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Scientific Modeling Without Representationalism

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Scientific Modeling Without Representationalism Scientific Modeling Without Representationalism A dissertation submitted to the Graduate School of the University of Cincinnati in partial fulfillment of the requirements for the degree of Doctor of Philosophy (PhD) in the Department of Philosophy of the College of Arts and Sciences by Guilherme SANCHES DE OLIVEIRA M.A. University of São Paulo, 2014 M.A. University of Cincinnati, 2017 Commitee Chair: Angela POTOCHNIK, Ph.D. August 23, 2019 i Abstract 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 philos- ophy of mind and cognitive science. Chapter one exposes and challenges this anti- psychologism. Drawing from ‘wide computationalist’ embodied cognitive science re- search, I put forward an account of scientific models as socially-distributed and materially- extended mental representations. This account illustrates how views on mental rep- resentation 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 on- tological 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 un- dermine the epistemological claim that ‘we learn from models because they represent ii 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 epis- temological 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, arti- facts, 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 artifac- tualism”), 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 ar- tifactualist 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 philoso- phy: 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. iii c 2019 Guilherme Sanches de Oliveira All rights reserved iv Acknowledgements In the sciences and in philosophy of science, we use the term “model” to refer to a computational, mathematical or concrete object that’s designed and used to facilitate learning about some other object, typically a system or phenomenon in the real world that’s the ultimate “target” of investigation: in this technical sense, philosophers often think of models as imitations or copies of that target. But this is exactly the opposite of how we use the term in ordinary contexts: in real life, a “model” is the thing to be imitated, not the thing that’s imitating something else. It is in this ordinary sense of the term that I want to acknowledge and thank my models—the people I have been working hard to learn from, whose virtues I will continue trying to emulate. My committee chair, Angela Potochnik, is my model of intellectual insight and gen- erosity. Angela was the first person to get excited about my dissertation, and I mean that literally: she was excited about my dissertation even before I knew what it was going to be about. She saw potential in the very first paper I wrote in my very first semester at UC, and she encouraged me to keep developing those still rudimentary ideas. Over the course of five years, she was always open to reading my nonsense and giving it the benefit of the doubt. Angela’s generosity was, of course, evident in the feedback she would give me, which was always timely, rigorous, and abundant. But I want to emphasize her generosity in supporting my professional development: the countless letters of recommendation (for grants, awards, fellowships, workshops, sum- mer schools, jobs, you name it); the opportunities to learn new skills (including LATEX), v to get involved in cool initiatives (PhilPapers, the Center for Public Engagement with Science), and to collaborate in research (co-authored paper forthcoming!); and all the advice, which I’m slowly coming to understand (e.g., learning when and what to say “no” to). For all of this and so much more, thank you! I’m lucky to have found a mentor in Tony Chemero, my interdisciplinary hero, the person who not only told me but also showed me that it’s possible to do good phi- losophy and good science all at once. Being around Tony made me realize that pretty much all of my clever ideas had already been had by pragmatists and phenomenolo- gists, and that turning to them could help dissolve my philosophical problems. Tony also helped me see the light and embrace the teachings of St. Jimmy (while always keeping a healthy dose of scientific skepticism!), and for this I will be forever grate- ful. Even though he’s a rockstar (or karate master?), Tony is always ready to share the spotlight with his students, and that speaks volumes about his character. Completing my committee, Tom Polger and Mike Richardson deserve recognition for their continued support. Tom gave me feedback on the first version of the paper that would become the basis for my dissertation and also on the first related conference presentation at the SSPP meeting in New Orleans; his (hard!) questions, since back then, have helped me avoid going down quite a few rabbit holes. Mike’s insight and encouragement, coming from the perspective of a modeler and ecological psychologist, made me believe in my project’s potential beyond disciplinary confines. My time at UC would not have been as productive and stimulating if it weren’t for all the amazing people I got to work with here. For their guidance, I thank the faculty in philosophy (especially Zvi Biener, John McEvoy, Jenefer Robinson, and Rob Skip- per), in psychology (especially Paula Silva, Kevin Shockley, Mike Riley, and Tehran vi Davis), and in the Graduate School (Steve Howe, David Stradling, and Mike Riley again). For their support and company, I thank good friends and colleagues in philos- ophy (especially Frank Faries, Valentina Petrolini, Walter Stepanenko, Sahar Heydari Fard, Mohan Pillai, and Jonathan McKinney), in psychology (especially Chris Riehm, Patric Nordbeck, Colin Annand, Francis Grover, Ed Baggs, and Patrick Nalepka), and, in both literal and metaphorical walks back and forth between McMicken and Ed- wards, my buddy Vicente Raja. Vesna Kocani deserves special thanks for her work behind the scenes, keeping everything running smoothly. I am indebted to Osvaldo Pessoa Jr. and to Caetano Plastino (University of São Paulo) for introducing me to philosophy of science, philosophy of mind and cognitive science. I am also indebted to Otávio Bueno (University of Miami) and Nancy Ners- essian (Georgia Tech) for their advice as I tried to figure out how to continue studying philosophy at the graduate level. Working on and completing a Ph.D. takes a lot of time and energy. I wouldn’t have been able to pour so much of myself into this project if it weren’t for my mom, my ultimate model of perseverance and unwavering optimism. My mom lost her parents at fifteen, and had to drop out of high school to work full time; years later, a failed marriage and a divorce made it so that she had to raise my sister and me all by her- self. Despite having gone through so many challenges in life (or, somehow, because of them), she has this resolutely positive outlook: if you work hard enough, things may still not turn out the way you wanted, but you’ll be okay. I’m not sure I totally buy that, but I am grateful that my mom does and that she worked hard so that I could have the educational opportunities she didn’t; her grit continues to be a source of inspiration. I dedicate this accomplishment to Maggie, who—along with Grafite, Friendly & vii Bijoux, Pulla & Snuffkin, all the chickens, and the occasional groundhog, fox and coy- ote—ensured that life, even during the PhD, was full and whole.
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