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Scientific Modeling Without Representationalism

Sanches De Oliveira, Guilherme
http://orcid.org/0000-0002-3509-9886
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1573571043132428

Abstract Details

2019, PhD, University of Cincinnati, Arts and Sciences: Philosophy.
Scientists often gain insight into real-world phenomena indirectly, through building and manipulating models. But what accounts for the epistemic import of model-based research? Why can scientists learn about real-world systems (such as the global climate or biological populations) by interacting not with the real-world systems themselves, but with computer simulations and mathematical equations? The traditional answer is that models teach us about certain real-world phenomena because they represent those phenomena. My dissertation challenges this representationalist intuition and provides an alternative framework for making sense of scientific modeling. The philosophical debate about scientific model-based representation has, by and large, proceeded in isolation from the debate about mental representation in philosophy of mind and cognitive science. Chapter one exposes and challenges this anti-psychologism. Drawing from "wide computationalist" embodied cognitive science research, I put forward an account of scientific models as socially-distributed and materially-extended mental representations. This account illustrates how views on mental representation can help advance philosophical understanding of scientific representation, while raising the question of how other views from (embodied) cognitive science might inform philosophical theorizing about scientific modeling. Chapter two argues that representationalism is untenable because it relies on ontological and epistemological assumptions that undermine one another no matter the theory of representation adopted. Views of scientific representation as mind-independent fail with the ontological claim that "models represent their targets" and thereby undermine the epistemological claim that "we learn from models because they represent their targets." On the other hand, views of scientific representation as mind-dependent support the ontological claim, but they do so in a way that also undermines the epistemological claim: if "representation" entails only use rather than success or accuracy, then the epistemic value of modeling cannot be explained purely in representational terms. Chapter three focuses on emerging "artifactualist" views of models as tools, artifacts, and instruments. The artifactualism of current accounts is a conciliatory view that is compatible with representationalism and merely promotes a shift in emphasis in theorizing. I argue against this version of artifactualism (which I call "weak artifactualism"), and I put forward an alternative formulation free from representationalism ("strong artifactualism"). Strong artifactualism is not only desirable, but it's also viable and promising as an approach to making sense of how we learn through modeling. Chapter four draws from ecological psychology to offer an empirically-informed account of modeling as a tool-building practice. I propose that the epistemic worth of modeling is best understood in terms of the "affordances" that the practice gives rise to for suitably-positioned embodied cognitive agents. This account develops a strong artifactualist view of models (chapter three) and it circumvents the challenges inherent to representationalism (chapter two) because it anchors the epistemic worth of modeling in the models' affordances, which are agent-relative but mind-independent. Moreover, this account provides an additional reason to give up anti-psychologism in philosophy: not only can views on mental representation help us better understand scientific representation (chapter one), but anti-representational views in psychology can also inform a nonrepresentational understanding of how and why modeling works.
Angela Potochnik, Ph.D. (Committee Chair)
Anthony Chemero, Ph.D. (Committee Member)
Thomas Polger, Ph.D. (Committee Member)
Michael Richardson, Ph.D. (Committee Member)
176 p.
Philosophy of Science
Representation; Scientific modeling; Simulation; Embodied Cognition; Ecological Psychology; Affordances

Recommended Citations

Citations

  • Sanches De Oliveira, G. (2019). Scientific Modeling Without Representationalism [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1573571043132428

    APA Style (7th edition)

  • Sanches De Oliveira, Guilherme. Scientific Modeling Without Representationalism. 2019. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1573571043132428.

    MLA Style (8th edition)

  • Sanches De Oliveira, Guilherme. "Scientific Modeling Without Representationalism." Doctoral dissertation, University of Cincinnati, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1573571043132428

    Chicago Manual of Style (17th edition)

ucin1573571043132428
425
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This open access ETD is published by University of Cincinnati and OhioLINK.