address the limitations in traditional regression methods that have been used previously. Results showed consistency with previous findings that binding foundations are negatively correlated with post-conventional moral reasoning and positively correlated with maintaining norms and personal interest schemas. In addition to (...)
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  39. Model templates within and between disciplines: from magnets to gases – and socio-economic systems.Tarja Knuuttila & Andrea Loettgers - 2016 - European Journal for Philosophy of Science 6 (3):377-400.
    One striking feature of the contemporary modelling practice is its interdisciplinary nature. The same equation forms, and mathematical and computational methods, are used across different disciplines, as well as within the same discipline. Are there, then, differences between intra- and interdisciplinary transfer, and can the comparison between the two provide more insight on the challenges of interdisciplinary theoretical work? We will study the development and various uses of the Ising model within physics, contrasting them to its applications to socio-economic (...)
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  40. Detection and Mathematical Modeling of Anxiety Disorder Based on Socioeconomic Factors Using Machine Learning Techniques.Razan Ibrahim Alsuwailem & Surbhi Bhatia - 2022 - Human-Centric Computing and Information Sciences 12:52.
    The mental risk poses a high threat to the individuals, especially overseas demographic, including expatriates in comparison to the general Arab demographic. Since Arab countries are renowned for their multicultural environment with half of the population of students and faculties being international, this paper focuses on a comprehensive analysis of mental health problems such as depression, stress, anxiety, isolation, and other unfortunate conditions. The dataset is developed from a web-based survey. The detailed exploratory data analysis is conducted on the dataset (...)
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  41. An Introduction to Hard and Soft Data Fusion via Conceptual Spaces Modeling for Space Event Characterization.Jeremy Chapman, David Kasmier, John L. Crassidis, James L. Llinas, Barry Smith & Alex P. Cox - 2021 - In Jeremy Chapman, David Kasmier, John L. Crassidis, James L. Llinas, Barry Smith & Alex P. Cox (eds.), National Symposium on Sensor & Data Fusion (NSSDF), Military Sensing Symposia (MSS).
    This paper describes an AFOSR-supported basic research program that focuses on developing a new framework for combining hard with soft data in order to improve space situational awareness. The goal is to provide, in an automatic and near real-time fashion, a ranking of possible threats to blue assets (assets trying to be protected) from red assets (assets with hostile intentions). The approach is based on Conceptual Spaces models, which combine features from traditional associative and symbolic cognitive models. While (...)
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  42. Causal Models and the Logic of Counterfactuals.Jonathan Vandenburgh - manuscript
    Causal models show promise as a foundation for the semantics of counterfactual sentences. However, current approaches face limitations compared to the alternative similarity theory: they only apply to a limited subset of counterfactuals and the connection to counterfactual logic is not straightforward. This paper addresses these difficulties using exogenous interventions, where causal interventions change the values of exogenous variables rather than structural equations. This model accommodates judgments about backtracking counterfactuals, extends to logically complex counterfactuals, and validates familiar principles of (...)
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  43. Modeling Mental Qualities.Andrew Y. Lee - 2021 - The Philosophical Review 130 (2):263-209.
    Conscious experiences are characterized by mental qualities, such as those involved in seeing red, feeling pain, or smelling cinnamon. The standard framework for modeling mental qualities represents them via points in geometrical spaces, where distances between points inversely correspond to degrees of phenomenal similarity. This paper argues that the standard framework is structurally inadequate and develops a new framework that is more powerful and flexible. The core problem for the standard framework is that it cannot capture precision structure: for example, (...)
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  44. Modelling competing legal arguments using Bayesian model comparison and averaging.Martin Neil, Norman Fenton, David Lagnado & Richard David Gill - 2019 - Artificial Intelligence and Law 27 (4):403-430.
    Bayesian models of legal arguments generally aim to produce a single integrated model, combining each of the legal arguments under consideration. This combined approach implicitly assumes that variables and their relationships can be represented without any contradiction or misalignment, and in a way that makes sense with respect to the competing argument narratives. This paper describes a novel approach to compare and ‘average’ Bayesian models of legal arguments that have been built independently and with no attempt to make (...)
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  45. Climate Models, Calibration, and Confirmation.Katie Steele & Charlotte Werndl - 2013 - British Journal for the Philosophy of Science 64 (3):609-635.
    We argue that concerns about double-counting—using the same evidence both to calibrate or tune climate models and also to confirm or verify that the models are adequate—deserve more careful scrutiny in climate modelling circles. It is widely held that double-counting is bad and that separate data must be used for calibration and confirmation. We show that this is far from obviously true, and that climate scientists may be confusing their targets. Our analysis turns on a Bayesian/relative-likelihood approach (...)
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  46. 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 (...)
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  47. Causation and Causal Selection in the Biopsychosocial Model of Health and Disease.Hane Htut Maung - 2021 - European Journal of Analytic Philosophy 17 (2):5-27.
    In The Biopsychosocial Model of Health and Disease, Derek Bolton and Grant Gillett argue that a defensible updated version of the biopsychosocial model requires a metaphysically adequate account of disease causation that can accommodate biological, psychological, and social factors. This present paper offers a philosophical critique of their account of biopsychosocial causation. I argue that their account relies on claims about the normativity and the semantic content of biological information that are metaphysically contentious. Moreover, I suggest that these claims are (...)
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  48. Mathematical Modelling and Contrastive Explanation.Adam Morton - 1990 - Canadian Journal of Philosophy 20 (Supplement):251-270.
    Mathematical models provide explanations of limited power of specific aspects of phenomena. One way of articulating their limits here, without denying their essential powers, is in terms of contrastive explanation.
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  49. 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 (...)
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  50. Causal Models and Metaphysics - Part 1: Using Causal Models.Jennifer McDonald - forthcoming - Philosophy Compass.
    This paper provides a general introduction to the use of causal models in the metaphysics of causation, specifically structural equation models and directed acyclic graphs. It reviews the formal framework, lays out a method of interpretation capable of representing different underlying metaphysical relations, and describes the use of these models in analyzing causation.
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