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Thesis Format



Doctor of Philosophy




Brennan, Samantha J.


University of Guelph


The central question for this dissertation is, how do we do moral philosophy well from within a broadly naturalist framework? Its main goal is to lay the groundwork for a methodological approach to moral philosophy that integrates traditional, intuition-driven approaches to ethics with empirical approaches that employ empirical data from biology and cognitive science. Specifically, it explores what restrictions are placed on our moral theorizing by findings in evolutionary biology, psychology, neuroscience, and other fields, and how we can integrate this data while still offering a fully normative account of ethics. To that end, the dissertation explores the methodological assumptions behind both traditional and more empirical or experimental approaches to ethics, to find where these assumptions cannot be properly supported and need to be re-examined. Chapter 1 explores the science-based objections that have been raised to traditional approaches, with a particular focus on questions concerning the reliability of moral intuitions. Chapter 2 examines three fundamental assumptions supporting the empirical approach to moral philosophy, and how those assumptions ultimately do not fit with how we ought to understand the project of ethics. In chapter 3, I discuss the fact/value distinction, and the restrictions that are placed on our moral theorizing by our commitment to this distinction. Chapter 4 offers a defense of ‘companions in guilt’ arguments, and uses these arguments to draw an analogy between moral philosophy and epistemology that will be used to help defend moral philosophy against empirical debunking arguments. Chapter 5 explores the ways in which epistemologists’ methods of incorporating empirical data into their research while maintaining the normativity of their accounts can be adapted to allow moral philosophers to do the same, and brings together the various methodological concerns addressed up to this point to lay out a methodological approach I call empirically-informed moral philosophy.

Summary for Lay Audience

Moral philosophers have long been resistant to using scientific data in their philosophical work, owing to a commitment to the idea that descriptions of the world (claims that tell us what is the case) cannot tell us anything about what ought to be the case, or what we ought to do, which is what moral philosophers are concerned with. Recently, however, some philosophers have taken note of research from psychologists, neuroscientists, biologists, and others that seem to have implications for our understanding of ethics. Some of these philosophers have even begun to conduct their own experiments to learn more about how people actually think about ethics and what is going on in the brain when people think about moral questions. This has led to two approaches to ethics: what I call the “traditional approach,” which continues to rely on pure intuition and reasoning, and what I call the “empirical approach,” which relies heavily on scientific data. In this thesis, I look at these two approaches, and ask what each of them must think about how we should understand ethics to justify their approach. I argue that neither approach can give a fully satisfactory account of ethics, as both rely on background assumptions that cannot be entirely justified. I then consider general restrictions on how we approach moral philosophy, and how this can help to deal with these issues. I look at how other areas of philosophy have contended with similar challenges, and ways in which their solutions can be used to address the problematic background assumptions of both the traditional and empirical approaches. Applying these lessons, I present my approach, striking a balance between the two existing approaches, which I call the “empirically-informed” approach. This approach makes use of scientific data, but does not rely on it as heavily as the purely empirical approach, and so does not face the same issues as either other method.

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Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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