In this paper, I assess recent claims in philosophy of science about scientific perspectivism being compatible with realism. I clarify the rationale for scientific perspectivism and the problems and challenges that perspectivism faces in delivering a form of realism. In particular, I concentrate my attention on truth, and on ways in which truth can be understood in perspectival terms. I offer a cost‐benefit analysis of each of them and defend a version that in my view is most promising in living up to realist expectations.
The goal of this article is to address the problem of inconsistent models, and the challenge it poses for perspectivism. I analyse 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 heuristic. I illustrate perspectival modeling with two examples taken from contemporary high-energy physics at LHC, CERN, which are designed to show how a plurality of seemingly incompatible models (suitably understood) is methodologically crucial to advance the realist quest in cutting-edge areas of scientific inquiry.
What does it mean to be a realist about science if one takes seriously the view that scientific knowledge is always perspectival, namely historically and culturally situated? In Perspectival Realism, Michela Massimi articulates an original answer to this question. The result is a philosophical view that goes under the name of ‘perspectival realism’ and it offers a new lens for thinking about scientific knowledge, realism, and pluralism in science. Perspectival Realism begins with an exploration of how epistemic communities often resort to several models and a plurality of practices in some areas of inquiry, drawing on examples from nuclear physics, climate science, and developmental psychology. Taking this plurality in science as a starting point, Massimi explains the perspectival nature of scientific representation, the role of scientific models as inferential blueprints, and the variety of realism that naturally accompanies such a view. Perspectival realism is realism about phenomena (rather than about theories or unobservable entities). The result of this novel view is a portrait of scientific knowledge as a collaborative inquiry, where the reliability of science is made possible by a plurality of historically and culturally situated scientific perspectives. Along the way, Massimi offers insights into the nature of scientific modelling, scientific knowledge qua modal knowledge, data-to-phenomena inferences, and natural kinds as sortal concepts. Perspectival realism offers a realist view that takes the multicultural roots of science seriously and couples it with cosmopolitan duties about how one ought to think about scientific knowledge and the distribution of benefits gained from scientific advancements.
I analyse the exploratory function of two main modelling practices: targetless fictional models and hypothetical perspectival models. In both cases, I argue, modelers invite us to imagine or conceive something about the target system, which is either known to be non-existent (fictional models) or just hypothetical (in perspectival models). I clarify the kind of imagining or conceiving involved in each modelling practice, and I show how each-in its own right-delivers important modal knowledge. I illustrate these two kinds of exploratory models with Maxwell's ether model and SUSY models at the LHC. ß ß experimental evidence e1 (e.g. Higgs mass, electroweak measurements) ß how could x1,…xz -1 be possible, were y1,…yn -1 conceivedLB to be the case? ß ß experimental evidence e2 (e.g. 22 Run 1 searches at ATLAS) ß how could x1,…xz -2 be possible, were y1,…yn -2 conceivedLB to be the case? ß ß experimental evidence e3 (e.g. Run 2 searches at ATLAS) ß how could x1,…xz -3 be possible, were y1,…yn -3 conceivedLB to be the case?To illustrate the bootstrap at work in these how-possible inferences, let us go back to our example. The ATLAS Collaboration (2015) used the pMSSM-19, with minimal nomological constraints (R-parity exactly conserved, among others). The resulting 500 million model points 10 To simplify the matter, in the scheme above I assumed that only one physically conceivableLB state is ruled out in the antecedent at any round of new experimental evidence. But in practice, obviously, very many physically conceivableLB scenarios are ruled out at any one time.
In this paper I argue-against van Fraassen's constructive empiricism-that the practice of saving phenomena is much broader than usually thought, and includes unobservable phenomena as well as observable ones. My argument turns on the distinction between data and phenomena: I discuss how unobservable phenomena manifest themselves in data models and how theoretical models able to save them are chosen. I present a paradigmatic case study taken from the history of particle physics to illustrate my argument. The first aim of this paper is to draw attention to the experimental practice of saving unobservable phenomena, which philosophers have overlooked for too long. The second aim is to explore some far-reaching implications this practice may have for the debate on scientific realism and constructive empiricism. 1 Introduction 2 Unobservable Phenomena 2.1 Data and phenomena 2.2 What is a data model? 2.3 Data models and unobservable phenomena 3 Saving Unobservable Phenomena: An Exemplar 4 The October Revolution of 1974: From the J /ψ to Charmonium 4.1 A new unobservable phenomenon at 3.1 GeV 4.2 How the charmonium model saved the new unobservable phenomenon 4.2.1 The J /ψ as a baryon-antibaryon bound state 4.2.2 The J /ψ as the spin-1 meson of a model with three charmed quarks 4.2.3 The J /ψ as a charmonium state 5 Concluding Remarks
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