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  1. A Mid-Level Approach to Modeling Scientific Communities.Audrey Harnagel - 2019 - Studies in History and Philosophy of Science Part A 76:49-59.
    This paper provides an account of mid-level models, which calibrate highly theoretical agent-based models of scientific communities by incorporating empirical information from real-world systems. As a result, these models more closely correspond with real-world communities, and are better suited for informing policy decisions than extant how-possibly models. I provide an exemplar of a mid-level model of science funding allocation that incorporates bibliometric data from scientific publications and data generated from empirical studies of peer review into an epistemic landscape model. The (...)
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  • Against the New Fictionalism: A Hybrid View of Scientific Models.Chuang Liu - 2016 - International Studies in the Philosophy of Science 30 (1):39-54.
    This article develops an approach to modelling and models in science—the hybrid view—that is against model fictionalism of a recent stripe. It further argues that there is a version of fictionalism about models to which my approach is neutral and which makes sense only if one adopts a special sort of antirealism. Otherwise, my approach strongly suggests that one stay away from fictionalism and embrace realism directly.
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  • Understanding (with) Toy Models.Alexander Reutlinger, Dominik Hangleiter & Stephan Hartmann - 2018 - British Journal for the Philosophy of Science 69 (4):1069-1099.
    Toy models are highly idealized and extremely simple models. Although they are omnipresent across scientific disciplines, toy models are a surprisingly under-appreciated subject in the philosophy of science. The main philosophical puzzle regarding toy models concerns what the epistemic goal of toy modelling is. One promising proposal for answering this question is the claim that the epistemic goal of toy models is to provide individual scientists with understanding. The aim of this article is to precisely articulate and to defend this (...)
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  • What Distinguishes Data From Models?Sabina Leonelli - 2019 - European Journal for Philosophy of Science 9 (2):22.
    I propose a framework that explicates and distinguishes the epistemic roles of data and models within empirical inquiry through consideration of their use in scientific practice. After arguing that Suppes’ characterization of data models falls short in this respect, I discuss a case of data processing within exploratory research in plant phenotyping and use it to highlight the difference between practices aimed to make data usable as evidence and practices aimed to use data to represent a specific phenomenon. I then (...)
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  • Are More Details Better? On the Norms of Completeness for Mechanistic Explanations.Carl F. Craver & David M. Kaplan - 2020 - British Journal for the Philosophy of Science 71 (1):287-319.
    Completeness is an important but misunderstood norm of explanation. It has recently been argued that mechanistic accounts of scientific explanation are committed to the thesis that models are complete only if they describe everything about a mechanism and, as a corollary, that incomplete models are always improved by adding more details. If so, mechanistic accounts are at odds with the obvious and important role of abstraction in scientific modelling. We respond to this characterization of the mechanist’s views about abstraction and (...)
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  • The Argument From Surprise.Adrian Currie - 2018 - Canadian Journal of Philosophy 48 (5):639-661.
    I develop an account of productive surprise as an epistemic virtue of scientific investigations which does not turn on psychology alone. On my account, a scientific investigation is potentially productively surprising when results can conflict with epistemic expectations, those expectations pertain to a wide set of subjects. I argue that there are two sources of such surprise in science. One source, often identified with experiments, involves bringing our theoretical ideas in contact with new empirical observations. Another, often identified with simulations, (...)
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  • Model Pluralism.Walter Veit - 2020 - Philosophy of the Social Sciences 50 (2):91-114.
    This paper introduces and defends an account of model-based science that I dub model pluralism. I argue that despite a growing awareness in the philosophy of science literature of the multiplicity, diversity, and richness of models and modeling practices, more radical conclusions follow from this recognition than have previously been inferred. Going against the tendency within the literature to generalize from single models, I explicate and defend the following two core theses: any successful analysis of models must target sets of (...)
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  • Dynamics and Diversity in Epistemic Communities.Cailin O’Connor & Justin Bruner - 2019 - Erkenntnis 84 (1):101-119.
    Bruner shows that in cultural interactions, members of minority groups will learn to interact with members of majority groups more quickly—minorities tend to meet majorities more often as a brute fact of their respective numbers—and, as a result, may come to be disadvantaged in situations where they divide resources. In this paper, we discuss the implications of this effect for epistemic communities. We use evolutionary game theoretic methods to show that minority groups can end up disadvantaged in academic interactions like (...)
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  • Adding Logic to the Toolbox of Molecular Biology.Giovanni Boniolo, Marcello D’Agostino, Mario Piazza & Gabriele Pulcini - 2015 - European Journal for Philosophy of Science 5 (3):399-417.
    The aim of this paper is to argue that logic can play an important role in the “toolbox” of molecular biology. We show how biochemical pathways, i.e., transitions from a molecular aggregate to another molecular aggregate, can be viewed as deductive processes. In particular, our logical approach to molecular biology — developed in the form of a natural deduction system — is centered on the notion of Curry-Howard isomorphism, a cornerstone in nineteenth-century proof-theory.
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  • Derivational Robustness, Credible Substitute Systems and Mathematical Economic Models: The Case of Stability Analysis in Walrasian General Equilibrium Theory.D. Wade Hands - 2016 - European Journal for Philosophy of Science 6 (1):31-53.
    This paper supports the literature which argues that derivational robustness can have epistemic import in highly idealized economic models. The defense is based on a particular example from mathematical economic theory, the dynamic Walrasian general equilibrium model. It is argued that derivational robustness first increased and later decreased the credibility of the Walrasian model. The example demonstrates that derivational robustness correctly describes the practices of a particular group of influential economic theorists and provides support for the arguments of philosophers who (...)
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  • The Turn of the Valve: Representing with Material Models.Roman Frigg & James Nguyen - 2018 - European Journal for Philosophy of Science 8 (2):205-224.
    Many scientific models are representations. Building on Goodman and Elgin’s notion of representation-as we analyse what this claim involves by providing a general definition of what makes something a scientific model and formulating a novel account of how models represent. We call the result the DEKI account of representation, which offers a complex kind of representation involving an interplay of denotation, exemplification, keying up of properties, and imputation. Throughout we focus on material models, and we illustrate our claims with the (...)
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  • Scientific Realism: What It is, the Contemporary Debate, and New Directions.Darrell P. Rowbottom - 2019 - Synthese 196 (2):451-484.
    First, I answer the controversial question ’What is scientific realism?’ with extensive reference to the varied accounts of the position in the literature. Second, I provide an overview of the key developments in the debate concerning scientific realism over the past decade. Third, I provide a summary of the other contributions to this special issue.
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  • Similarity and Representation in Chemical Knowledge Practices.Juan Bautista Bengoetxea, Oliver Todt & José Luis Luján - 2014 - Foundations of Chemistry 16 (3):215-233.
    This paper argues for the theoretical and practical validity of similarity as a useful epistemological tool in scientific knowledge generation, specifically in chemistry. Classical analyses of similarity in philosophy of science do not account for the concept’s practical significance in scientific activities. We recur to examples from chemistry to counter the claim of authors like Quine or Goodman to the effect that similarity must be excluded from scientific practices . In conclusion we argue that more recent conceptualizations of the notion (...)
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  • Probability and Manipulation: Evolution and Simulation in Applied Population Genetics.Marshall Abrams - 2015 - Erkenntnis 80 (S3):519-549.
    I define a concept of causal probability and apply it to questions about the role of probability in evolutionary processes. Causal probability is defined in terms of manipulation of patterns in empirical outcomes by manipulating properties that realize objective probabilities. The concept of causal probability allows us see how probabilities characterized by different interpretations of probability can share a similar causal character, and does so in such way as to allow new inferences about relationships between probabilities realized in different chance (...)
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  • Anchoring Fictional Models: Adam Toon: Models as Make-Believe. Plagrave-Macmillan, 2012.Arnon Levy - 2013 - Biology and Philosophy 28 (4):693-701.
  • Semblance or Similarity? Reflections on Simulation and Similarity: Michael Weisberg: Simulation and Similarity: Using Models to Understand the World. Oxford University Press, 2013. 224pp. ISBN 9780199933662, $65.00.Jay Odenbaugh - 2015 - Biology and Philosophy 30 (2):277-291.
    In this essay, I critically evaluate components of Michael Weisberg’s approach to models and modeling in his book Simulation and Similarity. First, I criticize his account of the ontology of models and mathematics. Second, I respond to his objections to fictionalism regarding models arguing that they fail. Third, I sketch a deflationary approach to models that retains many elements of his account but avoids the inflationary commitments.
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  • Factive Scientific Understanding Without Accurate Representation.Collin C. Rice - 2016 - Biology and Philosophy 31 (1):81-102.
    This paper analyzes two ways idealized biological models produce factive scientific understanding. I then argue that models can provide factive scientific understanding of a phenomenon without providing an accurate representation of the features of their real-world target system. My analysis of these cases also suggests that the debate over scientific realism needs to investigate the factive scientific understanding produced by scientists’ use of idealized models rather than the accuracy of scientific models themselves.
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  • Computer Simulations in Science and Engineering. Concept, Practices, Perspectives.Juan Manuel Durán - 2018 - Springer.
  • Are Model Organisms Theoretical Models?Veli-Pekka Parkkinen - 2017 - Disputatio 9 (47):471-498.
    This article compares the epistemic roles of theoretical models and model organisms in science, and specifically the role of non-human animal models in biomedicine. Much of the previous literature on this topic shares an assumption that animal models and theoretical models have a broadly similar epistemic role—that of indirect representation of a target through the study of a surrogate system. Recently, Levy and Currie have argued that model organism research and theoretical modelling differ in the justification of model-to-target inferences, such (...)
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  • Causal Concepts Guiding Model Specification in Systems Biology.Dana Matthiessen - 2017 - Disputatio 9 (47):499-527.
    In this paper I analyze the process by which modelers in systems biology arrive at an adequate representation of the biological structures thought to underlie data gathered from high-throughput experiments. Contrary to views that causal claims and explanations are rare in systems biology, I argue that in many studies of gene regulatory networks modelers aim at a representation of causal structure. In addressing modeling challenges, they draw on assumptions informed by theory and pragmatic considerations in a manner that is guided (...)
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  • What is the Problem with Model-Based Explanation in Economics?Caterina Marchionni - 2017 - Disputatio 9 (47):603-630.
    The question of whether the idealized models of theoretical economics are explanatory has been the subject of intense philosophical debate. It is sometimes presupposed that either a model provides the actual explanation or it does not provide an explanation at all. Yet, two sets of issues are relevant to the evaluation of model-based explanation: what conditions should a model satisfy in order to count as explanatory and does the model satisfy those conditions. My aim in this paper is to unpack (...)
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  • When Are Purely Predictive Models Best?Robert Northcott - 2017 - Disputatio 9 (47):631-656.
    Can purely predictive models be useful in investigating causal systems? I argue ‘yes’. Moreover, in many cases not only are they useful, they are essential. The alternative is to stick to models or mechanisms drawn from well-understood theory. But a necessary condition for explanation is empirical success, and in many cases in social and field sciences such success can only be achieved by purely predictive models, not by ones drawn from theory. Alas, the attempt to use theory to achieve explanation (...)
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  • Explicating Objectual Understanding: Taking Degrees Seriously.Christoph Baumberger - 2019 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 1:1-22.
    The paper argues that an account of understanding should take the form of a Carnapian explication and acknowledge that understanding comes in degrees. An explication of objectual understanding is defended, which helps to make sense of the cognitive achievements and goals of science. The explication combines a necessary condition with three evaluative dimensions: An epistemic agent understands a subject matter by means of a theory only if the agent commits herself sufficiently to the theory of the subject matter, and to (...)
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  • Social intelligence: How to integrate research? A mechanistic perspective.Marcin Miłkowski - 2019 - AI and Society 34 (4):735-744.
    Is there a field of social intelligence? Many various disciplines approach the subject and it may only seem natural to suppose that different fields of study aim at explaining different phenomena; in other words, there is no special field of study of social intelligence. In this paper, I argue for an opposite claim. Namely, there is a way to integrate research on social intelligence, as long as one accepts the mechanistic account to explanation. Mechanistic integration of different explanations, however, comes (...)
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  • 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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  • Abstract Versus Causal Explanations?Reutlinger Alexander & Andersen Holly - 2016 - International Studies in the Philosophy of Science 30 (2):129-146.
    In the recent literature on causal and non-causal scientific explanations, there is an intuitive assumption according to which an explanation is non-causal by virtue of being abstract. In this context, to be ‘abstract’ means that the explanans in question leaves out many or almost all causal microphysical details of the target system. After motivating this assumption, we argue that the abstractness assumption, in placing the abstract and the causal character of an explanation in tension, is misguided in ways that are (...)
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  • How Could Models Possibly Provide How-Possibly Explanations?Philippe Verreault-Julien - 2019 - Studies in History and Philosophy of Science Part A 73:1-12.
    One puzzle concerning highly idealized models is whether they explain. Some suggest they provide so-called ‘how-possibly explanations’. However, this raises an important question about the nature of how-possibly explanations, namely what distinguishes them from ‘normal’, or how-actually, explanations? I provide an account of how-possibly explanations that clarifies their nature in the context of solving the puzzle of model-based explanation. I argue that the modal notions of actuality and possibility provide the relevant dividing lines between how-possibly and how-actually explanations. Whereas how-possibly (...)
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  • The Content of Model-Based Information.Raphael van Riel - 2015 - Synthese 192 (12):3839-3858.
    The paper offers an account of the structure of information provided by models that relevantly deviate from reality. It is argued that accounts of scientific modeling according to which a model’s epistemic and pragmatic relevance stems from the alleged fact that models give access to possibilities fail. First, it seems that there are models that do not give access to possibilities, for what they describe is impossible. Secondly, it appears that having access to a possibility is epistemically and pragmatically idle. (...)
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  • How Are Models and Explanations Related?Yasha Rohwer & Collin Rice - 2016 - Erkenntnis 81 (5):1127-1148.
    Within the modeling literature, there is often an implicit assumption about the relationship between a given model and a scientific explanation. The goal of this article is to provide a unified framework with which to analyze the myriad relationships between a model and an explanation. Our framework distinguishes two fundamental kinds of relationships. The first is metaphysical, where the model is identified as an explanation or as a partial explanation. The second is epistemological, where the model produces understanding that is (...)
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  • Models Don’T Decompose That Way: A Holistic View of Idealized Models.Collin Rice - 2019 - British Journal for the Philosophy of Science 70 (1):179-208.
    Many accounts of scientific modelling assume that models can be decomposed into the contributions made by their accurate and inaccurate parts. These accounts then argue that the inaccurate parts of the model can be justified by distorting only what is irrelevant. In this paper, I argue that this decompositional strategy requires three assumptions that are not typically met by our best scientific models. In response, I propose an alternative view in which idealized models are characterized as holistically distorted representations that (...)
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  • Idealization.Alkistis Elliott-Graves & Michael Weisberg - 2014 - Philosophy Compass 9 (3):176-185.
    This article reviews the recent literature on idealization, specifically idealization in the course of scientific modeling. We argue that idealization is not a unified concept and that there are three different types of idealization: Galilean, minimalist, and multiple models, each with its own justification. We explore the extent to which idealization is a permanent feature of scientific representation and discuss its implications for debates about scientific realism.
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  • Minimal Model Explanations.Robert W. Batterman & Collin C. Rice - 2014 - Philosophy of Science 81 (3):349-376.
    This article discusses minimal model explanations, which we argue are distinct from various causal, mechanical, difference-making, and so on, strategies prominent in the philosophical literature. We contend that what accounts for the explanatory power of these models is not that they have certain features in common with real systems. Rather, the models are explanatory because of a story about why a class of systems will all display the same large-scale behavior because the details that distinguish them are irrelevant. This story (...)
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  • From Implausible Artificial Neurons to Idealized Cognitive Models: Rebooting Philosophy of Artificial Intelligence.Catherine Stinson - forthcoming - Philosophy of Science.
    There is a vast literature within philosophy of mind that focuses on artificial intelligence, but hardly mentions methodological questions. There is also a growing body of work in philosophy of science about modeling methodology that hardly mentions examples from cognitive science. Here these discussions are connected. Insights developed in the philosophy of science literature about the importance of idealization provide a way of understanding the neural implausibility of connectionist networks. Insights from neurocognitive science illuminate how relevant similarities between models and (...)
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  • Model Organisms Are Not (Theoretical) Models.Arnon Levy & Adrian Currie - 2015 - British Journal for the Philosophy of Science 66 (2):327-348.
    Many biological investigations are organized around a small group of species, often referred to as ‘model organisms’, such as the fruit fly Drosophila melanogaster. The terms ‘model’ and ‘modelling’ also occur in biology in association with mathematical and mechanistic theorizing, as in the Lotka–Volterra model of predator-prey dynamics. What is the relation between theoretical models and model organisms? Are these models in the same sense? We offer an account on which the two practices are shown to have different epistemic characters. (...)
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  • Models on the Move: Migration and Imperialism.Seamus Bradley & Karim P. Y. Thébault - 2019 - Studies in History and Philosophy of Science Part A 77:81-92.
    We introduce ‘model migration’ as a species of cross-disciplinary knowledge transfer whereby the representational function of a model is radically changed to allow application to a new disciplinary context. Controversies and confusions that often derive from this phenomenon will be illustrated in the context of econophysics and phylogeographic linguistics. Migration can be usefully contrasted with concept of ‘imperialism’, that has been influentially discussed in the context of geographical economics. In particular, imperialism, unlike migration, relies upon extension of the original model (...)
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  • Modeling Practical Thinking.Matthew Mosdell - 2019 - 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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  • Why Experiments Matter.Adrian Currie & Arnon Levy - 2018 - Inquiry: An Interdisciplinary Journal of Philosophy (9-10):1-25.
    Traditionally, experimentation is considered a privileged means of confirmation. However, how experiments are a better confirmatory source than other strategies is unclear, and recent discussions have identified experiments with various modeling strategies on the one hand, and with ‘natural’ experiments on the other hand. We argue that experiments aiming to test theories are best understood as controlled investigations of specimens. ‘Control’ involves repeated, fine-grained causal manipulation of focal properties. This capacity generates rich knowledge of the object investigated. ‘Specimenhood’ involves possessing (...)
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  • Scientific Representation is Representation-As.Frigg Roman & Nguyen James - 2017 - In Hsiang-Ke Chao & Julian Reiss (eds.), Philosophy of Science in Practice: Nancy Cartwright and the nature of scientific reasoning. Switzerland: Springer International Publishing. pp. 149-179.
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  • In Search of Lost Deterrence – Two Essays on Deterrence and the Models Employed to Study the Phenomenon.Karl Sörenson - unknown
    To deter is central for strategic thinking. Some of the more astute observations regarding the dynamics of deterrence were made during the Cold War by game theorists. This set the stage for how deterrence has come to be studied. A strong methodological element like the research on deterrence’s reliance on game theory requires examination in order to understand what sort of knowledge it actually yields. What sort of knowledge does one acquire when deterrence is viewed through game theoretic models? How (...)
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  • Phonetic Segments and the Organization of Speech.Luca Gasparri - 2019 - Philosophy of Science 86 (2):304-324.
    According to mainstream linguistic phonetics, speech can be modeled as a string of discrete sound segments or “phones” drawn from a universal phonetic inventory. Recent work has argued that a mature phonetics should refrain from theorizing about speech and speech processing using sound segments, and that the phone concept should be eliminated from linguistic theory. The paper lays out the tenets of the phone methodology and evaluates its prospects in light of the eliminativist arguments. I claim that the eliminativist arguments (...)
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  • Depiction, Pictorial Experience, and Vision Science.Robert Briscoe - 2016 - Philosophical Topics 44 (2):43-81.
    Pictures are 2D surfaces designed to elicit 3D-scene-representing experiences from their viewers. In this essay, I argue that philosophers have tended to underestimate the relevance of research in vision science to understanding the nature of pictorial experience. Both the deeply entrenched methodology of virtual psychophysics as well as empirical studies of pictorial space perception provide compelling support for the view that pictorial experience and seeing face-to-face are experiences of the same psychological, explanatory kind. I also show that an empirically informed (...)
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  • Big Data and Changing Concepts of the Human.Carrie Figdor - 2019 - European Review 27 (3):328-340.
    Big Data has the potential to enable unprecedentedly rigorous quantitative modeling of complex human social relationships and social structures. When such models are extended to nonhuman domains, they can undermine anthropocentric assumptions about the extent to which these relationships and structures are specifically human. Discoveries of relevant commonalities with nonhumans may not make us less human, but they promise to challenge fundamental views of what it is to be human.
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  • Inequality and Inequity in the Emergence of Conventions.Calvin Cochran & Cailin O’Connor - 2019 - Politics, Philosophy and Economics 18 (3):264-281.
    Many societies have norms of equity – that those who make symmetric social contributions deserve symmetric rewards. Despite this, there are widespread patterns of social inequity, especially along gender and racial lines. It is often the case that members of certain social groups receive greater rewards per contribution than others. In this article, we draw on evolutionary game theory to show that the emergence of this sort of convention is far from surprising. In simple cultural evolutionary models, inequity is much (...)
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  • How-Possibly Explanation in Biology: Lessons From Wilhelm His’s ‘Simple Experiments’ Models.Christopher Pearson - 2018 - Philosophy, Theory, and Practice in Biology 10 (4).
    A common view of how-possibly explanations in biology treats them as explanatorily incomplete. In addition to this interpretation of how-possibly explanation, I argue that there is another interpretation, one which features what I term “explanatory strategies.” This strategy-centered interpretation of how-possibly explanation centers on there being a different explanatory context within which how-possibly explanations are offered. I contend that, in conditions where this strategy context is recognized, how-possibly explanations can be understood as complete explanations. I defend this alternative interpretation by (...)
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  • Model-Based Theorizing in Cognitive Neuroscience.Elizabeth Irvine - 2016 - British Journal for the Philosophy of Science 67 (1):143-168.
    Weisberg and Godfrey-Smith distinguish between two forms of theorising: data-driven ‘abstract direct representation’ and modeling. The key difference is that when using a data-driven approach, theories are intended to represent specific phenomena, so directly represent them, while models may not be intended to represent anything, so represent targets indirectly, if at all. The aim here is to compare and analyse these practices, in order to outline an account of model-based theorising that involves direct representational relationships. This is based on the (...)
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  • 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 for a physically (...)
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  • Why Build a Virtual Brain? Large-Scale Neural Simulations as Jump Start for Cognitive Computing.Matteo Colombo - 2016 - Journal of Experimental and Theoretical Artificial Intelligence.
    Despite the impressive amount of financial resources recently invested in carrying out large-scale brain simulations, it is controversial what the pay-offs are of pursuing this project. One idea is that from designing, building, and running a large-scale neural simulation, scientists acquire knowledge about the computational performance of the simulating system, rather than about the neurobiological system represented in the simulation. It has been claimed that this knowledge may usher in a new era of neuromorphic, cognitive computing systems. This study elucidates (...)
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  • The Nature of Model-World Comparisons.Fiora Salis - 2016 - The Monist 99 (3):243-259.
    Upholders of fictionalism about scientific models have not yet successfully explained how scientists can learn about the real world by making comparisons between models and the real phenomena they stand for. In this paper I develop an account of model-world comparisons in terms of what I take to be the best antirealist analyses of comparative claims that emerge from the current debate on fiction.
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  • Imagination in Scientific Modeling.Adam Toon - 2016 - In Amy Kind (ed.), The Routledge Handbook of Philosophy of Imagination. 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 parallels (...)
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  • Thought Experiments in Biology.Guillaume Schlaepfer & Marcel Weber - 2018 - In Michael T. Stuart, Yiftach J. H. Fehige & James Robert Brown (eds.), The Routledge Companion to Thought Experiments. London: Routledge. pp. 243-256.
    Unlike in physics, the category of thought experiment is not very common in biology. At least there are no classic examples that are as important and as well-known as the most famous thought experiments in physics, such as Galileo’s, Maxwell’s or Einstein’s. The reasons for this are far from obvious; maybe it has to do with the fact that modern biology for the most part sees itself as a thoroughly empirical discipline that engages either in real natural history or in (...)
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