Research
You can download a PDF of my CV here.
My research program spans both epistemological and metaphysical questions within the Philosophy of Science, the Philosophy of Biology, and the Philosophy of Mind. Specifically, my research focuses on the nature of scientific explanation and understanding, the use of idealization and modeling in scientific theorizing, and the ways idealizations make positive contributions to scientific explanation and understanding. Much of my research focuses on examples from biology—especially adaptationist and statistical explanations that employ highly idealized models. However, I aim to use these examples from biological modeling to draw broader lessons about the epistemology and metaphysics of scientific modeling and explanation more generally. I also aim to identify similarities (and differences) in the ways these concepts are used across different sciences; e.g., I have investigated how the use of highly idealized models to explain in biology compares with their use in physics, economics, and climate science. I have also done some work on the nature of mental concepts and cognitive architecture, which I am currently combining with my work on the concepts of explanation and understanding.
At the core of my research program I have published several papers that engage with the following puzzling question: if scientific explanations and understanding are supposed to provide true accounts of the world, then how can the drastic distortion involved in scientific modeling contribute to science’s epistemic aims? I have attempted to address this question by developing accounts of (various kinds of) explanation, idealization, modeling, and understanding. My main focus has been to show how the distortions introduced via idealization and abstraction make positive and ineliminable contributions to science’s attempts to explain and understand our world. As a result, many of our best epistemic tools require us to deliberately misrepresent the relevant features of interest.
My approach to each of these topics is naturalistic and interdisciplinary and I continually look to collaborate with colleagues, scientists, and students who are interested in these questions. I frequently combine contemporary analytic philosophy methods with empirical evidence from science and insights from the history of philosophy and science.
The Nature of Scientific Explanation
The first part of this project has been to identify the various kinds of explanation provided in scientific practice. In particular, given that most philosophical accounts of explanation are explicitly causal, much of my research focuses on developing accounts of various kinds of noncausal explanations. For example, in “Moving Beyond Causes: Optimality Models and Scientific Explanation” I argue that biological optimality explanations succeed despite (1) ignoring difference-making causes, (2) drastically distorting the causal processes that made a difference to the production of the trait, and (3) providing synchronic representations of noncausal constraints and tradeoffs of the overall system. Because these features result in explanations that violate the key features of the most prominent accounts of causal explanation, I contend that our account of explanation must move beyond an exclusively causal approach.
I have also developed noncausal accounts of statistical explanations. For example, in “Autonomous Statistical Explanations and Natural Selection” André Ariew, Yasha Rohwer and I contend that there are at least two distinct theories of natural selection: Darwin’s theory and the genetical theory formulated during the modern synthesis. We then argue that these theories provide very different kinds of explanations (e.g. causal vs. statistical) and that the modern genetical theory provides autonomous statistical explanations that are independent of the underlying causal facts about the population(s) of interest. We develop this account further in “Explanatory Schema and the Process of Model Building” by showing how similar kinds of statistical explanations can be found in physics, biology, and social science.
Robert Batterman and I analyze another kind of noncausal explanation in our paper, “Minimal Model Explanations”. In this paper we investigate the structure of a unique type of explanation provided for universal patterns in physics and biology. In these cases, scientists appeal to extremely minimal models that include only a handful of features and yet are able to display the large-scale patterns of interest. We contend that the key to explaining the stability of these universal patterns is to provide a detailed story about why the features that distinguish various real, possible, and model systems from one another are irrelevant to the universal patterns of interest.
In light of this variety of types of explanation, we need to reassess whether or not a universal account of scientific explanation is possible. In “How to Reconcile a Unified Account of Explanation with Explanatory Diversity” Yasha Rohwer and I argue that none of the features identified by existing accounts of explanation are necessary for all explanations. However, we argue that a unified account can still be provided by reconceiving of scientific explanation as a cluster concept: there are multiple subsets of features that are sufficient for providing an explanation, but no single feature is necessary for all explanations.
The Role of Idealization and Modeling in Scientific Practice
The second part of my primary research project has been the development of alternative accounts of the role of idealization and modeling in scientific practice. For example, in a paper titled “Idealized Models, Holistic Distortions, and Universality” I first argue against various attempts to justify the use of idealizations in models that explain by showing that they only distort irrelevant features. In particular, I show how their role in the foundational mathematical frameworks used in providing the explanation means that the idealizations introduced also distort features that are known to be relevant to the occurrence of the phenomenon. I argue for this claim by providing a detailed analysis of three cases from physics, biology, and climate science in which the models directly distort difference-making (i.e. relevant) causes of their target system(s). I then use these cases to motivate my alternative holistic distortion view. According to this view, models ought to be characterized as pervasively distorting both relevant and irrelevant features of their target system(s). Their epistemic uses can still be justified, however, because the idealizations are absolutely necessary for applying the mathematical modeling techniques required to extract the explanatory information of interest.
I develop this view further in “Models Don’t Decompose that Way: A Holistic View of Idealized Models” by first showing that across a wide range of philosophical debates concerning explanation, modeling, idealization, robustness, and realism philosophers have mistakenly sought to decompose scientific models into their accurate and inaccurate parts. After showing that this kind of decomposition is impossible for many scientific models (e.g. the ideal gas law and population genetics models), I argue for my alternative account that appeals to universality classes of systems that exhibit stable behaviors despite differences in their components and interactions. Showing that the idealized model and the real-world system(s) of interest are within the same universality classes allows us to connect their universal (i.e. stable) behaviors without requiring the model to provide an accurate representation of the causes or mechanisms that produce those behaviors in the real system(s).
I have also used this ‘universality account’ to address the use of multiple conflicting models to study the same phenomenon and scientists’ frequent interest in modeling limiting behaviors. In “Universality and the Problem of Inconsistent Models” I argue that conflicting sets of idealized models from cell biology can each be used to explain and understand the same phenomenon when they are in different universality classes (or are used to investigate different possible states of the system). Then, in “Universality and Modeling Limiting Behaviors”, I show that scientific modelers themselves commonly appeal to universality classes in order to justify their uses of extremely minimal models to explain and understand complex phenomena that arise only in the large-scale limits of the system.
Finally, I am currently working on a paper titled “Modeling Multiscale Patterns: Active Matter, Minimal Models, and Universality” in which I argue that several cases of investigating patterns across very different scales of the system cannot be captured by accounts focused on causal patterns since the patterns reoccur across systems that are known to be heterogeneous with respect to the causes that produce them. In light of these cases, I argue for an alternative conception of ‘explanatory autonomy’ that focuses on the explanatory affordances and limitations of particular kinds of idealized modeling strategies, rather than of scientific disciplines or levels of nature.
The Nature of Scientific Understanding
Another distinctive aim of scientific practice is to enable us to understand the phenomena we observe. Although developing explanations is one of the primary ways science produces understanding, I have extensively argued that providing explanations is not the only way to produce understanding. For example, in “Hypothetical Pattern Idealization and Explanatory Models”, Yasha Rohwer and I argue that, despite their failure to explain, several highly idealized models in biology and economics produce understanding by answering questions about what is possible and justifying background beliefs.
I have also developed my own account of understanding that shows how understanding can be factive (i.e. involve truth in an essential way) and yet be produced via scientific models that distort the relevant (e.g. difference-making) features of their target system(s). In “Factive Scientific Understanding Without Accurate Representation” I argue that highly idealized models in biology produce factive understanding not by accurately representing the features of the system, but by showing scientists how the system would have behaved differently if various features of the system were changed in the ways described by the idealized model.
Expanding on this view, I recently published a paper titled “Understanding Realism” in which I argue that the fact that our best scientific models and theories are pervasively inaccurate representations can be made compatible with a more nuanced form of scientific realism that I call ‘Understanding Realism’. Contrary to several recent accounts that have argued that idealizations entail that scientific understanding must be nonfactive, I argue that the facticity of scientific understanding can be separated from the inaccuracy of the models and theories used to produce it. This shows how we can be realists about scientific understanding even if we cannot be realists about scientific models and theories.
Finally, Piper Sledge (Bryn Mawr, Sociology) and I have been working on a paper currently titled “Epistemic Achievements, Social Structures, and Diversity in Science”. In this paper, we argue that, rather than focusing on the characteristics of individual researchers, investigations of diversity in science must account for the social structures of scientific inquiry in order to effectively promote the consideration of competing explanations and understandings. We presented this paper at the American Sociological Association meeting and are currently preparing the paper for submission to a philosophy journal.
Book Project
The above views concerning explanation, understanding, and idealization are unified, defended, and expanded upon in much more detail in my recently published book “Leveraging Distortions: Explanation, Idealization, and Universality in Science” (2021 from MIT Press). The main goal of the book is to provide an alternative philosophical approach to science that focuses on the positive and ineliminable contributions made by pervasive distortion of relevant features to the epistemic achievements of science—specifically, explanation and understanding. Throughout the book, I argue that philosophers’ narrow focus on the concepts of causation, accurate representation, and decomposition (into accurate and inaccurate parts) ought to be replaced by an alternative approach based on the concepts of modal information (about possibilities), holistic distortion, and universality. Adopting these alternative views enables us to see that the pervasive distortion of causally and contextually relevant features is, and ought to be, an ineliminable and epistemically fruitful tool of scientific practice. This allows us to develop philosophical accounts that explicitly recognize the essential and ineliminable use of pervasive distortion of relevant features as a virtue rather than an obstacle to the epistemic accomplishments of science.
Concepts and Cognitive Architecture
I also have research interests in the philosophy of mind and cognitive science. For example, in “Massive Modularity, Language and Content Integration” I analyze the role of language in constructing complex thoughts. Specifically, I argue against Peter Carruthers’s claim that the language faculty is responsible for content integration. I then provide empirical and theoretical arguments for thinking that content integration is accomplished without employing language.
The rest of my work in this area has focused on the nature and origin of mental concepts. For example, in “Concept Empiricism, Content, and Compositionality” I critique the recent resurgence of concept empiricism: the idea that all concepts are copies of, or combinations of copies of, perceptual representations. I argue that this view results in different concepts for the same category (e.g. dogs) having very different intentional content. Furthermore, concept empiricism struggles to show how complex concepts can be composed in ways that preserve the systematic patterns of concept combination we observe.
“Concepts as Pluralistic Hybrids” presents my positive view of the nature of mental concepts. I argue that we have multiple concepts for each category (i.e. pluralism) that are constituted by several distinct kinds of information (i.e. they are hybrids) including prototypical information, causal information, and information about exemplars. This goes against the traditional approach of trying to argue for only one of these kinds of information as being central to cognition. I use theoretical philosophical arguments and empirical evidence from cognitive science in order to support my view.
I’ve been continuing this project by working on a paper titled “Explanation as a Pluralistic Hybrid Concept”. In this paper I argue that, in line with my pluralistic hybrid view, we have multiple explanation concepts that are constructed out of different types of conceptual information. For example, we store various exemplars, prototypes, and theories of explanations and use these in combination when formulating our concepts of explanation tailored to particular contexts. This view accounts for why different scientists and philosophers reasonably disagree about whether or not a satisfactory explanation has been provided.