In no particular order:

My current project [2016] is focused on causation and complexity, developing a metaphysical account of causation that encompasses both process-oriented and counterfactual-oriented accounts of causation. 
  • I demonstrate how to represent counterfactually robust causal relata in phase space, over which probability distributions can be put, in order to then apply tools from information theory to get some lovely and really specific ways to evaluate the degree of causal relatedness between the relata (and to represent other features of the causal field, like a general gradient, divergence, etc.). This is the more technical side of a project that considers what a pattern ontology over the causal nexus looks like, and the consequences this view has for a number of other debates. 
I was one of the the keynote speakers at the 2016 Causality in the Sciences conference at Aarhus University in Copenhagen. My talk is now getting written up, "A new place for action explanation in scientific causal explanation."
  • Abstract: I make the case that the causal exclusion problem has little if anything useful to add to a discussion of the role of mental causation in the sciences, whereas good old fashioned action explanation a la Anscombe has a much more significant role to play in developing theoretical frameworks by which to relate causal variables at different levels in cognitive neuroscience. Kim’s causal exclusion problem has distant roots in a disagreement between Anscombe and Davidson, one outcome of which was Davidson’s thesis of anomalous monism to which Kim responded with the causal exclusion problem. Returning to this dispute between Anscombe and Davidson opens up a different path forward in understanding the role of uniquely mental variables in the sciences. Both agree on a now-largely-rejected notion of causal explanation, namely, the deductive-nomological model, and then disagree about whether or not action explanation is of that kind. Davidson then thinks action explanation must be of the same sort as this kind of explanation, whereas Anscombe thinks it could not be. I re-evaluate their arguments in light of a very different model of causal explanation in the sciences, Woodward’s interventionist account: what would action explanation look like if it were of that sort of causal explanation? By carefully keeping track of individual causal tokens and what might explain them, versus variables as tokens grouped in different ways with correspondingly different explanations, I show that there can be, in sciences such as cognitive neuroscience, legitimately testable and fully empirical causal claims involving uniquely mental variables that satisfy Anscombe’s desiderata for action explanation. These variables can be treated in the same way that any other (non-mental) variable can be treated, and provide a useful framework by which to connect causal claims made at higher and lower levels.
The paper "Complements, not competitors: causal and mathematical explanations" is forthcoming at BJPS, and available at Philpapers and philsci archive as a preprint.
  • Abstract: A finer-grained delineation of a given explanandum reveals a nexus of closely related causal and noncausal explanations, complementing one another in ways that yield further explanatory traction on the phenomenon in question. By taking a narrower construal of what counts as a causal explanation, a new class of distinctively mathematical explanations pops into focus; Lange’s (2013) characterization of distinctively mathematical explanations can be extended to cover these. This new class of distinctively mathematical explanations is illustrated with the Lotka-Volterra equations. There are at least two distinct ways those equations might hold of a system, one of which yields straightforwardly causal explanations, but the other of which yields explanations that are distinctively mathematical in terms of nomological strength. In the first, one first picks out a system or class of systems, finds that the equations hold in a causal-explanatory way; in the second, one starts with the equations and explanations that must apply to any system of which the equations hold, and only then one turns to the world to see of what, if any, systems it does in fact hold. Using this new way in which a model might hold of a system, I highlight four specific ways in which causal and noncausal explanations can complement one another.
I just finished a book chapter on Shadworth Hodgson's metaphysic of experience. 
  • This chapter offers a overview of Shadworth Hodgson's account of experience as fundamentally temporal, an account that was deeply influential on thinkers such as William James and which prefigures the phenomenology of Husserl in many ways. I highlight eight key features that are characteristic of Hodgson's account, and how they hang together to provide a coherent overall picture of experience and knowledge. Hodgson's account is then compared to Husserl's, and I argue that Hodgson's account offers a better target for projects such as neurophenomenology than does Husserl's. Hodgson's account is historically important as a culmination of a certain trajectory of British Empiricist thought. It offers a substantive alternative for how to think about temporality and experience in contemporary discussions, not just of the present moment but of the relationship between experience and knowledge more broadly.
A book chapter on reduction in the biomedical sciences is now available here. It is forthcoming in the Routledge Companion to Philosophy of Medicine, edited by Solomon, Simon, and Kincaid.
  • This chapter discusses several kinds of reduction that are often found in the biomedical sciences, in contrast to reduction in fields such as physics. This includes reduction as a methodological assumption for how to investigate phenomena like complex diseases, and reduction as a conceptual tool for relating distinct models of the same phenomenon. The case of Parkinson’s disease illustrates a wide variety of ways in which reductionism is an important tool in medicine.

At ISHPSSB 2015 in Montreal, I got an unexpected chance to debut a project to which I will return in about a year or so, about understanding land and ecosystem management decisions in terms of grafting mechanisms.

We had a fantastic time at Bogenfest in Pittsburgh. In case you missed it, the lyrics to "Oh Jim Bogen" are here.

The slides from my talk at the Causality and Complexity in the Sciences conference in Koln are available here. Much thanks to several audiences for great questions and really lively post-talk discussions on this work. This work is slow in coming out but will be worth the wait.

Some highlights from work published in the last couple of years:

The chapter "The Representation of Time in Agency" (Blackwell Companion to the Philosophy of Time) concerns the close interrelationships between time, representation, and agency, from an interdisciplinary perspective. The first part is more survey-oriented, and considers several methodological perspectives from which the issue in the title is often addressed, in terms of the difference between the role that time plays in agency, versus the role of explicitly represented time. The second half of this paper, however, lays out the framework for a new project in philosophy, one that I am hoping gets some uptake. I point out shortcomings in Husserl's phenomenology of time consciousness with regards to the prospects for generalizing it to include agency, and note that these shortcomings are inherited by projects in neurophenomenology. I propose a parallel project that begins with Michael Thompson's naive action theory instead of Husserl's account, as a way of characterizing the phenomena to be explained neuroscientifically. I sketch out what one might look for in a neuroscientific explanation of those features identified as key by Thompson of how certain temporal structure of actions are differentially represented. Looking for a dissertation project? There it is!

In "When to expect violations of causal faithfulness and why it matters", (Philosophy of Science) I show how one key assumption used in drawing inferences about causal structure from probabilistic relationships in data is one that will systematically fail in complex evolved systems. The very character of homeostatic systems is such that they will involve the kinds of precisely counterbalanced causal relationships that "fool" these inference methods. Failures of causal faithfulness to apply to a system under investigation are particularly important, since CF is an assumption used to go from probabilistic relationships in data to causal structure. I draw some conclusions about this: systems that are almost-but-not-quite balanced will cause as much experimental difficulty as systems that genuinely violate CF, which means this is a much more wide-spread experimental difficulty than has yet been appreciated in the formal literature; and we should expect experimental techniques in those sciences to have already incorporated compensatory means to accommodate this feature of their target systems (what, precisely, those compensatory methodological techniques are is a very useful and open question, to which I hope to return in the future).

The chapter "Mental Causation" (Springer Handbook of Neuroethics) is not merely an overview of the state of discussion on this topic, but also offers some arguments as to how we ought to be approaching the issue of mental causation from the perspective of causation in general, rather than as a sui generis kind of causal relation. This piece offers a helpful way to categorize the wide multitude of responses to the causal exclusion problem in terms of their broad argumentative strategies, which makes it useful for teaching.

The "Field guide to mechanisms," part I and part II (Philosophy Compass) identifies five distinct senses of the term 'mechanism' that are used in contemporary discussions of mechanisms in philosophy of science and related fields (I don't address the use of this term in other historical periods, such as the early modern period). I highlight the key characteristics of each species of mechanism, where it is likely to be found, and offer some ways to distinguish each species of mechanism from other closely related ones. I diagnose a number of debates concerning mechanisms as involving distinct senses of mechanism. Each section considers the relevant sense of mechanism in terms of its ontological commitments, methodological implications, role in explanation, and status with respect to anti/reduction.