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Basso, Alessandra, Lisciandra, Chiara, Marchionni, Caterina. Hypothetical models in social science: their features and uses
2017, Springer Handbook of Model-Based Science. Magnani, L. & Bertolotti, T. (eds.). Springer, 413-433
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Added by: Björn Freter, Contributed by:

Abstract: The chapter addresses the philosophical issues raised by the use of hypothetical modeling in the social sciences. Hypothetical modeling involves the construction and analysis of simple hypothetical systems to represent complex social phenomena for the purpose of understanding those social phenomena.

To highlight its main features hypothetical modeling is compared both to laboratory experimentation and to computer simulation. In analogy with laboratory experiments, hypothetical models can be conceived of as scientific representations that attempt to isolate, theoretically, the working of causal mechanisms or capacities from disturbing factors. However, unlike experiments, hypothetical models need to deal with the epistemic uncertainty due to the inevitable presence of unrealistic assumptions introduced for purposes of analytical tractability. Computer simulations have been claimed to be able to overcome some of the strictures of analytical tractability. Still they differ from hypothetical models in how they derive conclusions and in the kind of understanding they provide.

The inevitable presence of unrealistic assumptions makes the legitimacy of the use of hypothetical modeling to learn about the world a particularly pressing problem in the social sciences. A review of the contemporary philosophical debate shows that there is still little agreement on what social scientific models are and what they are for. This suggests that there might not be a single answer to the question of what is the epistemic value of hypothetical models in the social sciences.

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Chakravartty, Anjan, , . Realist Representations of Particles: The Standard Model, Top-Down and Bottom-Up
2019, In Contemporary Scientific Realism and the Challenge from the History of Science
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Added by: Simon Fokt, Contributed by: Matthew Watts

Introduction: Much debate about scientific realism concerns the issue of whether it is compatible with theory change over time. Certain forms of ‘selective realism’ have been suggested with this in mind. Here I consider a closely related challenge for realism: that of articulating how a theory should be interpreted at any given time. In a crucial respect the challenges posed by diachronic and synchronic interpretation are the same; in both cases, realists face an apparent dilemma. The thinner their interpretations, the easier realism is to defend, but at the cost of more substantial commitment. The more substantial their interpretations, the more difficult they are to defend. I consider this worry in the context of the Standard Model of particle physics.

Comment: This text presents challenges to scientific realism, and shows how these challenges can be mitigated.

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Leonelli, Sabina, , . What distinguishes data from models?
2019, European Journal for Philosophy of Science 9 (2):22.
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Added by: Sara Peppe, Contributed by:

Abstract: 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 argue that whether a set of objects functions as data or models does not depend on intrinsic differences in their physical properties, level of abstraction or the degree of human intervention involved in generating them, but rather on their distinctive roles towards identifying and characterizing the targets of investigation. The paper thus proposes a characterization of data models that builds on Suppes’ attention to data practices, without however needing to posit a fixed hierarchy of data and models or a highly exclusionary definition of data models as statistical constructs.

Comment: This article deepens the role of model an data in the scientific investigation taking into account the scientific practice. Obviously, a general framework of the themes the author takes into account is needed.

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Potochnik, Angela, , . Feminist implications of model-based science
2012, Studies in History and Philosophy of Science Part A 43 (2):383-389.
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Added by: Clotilde Torregrossa, Contributed by: Simon Fokt

Abstract: Recent philosophy of science has witnessed a shift in focus, in that significantly more consideration is given to how scientists employ models. Attending to the role of models in scientific practice leads to new questions about the representational roles of models, the purpose of idealizations, why multiple models are used for the same phenomenon, and many more besides. In this paper, I suggest that these themes resonate with central topics in feminist epistemology, in particular prominent versions of feminist empiricism, and that model-based science and feminist epistemology each has crucial resources to offer the other’s project.

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