What Constitutes an Explanation in Biology?

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

Horizontal Inequalities and Ethnonationalist Civil War: A Global Comparison

Contemporary research on civil war has largely dismissed the role of political and economic grievances, focusing instead on opportunities for conflict. However, these strong claims rest on questionable theoretical and empirical grounds. Whereas scholars have examined primarily the relationship between individual inequality and conflict, we argue that horizontal inequalities between politically relevant ethnic groups and states at large can promote ethnonationalist conflict. Extending the empirical scope to the entire world, this article introduces a new spatial method that combines our newly geocoded data on ethnic groups’ settlement areas with spatial wealth estimates. Based on these methodological advances, we find that, in highly unequal societies, both rich and poor groups fight more often than those groups whose wealth lies closer to the country average. Our results remain robust to a number of alternative sample definitions and specifications.</jats:p

The science of psychoanalysis

For psychoanalysis to qualify as scientific psychology, it needs to generate data that can evidentially support theoretical claims. Its methods, therefore, must at least be capable of correcting for biases produced in the data during the process of generating it; and we must be able to use the data in sound forms of inference and reasoning. Critics of psychoanalysis have claimed that it fails on both counts, and thus whatever warrant its claims have derive from other sources. In this article, I discuss three key objections, and then consider their implications together with recent developments in the generation and testing of psychoanalytic theory. The first and most famous is that of ‘suggestion’; if it sticks, clinical data may be biased in a way that renders all inferences from them unreliable. The second, sometimes confused with the first, questions whether the data are or can be used to provide genuine tests of theoretical hypotheses. The third will require us to consider the question of how psychology can reliably infer motives from behavior. I argue that the clinical method of psychoanalysis is defensible against these objections in relation to the psychodynamic model of mind, but not wider metapsychological and etiological claims. Nevertheless, the claim of psychoanalysis to be a science would be strengthened if awareness of the methodological pitfalls and means to avoid them, and alternative theories and their evidence bases, were more widespread. This may require changes in the education of psychoanalysts

Which causal structures might support a quantum-classical gap?

A causal scenario is a graph that describes the cause and effect relationships between all relevant variables in an experiment. A scenario is deemed `not interesting' if there is no device-independent way to distinguish the predictions of classical physics from any generalised probabilistic theory (including quantum mechanics). Conversely, an interesting scenario is one in which there exists a gap between the predictions of different operational probabilistic theories, as occurs for example in Bell-type experiments. Henson, Lal and Pusey (HLP) recently proposed a sufficient condition for a causal scenario to not be interesting. In this paper we supplement their analysis with some new techniques and results. We first show that existing graphical techniques due to Evans can be used to confirm by inspection that many graphs are interesting without having to explicitly search for inequality violations. For three exceptional cases -- the graphs numbered 15,16,20 in HLP -- we show that there exist non-Shannon type entropic inequalities that imply these graphs are interesting. In doing so, we find that existing methods of entropic inequalities can be greatly enhanced by conditioning on the specific values of certain variables.Comment: 13 pages, 9 figures, 1 bicycle. Added an appendix showing that e-separation is strictly more general than the skeleton method. Added journal referenc

Counterfactual Causality from First Principles?

In this position paper we discuss three main shortcomings of existing approaches to counterfactual causality from the computer science perspective, and sketch lines of work to try and overcome these issues: (1) causality definitions should be driven by a set of precisely specified requirements rather than specific examples; (2) causality frameworks should support system dynamics; (3) causality analysis should have a well-understood behavior in presence of abstraction.Comment: In Proceedings CREST 2017, arXiv:1710.0277