Research Interests

My main research is in the areas of philosophy of science, philosophy of biology, and history of logical empiricism.  Much of my work is inspired by an appreciation for the world's complexity and an interest in how scientific practices are shaped by humans with specific cognitive and practical goals grappling with that complexity. 

I am especially interested in the following interrelated topics:
My book Idealization and the Aims of Science (2017) addresses many of the topics listed above from the vantage point of the centrality of idealization in science. Recipes for Science (2018, 2nd Ed. 2024), coauthored with Matteo Colombo and Cory Wright, is an introductory textbook on scientific methods and reasoning from a philosophical perspective. In my role directing the UC Center for Public Engagement with Science, I am increasingly focused on exploring how philosophy of science can contribute to public engagement with science. This inspired my Element in Philosophy of Science, Science and the Public (2024). 

​See below for lists of my other publications arranged by topic. At the bottom is a list of the book reviews I've done. 

Methodology of Population Biology

What Constitutes an Explanation in Biology?, 2020, K. Kampourakis and T. Uller (Eds.), Philosophy of Science for Biologists, Cambridge University Press. 

One of biology's fundamental aims is to generate understanding of the living world around—and within—us. In this chapter, I aim to provide a relatively nonpartisan discussion of the nature of explanation in biology, grounded in widely shared philosophical views about scientific explanation. But this discussion also reflects what I think is important for philosophers and biologists alike to appreciate about successful scientific explanations, so some points will be controversial, at least among philosophers. I make three main points: (1) causal relationships and broad patterns have often been granted importance to scientific explanations, and they are in fact both important; (2) some explanations in biology cite the components of or processes in systems that account for the systems’ features, whereas other explanations feature large-scale or structural causes that influence a system; and (3) there can be multiple different explanations of a given biological phenomenon, explanations that respond to different research aims and can thus be compatible with one another even when they may seem to disagree. 

Defusing Ideological Defenses in Biology, 2013, BioScience, 63(2): 118–123.

Ideological language is widespread in biology. Game theory has been defended as a worldview; sexual selection theory has been criticized for what it posits as basic to biological nature; and evolutionary developmental biology is advocated as an alternative, not addition, to traditional evolutionary biology.  Views like these encourage the impression of ideological rift.  I advocate an alternative interpretation, whereby many disagreements between camps of biologists reflect unproblematic methodological differences. This interpretation provides a more accurate and more optimistic account of the state of play in the field of biology.  It also helps diagnose why biologists embrace ideological positions.  


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Modeling Social and Evolutionary Games, 2012, Studies in History and Philosophy of Biological and Biomedical Sciences, 43(1): 202-208.  

When game theory was introduced to biology, the components of classic game theory models were replaced with elements more befitting evolutionary phenomena. The actions of intelligent agents are replaced by phenotypic traits; utility is replaced by fitness; rational deliberation is replaced by natural selection. In this paper, I argue that this classic conception of comprehensive reapplication is misleading, for it overemphasizes the discontinuity between human behavior and evolved traits. Explicitly considering the representational roles of evolutionary game theory brings to attention neglected areas of overlap, as well as a range of evolutionary possibilities that are often overlooked. The clarifications this analysis provides are well-illustrated by—and particularly valuable for—game theoretic treatments of the evolution of social behavior.

Sex and Sensibility: The Role of Social Selection, review symposium of Roughgarden's The Genial Gene, 2011, with Erika Milam, Roberta Millstein, and Joan Roughgarden, Metascience, 20(2): 253-277. (also book reviews)

My essay: As detailed in The Genial Gene (2009), Joan Roughgarden's conception of social selection involves twenty-six empirical hypotheses regarding the evolution of a variety of traits related to sexual reproduction, gender, and the rearing of offspring. Yet Roughgarden sets out to show something even beyond this array of empirical hypotheses. Her contention is that cooperation is `basic to biological nature'. In this paper, I investigate the role that this claim plays in Roughgarden's work. This investigation clarifies the relationship between Roughgarden's social selection theory and extant sexual selection theory. It also clarifies the nature of Roughgarden's criticisms of other accounts of the evolution of cooperation, including kin selection, reciprocal altruism, and group selection. My purpose is twofold: this investigation helps put social selection theory into perspective, and it analyzes an episode of science where broad-scope theoretical claims are plainly entangled with empirical hypotheses.

Mechanisms Are Causes, Not Components: A Response to Connolly, et al., with Brian J. McGill, 2018, Trends in Ecology and Evolution, 33: 304-305. Second author.

We affirm Connolly et al.'s insight, in “Process, Mechanism, and Modeling in Macroecology, ” that “explicit representation of causal structure” is central to ecology. However, in our view, these authors' exclusive focus on mechanistic and process-based models overlooks a number of other important modeling approaches that explicitly represent causal structure. We thus argue that the advantages Connolly et al. tout for mechanistic and process-based models are better attributed to a much broader class of models: causal models. 
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Explanatory Independence and Epistemic Interdependence: A Case Study of the Optimality Approach, 2010, The British Journal for the Philosophy of Science, 61(1): 213–233. (also relations among projects/fields)

The value of optimality modeling has long been a source of contention amongst population biologists.  Here I present a view of the optimality approach as at once playing a crucial explanatory role and yet also depending on external sources of confirmation.  Optimality models are not alone in facing this tension between their explanatory value and their dependence on other approaches; I suspect that the scenario is quite common in science. This investigation of the optimality approach thus serves as a case study, on the basis of which I suggest that there is a widely felt tension in science between explanatory independence and broad epistemic interdependence, and that this tension influences scientific methodology.

Optimality Modeling in a Suboptimal World, 2009, Biology and Philosophy, 24(2): 183-197.  

The fate of optimality modeling is typically linked to that of adaptationism: the two are thought to stand or fall together. I argue here that this is mistaken. The debate over adaptationism has tended to focus on one particular use of optimality models, which I refer to here as their strong use. The strong use of an optimality model involves the claim that selection is the only important influence on the evolutionary outcome in question and is thus linked to adaptationism. However, biologists seldom intend this strong use of optimality models. One common alternative that I term the weak use simply involves the claim that an optimality model accurately represents the role of selection in bringing about the outcome. This and other weaker uses of optimality models insulate the optimality approach from criticisms of adaptationism, and they account for the prominence of optimality modeling (broadly construed) in population biology. The centrality of these uses of optimality models ensures a continuing role for the optimality approach, regardless of the fate of adaptationism.

Optimality Modeling and Explanatory Generality, 2007, Philosophy of Science, 74(5): 680-691. 

The optimality approach to modeling natural selection has been criticized by many biologists and philosophers of biology. For instance, Lewontin (1979) argues that the optimality approach is a shortcut that will be replaced by models incorporating genetic information, if and when such models become available. In contrast, I think that optimality models have a permanent role in evolutionary study. I base my argument for this claim on what I think it takes to best explain an event. In certain contexts, optimality and game-theoretic models best explain some central types of evolutionary phenomena. 

Idealized Representation in Science

Truth and Reality: How to Be a Scientific Realist Without Believing Scientific Theories Should Be True, K. Khalifa, I. Lawler & E. Shech (eds.), Scientific Understanding and Representation: Modeling in the Physical Sciences, Routledge, 2022. See Pincock's response here. 
Different Ways to be a Realist: A Response to Pincock, K. Khalifa, I. Lawler & E. Shech (eds.), Scientific Understanding and Representation: Modeling in the Physical Sciences, Routledge, 2022. A response to Pincock's paper here. 
Scientific realism is a thesis about the success of science. Most traditionally: science has been so successful at prediction and guiding action because its best theories are true (or approximately true or increasing in their degree of truth). If science is in the business of doing its best to generate true theories, then we should turn to those theories for explanatory knowledge, predictions, and guidance of our actions and decisions. Views that are popular in contemporary philosophy of science about scientific modeling and the centrality of idealization create several challenges for this traditional form of scientific realism. Yet the basic idea behind scientific realism that science has been and will continue to be epistemically successful is deeply appealing. This chapter explores the challenges posed by idealization and scientific modeling to motivate a scientific realism fully divorced from the idea that science is in the business of generating true theories. On the resulting view, the objects of scientific knowledge are causal patterns, so this knowledge only ever provides partial, simplified accounts of a complex reality. This variety of selective realism better accommodates the nature of our present-day scientific successes and offers an interpretation of scientific progress that resists the antirealist’s pessimism.  
In his chapter in this volume, Christopher Pincock develops an argument for scientific realism based on scientific understanding, and he argues that Giere’s (2006) and my (2017, 2020) commitment to the context-dependence of scientific understanding or knowledge renders our views unable to account for an essential step in how scientists come to know. Meanwhile, in my chapter in this volume, I motivate a view that I call "causal pattern realism." In this response to Pincock's chapter, I will sketch a revised version of Pincock’s argument for realism that is consistent with causal pattern realism. Then I will respond to Pincock’s concern that the context-dependence of understanding on my view would interfere with the scientific community’s ability to extrapolate from specific experimental and observational contexts as needed to develop knowledge. My goal is not to convince anyone to be a causal pattern realist but rather to create the space for such a view, taking into account the concerns motivating Pincock. 
Why It Matters that Idealizations Are False, unpublished draft from 2020. 
Idealization and Many Aims, 2020, Philosophy of Science, Symposium Proceedings PSA2018, 87: 933–943. 
Many of our best scientific explanations incorporate idealizations, that is, false assumptions. Philosophers of science disagree about whether and to what extent we must as a result give up on truth as a prerequisite for explanation and thus understanding. Here I propose reframing this. Factivism or veritism about explanation is not, I think, an obvious and preferable view to be given up only under duress. Rather, it is philosophically fruitful to emphasize how departures from the truth facilitate explanation (and understanding). I begin by motivating one version of the idea that idealizations positively contribute to understanding, and then I make the case that it is philosophically important to emphasize this contribution of idealizations. I conclude with a positive account of what theorists about science stand to gain by acknowledging, even emphasizing, how certain departures from the truth benefit our scientific explanations.
The Diverse Aims of Science, 2015, in a special issue of Studies in History and Philosophy of Science, 53: 71-80.
(also science, values, public)
There is increasing attention to the centrality of idealization in science.   One common view is that models and other idealized representations are important to science, but that they fall short in one or more ways. On this view, there must be an intermediary step between idealized representation and the traditional aims of science, including truth, explanation, and prediction. Here I develop an alternative interpretation of the relationship between idealized representation and the aims of science. In my view, continuing, widespread idealization calls into question the idea that science aims for truth. I argue that understanding must replace truth as the ultimate epistemic aim of science. Additionally, science has a wide variety aims, epistemic and non-epistemic, and these aims motivate different kinds of scientific products. Finally, I show how these diverse aims---all rather distant from truth---result in the expanded influence of social values on science.
In this paper, I first outline the view developed in my recent book on the role of idealization in scientific understanding. I discuss how this view leads to the recognition of a number of kinds of variability among scientific representations, including variability introduced by the many different aims of scientific projects. I then argue that the role of idealization in securing understanding distances understanding from truth, but that this understanding nonetheless gives rise to scientific knowledge. This discussion will clarify how my view relates to three other recent books on understanding by Henk de Regt, Catherine Elgin, and Kareem Khalifa.

Scientific Explanation

Eight Other Questions about Explanation, 2018, Explanation Beyond Causation, Alexander Reutlinger and Juha Saatsi (Eds.), Oxford University Press.

The tremendous philosophical focus on how to characterize explanatory metaphysical dependence has eclipsed a number of other unresolved issued about scientific explanation. The purpose of this paper is taxonomical. I will outline a number of other questions about the nature of explanation and its role in science—eight, to be precise—and argue that each is independent. All of these topics have received some philosophical attention, but none nearly so much as it deserves. Furthermore, existing views on these topics have been obscured by not distinguishing among these independent questions and, especially, by not separating them from the question of what metaphysical dependence relation is explanatory. Philosophical analysis of scientific explanation would be much improved by attending more carefully to these, and probably still other, elements of an account of explanation. 
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Causal Patterns and Adequate Explanations, 2015, Philosophical Studies, 172(5): 1163-1182. 

Causal accounts of scientific explanation are currently broadly accepted (though not universally so). My first task in this paper is to show that, even for a causal approach to explanation, significant features of explanatory practice are not determined by settling how causal facts bear on the phenomenon to be explained. I then develop a broadly causal approach to explanation that accounts for the additional features that I argue an explanation should have. This approach to explanation makes sense of several aspects of actual explanatory practice, including the widespread use of equilibrium explanations, the formulation of distinct explanations for a single event, and the tight relationship between explanations of events and explanations of causal regularities.
 

Levels of Explanation Reconceived, 2010, Philosophy of Science, 77(1): 59-72. 

A common argument against explanatory reductionism is that higher-level explanations are sometimes or always preferable because they are more general than reductive explanations. Here I challenge two basic assumptions that are needed for that argument to succeed. It cannot be assumed that higher-level explanations are more general than their lower-level alternatives or that higher-level explanations are general in the right way to be explanatory. I suggest a novel form of pluralism regarding levels of explanation, according to which explanations at different levels are preferable in different circumstances because they offer different types of generality, which are appropriate in different circumstances of explanation.

Scientific Explanation: Putting Communication First, 2016, Philosophy of Science, Symposium Proceedings PSA2014, 83: 721–732.

Explanations must bear the proper relationship to the world: they must capture what, out in the world, is responsible for the explanandum. At issue among traditional accounts of explanation is how to construe that responsibility relation, viz., what it is upon which the explanandum depends. But this does not tell us everything we need to know in order to determine the content of scientific explanations. Just as explanations must bear the proper relationship to the world, they must also bear the proper relationship to the individuals for which they are generated. With few exceptions, philosophers either ignore entirely the relationship between explanations and their audience, or else demote this consideration to a secondary role. In contrast, I argue that considerations of an explanation's communicative purpose are necessary in order to get a satisfactory account of explanation off the ground. 

Biological Explanation, 2013, in K. Kampourakis (Ed.), The Philosophy of Biology: A Companion for Educators, chapter 4, 49-65, Springer.  

One of the central aims of science is explanation: scientists seek to uncover why things happen the way they do. The most pressing issue about explanation in biology may be how to account for the wide range of explanatory styles encountered in the field. This issue is crucial, for the aims of biological explanation influence a variety of other features of the field of biology. Explanatory aims account for the continued neglect of some central causal factors, a neglect that would otherwise be mysterious. This is linked to the persistent use of models like evolutionary game theory and population genetic models, models that are simplified to the point of unreality. These explanatory aims also offer a way to interpret many biologists’ total commitment to one or another methodological approach and the intense disagreements that result. 

Explanation and Understanding: An Alternative to Strevens' Depth, 2011, The European Journal for Philosophy of Science, 1(1): 29-38. 

In Depth (2009), Michael Strevens offers an account of causal explanation according to which explanatory practice is shaped by counterbalanced commitments to representing causal influence and abstracting away from overly specific details.  Here I outline Strevens’ approach to event explanation and raise one concern with that account.  I argue that what Strevens calls explanatory frameworks figure prominently in explanatory practice because, contrary to Strevens’ view, they actually improve explanations.  This suggestion is simple but has far-reaching implications.  It affects the status of explanations that cite multiply realizable properties; the explanatory role of causal factors with small effect; and Strevens’ titular explanatory virtue, depth.  This results in greater coherence with explanatory practice and accords with the emphasis that Strevens places on explanatory patterns.  Ultimately, my suggestion preserves a tight connection between explanation and the creation of understanding.

Relations Among Scientific Projects and Fields

Antireductionism Has Outgrown Levels, A. Wilson and K. Robertson (Eds.), Levels of Explanation, Oxford UP, forthcoming. 

Positing levels of explanation has played an important role in philosophy of science. This facilitated the advocacy of antireductionism of explanations, which, at its most basic, is the idea that scientific explanations citing large (i.e. non-microphysical) entities will persist. The idea that explanations come in levels captures important features of explanatory practices, and it also does well at helping to define different positions one might take regarding explanatory reductionism or antireductionism. Yet the idea that explanations come in levels has also led philosophers astray. This systematically misconstrues the relationship different explanations bear to one another, suggests candidate explanations are less numerous than they in fact are, and occludes recognition of how the selection of explanations can vary across research projects. Antireductionists about explanation should move on from talk of levels. Or so I will argue..
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A Neurathian Conception of the Unity of Science, 2011, Erkenntnis, 34(3): 305–319.  (also logical empiricism)

An historically important conception of the unity of science is explanatory reductionism, according to which the unity of science is achieved by explaining all laws of science in terms of their connection to microphysical laws. There is, however, a separate tradition that advocates the unity of science. According to that tradition, the unity of science consists of the coordination of diverse fields of science, none of which is taken to have privileged epistemic status. This alternate conception has roots in Otto Neurath’s notion of unified science. In this paper, I develop a version of this coordination approach to unity and discuss its connection to Neurath’s views. The resulting conception of the unity of science achieves some aims similar to those of explanatory reductionism, but does so in a radically different way. As a result, it is immune to the criticisms of explanatory reductionism. This conception of unity is also importantly different from the view that science is disunified, and I conclude by demonstrating how the coordinate unity of science accords better with scientific practice than do conceptions of the disunity of science.

The Limitations of Hierarchical Organization, with Brian McGill, 2012, Philosophy of Science, 79(1): 120-140.  

The concept of hierarchical organization is commonplace in science. Subatomic particles compose atoms, which compose molecules; cells compose tissues, which compose organs, which compose organisms; etc. Hierarchical organization is particularly prominent in ecology, a field of research explicitly arranged around levels of ecological organization. The concept of levels of organization is also central to a variety of debates in philosophy of science. Yet many difficulties plague the concept of discrete hierarchical levels. In this paper, we show how these difficulties undermine various implications ascribed to hierarchical organization, and we suggest  the concept of scale as a promising alternative to levels. Investigating causal processes at different scales offers a way to retain a notion of quasi-levels that avoids the difficulties inherent in the classic concept of hierarchical levels of organization. Throughout, our focus is on ecology, but the results generalize to other invocations of hierarchy in science and philosophy of science.

Our World Isn't Organized into Levels, 2021, D. S. Brooks, J. DiFrisco, and W. C. Wimsatt (Eds.), Levels of Organization in the Biological Sciences, MIT Press.

Levels of organization and their use in science have received increased philosophical attention of late, including challenges to the well-foundedness or widespread usefulness of levels concepts. One kind of response to these challenges has been to advocate a more precise and specific levels concept that is coherent and useful. Another kind of response has been to argue that the levels concept should be taken as a heuristic, to embrace its ambiguity and the possibility of exceptions as acceptable consequences of its usefulness. In this chapter, I suggest that each of these strategies faces its own attendant downsides, and that pursuit of both strategies (by different thinkers) compounds the difficulties. That both kinds of approaches are advocated is, I think, illustrative of the problems plaguing the concept of levels of organization. I end by suggesting that the invocation of levels may mislead scientific and philosophical investigations more than it informs them, so our use of the levels concept should be updated accordingly.

Patterns in Cognitive Phenomena and Pluralism of Explanatory Styles, with Guilherme Sanches de Oliveira, Topics in Cognitive Science, 2020, 12 (4): 1306-1320.

​Debate about cognitive science explanations has been formulated in terms of identifying the proper level(s) of explanation. Views range from reductionist, favoring only neuroscience explanations, to mechanist, favoring the integration of levels, to pluralist, favoring the preservation of even the most general, high-level explanations. We challenge this framing. We suggest that these are not different levels of explanation at all but, rather, different styles of explanation that capture different, cross-cutting patterns in cognitive phenomena. Which pattern is explanatory depends on both the cognitive phenomenon under investigation and the research interests occasioning the explanation. This reframing changes how we should answer the basic questions of which cognitive science approaches explain and how these explanations relate to one another. On this view, we should expect different approaches to offer independent explanations in terms of their different focal patterns and the value of those explanations to partly derive from the broad patterns they feature.

Explanatory Independence and Epistemic Interdependence: A Case Study of the Optimality Approach, 2010, The British Journal for the Philosophy of Science, 61(1): 213–233.  (also population biology)

The value of optimality modeling has long been a source of contention amongst population biologists.  Here I present a view of the optimality approach as at once playing a crucial explanatory role and yet also depending on external sources of confirmation.  Optimality models are not alone in facing this tension between their explanatory value and their dependence on other approaches; I suspect that the scenario is quite common in science. This investigation of the optimality approach thus serves as a case study, on the basis of which I suggest that there is a widely felt tension in science between explanatory independence and broad epistemic interdependence, and that this tension influences scientific methodology.

Science's Relationship to Values and the Public

Theorizing Participatory Research, with Andrew Evans, in E. Anderson (Ed.), Ethical Issues in Stakeholder-Engaged Health Research, Springer, 2023. Second author. 

A wide variety of scientific research projects include public participation in roles going beyond the classic use of subjects in human subjects research. “Participatory research” is an umbrella term for such projects. In this chapter, we begin by surveying the variety of participatory research approaches across fields. We examine what goals participatory research projects seek to achieve, both of social and scientific value. Next, we apply this theoretical framework to challenges that participatory research faces. We then survey three typologies of participatory research projects, each of which can illuminate and guide decisions in project development. We end with a look at participatory research approaches in health contexts, applying the theoretical resources we introduced earlier in the chapter. 


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The Diverse Aims of Science, 2015, in a special issue of Studies in History and Philosophy of Science, 53: 71-80.
(also idealized representation)

There is increasing attention to the centrality of idealization in science.   One common view is that models and other idealized representations are important to science, but that they fall short in one or more ways. On this view, there must be an intermediary step between idealized representation and the traditional aims of science, including truth, explanation, and prediction. Here I develop an alternative interpretation of the relationship between idealized representation and the aims of science. In my view, continuing, widespread idealization calls into question the idea that science aims for truth. I argue that understanding must replace truth as the ultimate epistemic aim of science. Additionally, science has a wide variety aims, epistemic and non-epistemic, and these aims motivate different kinds of scientific products. Finally, I show how these diverse aims---all rather distant from truth---result in the expanded influence of social values on science.

Feminist Implications of Model-Based Science, 2012, Studies in History and Philosophy of Science, 43(2): 383-389. 

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. 

Divergence of Values and Goals in Participatory Research, with Lucas Dunlap, Amanda Corris, Melissa Jacquart, and Zvi Biener, 2021, in a special issue of Studies in History and Philosophy of Science.

Public participation in scientific research has gained prominence in many scientific fields, but the theory of participatory research is still limited. In this paper, we suggest that the divergence of values and goals between academic researchers and public participants in research is key to analyzing the different forms this research takes. We introduce an expanded conception of norms of collaboration that extends to both academic researchers and public participants. We suggest that satisfying these norms requires consideration of the two groups' possibly divergent values and goals, and that a broad characterization of participatory research that starts from participants' values and goals can motivate both public participants’ role in the research and the virtues of the research. The resulting framework clarifies the similarities and differences among participatory projects and can help guide the responsible design of such projects.

Toward Philosophy of Science’s Social Engagement, with Francis Cartieri, 2014, in a special issue of Erkenntnis on socially engaged philosophy of science, 79(5): 901-916.  

In recent years, philosophy of science has witnessed a significant increase in attention directed toward the field’s social relevance. This is demonstrated by the formation of societies with related agendas, the organization of research symposia, and an uptick in work on topics of immediate public interest. This collection of papers results from one such event: a three-day colloquium on socially engaged philosophy of science held at the University of Cincinnati. In this introduction, we first survey the recent history of philosophy of science’s social involvement (or lack thereof) and contrast this with the much greater social involvement of the sciences themselves. Next, we argue that the field of philosophy of science bears a special responsibility to contribute to public welfare. We then introduce as a term of art “socially engaged philosophy of science” and articulate what we take to be distinctive about social engagement. Finally, we survey the current state of social engagement in philosophy of science and suggest some practical steps to support this trajectory. 

History of Logical Empiricism

A Neurathian Conception of the Unity of Science, 2011, Erkenntnis, 34(3): 305–319. 
(also relations among projects/fields)

Revisiting Galison's 'Aufbau/Bauhaus' in Light of Neurath's Philosophical Projects, with Audrey Yap, 2006, Studies in History and Philosophy of Science, 37(3): 469-488.
An historically important conception of the unity of science is explanatory reductionism, according to which the unity of science is achieved by explaining all laws of science in terms of their connection to microphysical laws. There is, however, a separate tradition that advocates the unity of science. According to that tradition, the unity of science consists of the coordination of diverse fields of science, none of which is taken to have privileged epistemic status. This alternate conception has roots in Otto Neurath’s notion of unified science. In this paper, I develop a version of this coordination approach to unity and discuss its connection to Neurath’s views. The resulting conception of the unity of science achieves some aims similar to those of explanatory reductionism, but does so in a radically different way. As a result, it is immune to the criticisms of explanatory reductionism. This conception of unity is also importantly different from the view that science is disunified, and I conclude by demonstrating how the coordinate unity of science accords better with scientific practice than do conceptions of the disunity of science.
Historically, the Vienna Circle and the Dessau Bauhaus were related, with members of each group familiar with the ideas of the other.  Peter Galison argues that their projects are related as well, through shared political views and methodological approach.  The two main figures that connect the Vienna Circle to the Bauhaus—and the figures upon which Galison focuses—are Rudolf Carnap and Otto Neurath.  Yet the connections that Galison develops do not properly capture the common themes between the Bauhaus and Neurath’s philosophical projects.  We demonstrate this by considering Neurath’s philosophical commitments.  We suggest different connections between Neurath’s projects and the Bauhaus, connections that are both substantive and philosophically interesting.

Book Reviews

Review of Adam Frank, Marcelo Glaser, and Evan Thompson's The Blind Spot, 2024, Public Understanding of Science. 

A Philosophical Journey with Some Startling Detours, review of Paul Dicken’s Getting Science Wrong: Why the Philosophy of Science Matters , 2018, Physics Today, 71(11):52. 
Sex and Sensibility: The Role of Social Selection, review symposium of Joan Roughgarden's The Genial Gene, 2011, with Erika Milam, Roberta Millstein, and Joan Roughgarden, Metascience, 20(2): 253-277.
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Review of Michela Massimi's Perspectival Realism, 2023, The Philosophical Quarterly, 128: 363-366. 

Review of Catherine Elgin’s True Enough, 2019, Philosophical Review, 128: 363-366. 

Review of Helen Longino's Studying Human Behavior: How Scientists Investigate Aggression and Sexuality , 2013, Notre Dame Philosophical Reviews.