Some philosophers argue that tying scientific understanding to explanation and truth generates a dilemma for a realist view of science: given the practice and the history of science, either we should give up the idea that understanding requires truth, or we should accept that we do not have scientific understanding. Given, we were told, that the latter horn is repugnant, we should jettison the first horn and disconnect understanding and truth. In this paper, I argue that the alleged dilemma for realism is a non-starter. We can accept both that understanding requires truth and claim that theories offer understanding. I argue that understanding and explanation in science go hand in hand, and that—for a realist at least—they should both be world-involving.

Science is increasingly interdisciplinary, as evidenced by empirical measures, funding initiatives, and the rise of integrative fields such as systems biology and cognitive neuroscience. In this paper, I motivate and outline an account of explanation for interdisciplinary contexts, building on recent debates about scientific perspectivism. Insights from these debates yield an inclusive list of relations between models constructed from different perspectives, which I then refine and generalize into a simple taxonomy. Within this taxonomy of relations among models, I identify the set of relations applicable to interdisciplinary contexts, discuss concepts of unification associated with each, and introduce three further constraints which furnish norms for this variety of explanation. Finally, I discuss implications of this account for a recent debate about understanding and explanation. One important consequence of my view is that explanation in interdisciplinary contexts and understanding of individual agents in those contexts are not equivalent.

Factivism is the view that understanding why a natural phenomenon takes place must rest exclusively on (approximate) truths. One of the arguments for nonfactivism—the opposite view, that falsehoods can play principal roles in producing understanding—relies on our inclination to say that past, false, now superseded but still important scientific theories (such as Newtonian mechanics) do provide understanding. In this paper, my aim is to articulate what I take to be an interesting point that has yet to be discussed: the natural way in which nonfactivism fits within the unificationist account of scientific explanation. I contend that unificationism gives non-factivists a better framework to uphold their position. After I show why this is so, toward the end of the paper I will express doubts with regard to the viability of de Regt’s (2015) kind of non-factivism, based on the idea that understanding should be captured in terms of (scientific) skill.

The central argument in this paper is the following: if we agree that one of the aims of explanation is to provide or increase understanding, and if we assess understanding on the basis of the inferences one can draw from the knowledge of the phenomenon which is understood, then the value of an explanation, i.e. its capacity to provide or increase understanding of the explained phenomenon, should be assessed on the basis of the extra-inferences which this explanation allows for. The extra-inferences which a given explanation allows for constitute its inferential content. The analysis of the explanation’s inferential content could be applied to all kinds of explanations with the aim of assessing their goodness. I show how such an analysis helps us to better understand a number of difficulties that have puzzled contemporary philosophers of explanation: the flagpole counterexample to the deductive-nomological model of explanation, the conjunction problem, the difference between good and bad circular explanations.

The aim of this paper is to argue that it is more plausible to approach understanding from a special type of model—the ABS/IBMs models—as a non-explanatory form following some suggestions advanced by Lipton. I will first look to the type of explanation that some authors claimed is disclosed by these models: Weisberg’s analysis of IBMs in ecology and Grüne-Yanoff’s analysis of the Anasazi model. I argue that their analyses fail to show that these models qualify as explanatory understandings. This brings us to Strevens’ “simple view,” which claims the existence of a correct explanation behind any understanding, and his strategy of dismissing the challenges posed by non-explanatory forms. I argue that this strategy incurs damaging costs on his view. In the last part we turn to Khalifa’s critique on Lipton’s proposals and argue that it is based on an unjustified construal of Lipton’s framework. I show how Khalifa’s “argumentative strategy” fails to establish the superiority of actual understanding over possible explanation.

A phenome occurs through the many pathways of the complex net of interaction between the phenome and its environment; therefore researching and understanding how it arises requires investigation into many possible causes that are in constant interaction with each other. The most comprehensive investigations in biology are the ones in which many biologists from different sub-areas—evolutionary biology, developmental biology, molecular biology, physiology, genetics, epigenetics, ecology—have collaborated. Still, biologists do not always need to collaborate or look for the most comprehensive explanations. A more standard investigation in biology occurs within a single subarea, and uses well-defined experiments with very specific conditions. This paper is about causation and related explanation in plant phenome research and its relevance to Aristotle’s Theory of Four Causes. I argue that there are causes which resemble Aristotle’s formal, material, and efficient causes in phenotype explanation and occurrence; but causes which resemble Aristotle’s final causes occur in phenotype explanation only, not in the occurrence.

The Principle of Common Cause (PCC) puts forward the idea that events which occur simultaneously and are correlated have a prior common cause which screens off the correlation. I endorse the view that the PCC does qualify as a principle that can be used as a tool in explaining improbable coincidences. However, though there are epistemological advantages in common cause explanations of correlated events, the PCC is not impeccable. This paper offers a preliminary assessment of the PCC advocated by Reichenbach, and then attempts to illustrate three scenarios in which the principle might be inadequate in explaining correlated events. The paper also compares the Common Cause Principle and the Causal Markov Condition (CMC), and examines the advantages of CMC over the Common Cause Principle.