According to the “received view” the disagreement between endurantism and perdurantism is ontological and concerns the existence of temporal parts of continuants. In a recent paper, Wasserman (2016) argues that the ontological conception of these theories does not address the crucial point: explaining the way things persist. According to Wasserman, perdurantism is not just the view that things have temporal parts; it is the view that things persist by (or in virtue of) having temporal parts. Moreover, in the last decade an alternative understanding of the dispute between endurantism and perdurantism, the so called “locative turn”, has led to an understanding of these two theories as concerning crucially locational rather than mereological notions. Our main aim in this paper is to bring together those two revisionary approaches to the received view, and show how they can enrich each other and open up further dimensions of the debate. Finally in the last section we focus on some of the non-standard accounts of persistence and location that arise from this approach, such as “autonomism of persistence and location” and “reverse locational endurantism/perdurantism”.

Can philosophical theories of perception defer to perceptual science when fixing their ontological commitments regarding the objects of perception? Or in other words, can perceptual science inform us about the nature of perception? Many contemporary mainstream philosophers of perception answer affirmatively. However, in this essay I provide two arguments against this idea. On the one hand, I will argue that perceptual science is not committed to certain assumptions, relevant for determining perceptual ontology, which however are generally relied upon by philosophers when interpreting such science. On the other hand, I will show how perceptual science often relies on another assumption, which I call the ‘Measuring instrument conception’ of sensory systems, which philosophers of perception should clearly reject. Given these two symmetric lines of argument, I will finally suggest that we ought to think differently about the relationship between perceptual science and the philosophy of perception.

In this paper, I will argue that, contrary to what is generally assumed in the debate on expressive action, we do not have good reasons to exclude facial and bodily expressions of emotion such as smiling or frowning from the category of actions. For this purpose, I will compare facial and bodily expressions of emotion with simple expressive actions, such as jumping for joy or covering one’s face in shame. I will try to show that simple expressive actions cannot be presented as actions while excluding facial and bodily expressions of emotion from this condition. My contention will then be that either both sorts of behaviour are to be identified as actions or neither is. The latter sounds rather implausible, though, as we would have to assimilate jumping for joy or covering one’s face in shame to spasms, which conflicts with the way we relate to such behaviours. My conclusion will then be that both simple expressive actions and facial and bodily expressions of emotion should be included within the category of actions, at least on the basis of the main assumptions in the current debate on expressive action.

The argument from inductive risk is considered to be one of the strongest challenges for value-free science. A great part of its appeal lies in the idea that even an ideal epistemic agent—the “perfect scientist” or “scientist qua scientist”—cannot escape inductive risk. In this paper, I scrutinize this ambition by stipulating an idealized Bayesian decision setting. I argue that inductive risk does not show that the “perfect scientist” must, descriptively speaking, make non-epistemic value-judgements, at least not in a way that undermines the value-free ideal. However, the argument is more successful in showing that there are cases where the “perfect scientist” should, normatively speaking, use non-epistemic values. I also show that this is possible without creating problems of illegitimate prescription and wishful thinking. Thus, while inductive risk does not refute value-freedom completely, it still represents a powerful critique of value-free science.

The Price equation is currently considered one of the fundamental equations —or even the fundamental equation— of evolution. In this article, we explore the role of this equation within cultural evolutionary theory. More specifically, we use it to account for the explanatory power and the theoretical structure of a certain generalised version of dual-inheritance theory. First, we argue that, in spite of not having a definite empirical content, the Price equation offers a suitable formalisation of the processes of cultural evolution, and provides a powerful heuristic device for discovering the actual causes of cultural change and accumulation. Second, we argue that, as a consequence of this, a certain version of the Price equation is the fundamental law of cultural evolutionary theory. In order to support this claim, we sketch the ideal structure of dual-inheritance theory and we stress the unificatory role that the Price equation plays in it.

One of the most important things that the Darwinian revolution affected is the previous teleological thinking. In particular, the attribution of functions to various entities of the natural world with explanatory pretensions. In this change, his theory of natural selection played an important role. We all agree on that, but the diversity and heterogeneity of the answers that try to explain what Darwin did exactly with functional biology are overwhelming. In this paper I will try to show how Darwin modified previous functional biology. Pre-Darwinian naturalists did not hesitate to attribute functions in which, for example, the traits of one species were in the service of other species. I will try to show that this has consequences on the discussion regarding the nature of functional language, since the main approaches, the systemic and the etiological, do not adequately account for these changes and therefore do not account for the way functional biology regulates the kind of legitimate functions. I will outline a possible new solution to this problem: appropriate functional attributions in Darwinian functional biology could be regulated by a theory or a set of laws that provide the criteria for determining its fundamental concepts.

This paper discusses some procedures developed in recent work in machine learning for inferring causal direction from observational data. The role of independence and invariance assumptions is emphasized. Several familiar examples, including Hempel’s flagpole, problem are explored in the light of these ideas. The framework is then applied to problems having to do with explanatory direction in non-causal explanation.

Does smoke cause fire or does fire cause smoke? James Woodward’s “Flagpoles anyone? Causal and explanatory asymmetries” argues that various statistical independence relations not only help us to uncover the directions of causal and explanatory relations in our world, but also are the worldly basis of causal and explanatory directions. We raise questions about Woodward’s envisioned epistemology, but our primary focus is on his metaphysics. We argue that any alleged connection between statistical (in)dependence and causal/explanatory direction is contingent, at best. The directions of causal/explanatory relations in our world seem not to depend on the statistical (in)dependence relations in our world (conceived of either as frequency patterns or as relations among chances). Thus, we doubt that statistical (in)dependence relations are the worldly basis of causal and explanatory directions.

In “Flagpoles anyone? Causal and explanatory asymmetries”, James Woodward supplements his celebrated interventionist account of causation and explanation with a set of new ideas about causal and explanatory asymmetries, which he extracts from some cutting-edge methods for causal discovery from observational data. Among other things, Woodward draws interesting connections between observational causal discovery and interventionist themes that are inspired in the first place by experimental causal discovery, alluding to a sort of unity between observational and experimental causal discovery. In this paper, I make explicit what I take to be the implicated unity. Like experimental causal discovery, observational causal discovery also relies on interventions (or exogenous variations, to be more accurate), albeit interventions that are not carried out by investigators and hence need to be detected as part of the inference. The observational patterns appealed to in observational causal discovery are not only surrogates for would-be interventions, as Woodward sometimes puts it; they also serve to mark relevant interventions that actually happen in the data generating process.

I would like to congratulate James Woodward for another landmark accomplishment, after publishing his Making Things Happen: A Theory of Causal Explanation (Woodward, 2003). Makes Things Happens gives an elegant interventionist theory for understanding explanation and causation. The new contribution (Woodward, 2022) relies on that theory and further makes a big step towards empirical inference of causal relations from nonexperimental data. In this paper, I will focus on some of the emerging computational methods for finding causal relations from non-experimental data and attempt to complement Woodward’s contribution with discussions on 1) how these methods are connected to the interventionist theory of causality, 2) how informative the output of the methods is, including whether they output directed causal graphs and how they deal with confounders (unmeasured common causes of two measured variables), and 3) the assumptions underlying the asymptotic correctness of the output of the methods about causal relations. Different causal discovery methods may rely on different aspects of the joint distribution of the data, and this discussion aims to provide a technical account of the assumptions.

What happens to the causal structure of a world when time is reversed? At first glance it seems there are two possible answers: the causal relations are reversed, or they are not. I argue that neither of these answers is correct: we should either deny that time-reversed worlds have causal relations at all, or deny that causal concepts developed in the actual world are reliable guides to the causal structure of time-reversed worlds. The first option is motivated by the instability under intervention of time-reversed dynamical evolutions. The second option is motivated by a recognition of how contingent structural features of the actual world shape, and license the application of, our causal concepts and reasoning strategies.

This paper aims to provide an answer to James Woodward’s article “Flagpoles anyone? Causal and explanatory asymmetries”. It will be conjectured that, when causal directionality depends on the experimental design, it is because the variables involved are capable of producing changes in each other. This will be exemplified using the case of ideal gases as opposed to the flagpole-shadow scenario.