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In climate science, climate models are one of the main tools for understanding phenomena. Here, we develop a framework to assess the fitness of a climate model for providing understanding. The framework is based on three dimensions representational accuracy, representational depth, and graspability. We show that this framework does justice to the intuition that classical process-based climate models give understanding of phenomena. While simple climate models are characterized by a larger graspability, state-of-the-art models have a higher representational accuracy and representational depth. We then compare the fitness-for-providing understanding of process-based to data-driven models that are built with machine learning. We show that at first glance, data-driven models seem either unnecessary or inadequate for understanding. However, a case study from atmospheric research demonstrates that this is a false dilemma. Data-driven models can be useful tools for understanding, specifically for phenomena for which scientists can argue from the coherence of the models with background knowledge to their representational accuracy and for which the model complexity can be reduced such that they are graspable to a satisfactory extent.This paper investigates the case of enzyme classification to evaluate different ideals for regulating values in science. I show that epistemic and non-epistemic considerations are inevitably and untraceably entangled in enzyme classification, and argue that this has significant implications for the two main kinds of views on values in science, namely, Epistemic Priority Views and Joint Satisfaction Views. More precisely, I argue that the case of enzyme classification poses a problem for the usability and descriptive accuracy of these two views. The paper ends by suggesting that these two views provide different but complementary perspectives, and that both are useful for evaluating values in science.As an application of his Material Theory of Induction, Norton (2018; manuscript) argues that the correct inductive logic for a fair infinite lottery, and also for evaluating eternal inflation multiverse models, is radically different from standard probability theory. This is due to a requirement of label independence. It follows, Norton argues, that finite additivity fails, and any two sets of outcomes with the same cardinality and co-cardinality have the same chance. This makes the logic useless for evaluating multiverse models based on self-locating chances, so Norton claims that we should despair of such attempts. However, his negative results depend on a certain reification of chance, consisting in the treatment of inductive support as the value of a function, a value not itself affected by relabeling. Here we define a purely comparative infinite lottery logic, where there are no primitive chances but only a relation of 'at most as likely' and its derivatives. This logic satisfies both label independence and a comparative version of additivity as well as several other desirable properties, and it draws finer distinctions between events than Norton's. Consequently, it yields better advice about choosing between sets of lottery tickets than Norton's, but it does not appear to be any more helpful for evaluating multiverse models. Dolutegravir Hence, the limitations of Norton's logic are not entirely due to the failure of additivity, nor to the fact that all infinite, co-infinite sets of outcomes have the same chance, but to a more fundamental problem We have no well-motivated way of comparing disjoint countably infinite sets.In this paper, we present an explanatory objection to Norton's material theory of induction, as applied to predictive inferences. According to the objection we present, there is an explanatory disconnect between our beliefs about the future and the relevant future facts. We argue that if we recognize such a disconnect, we are no longer rationally entitled to our future beliefs.We start by reviewing the complicated situation in methods of scientific attribution of climate change to extreme weather events. We emphasize the social values involved in using both so-called ″storyline″ and ordinary probabilistic or ″risk-based″ methods, noting that one important virtue claimed by the storyline approach is that it features a reduction in false negative results, which has much social and ethical merit, according to its advocates. This merit is critiqued by the probabilistic, risk-based, opponents, who claim the high ground; the usual probabilistic approach is claimed to be more objective and more ″scientific″, under the grounds that it reduces false positive error. We examine this mostly-implicit debate about error, which apparently mirrors the old Jeffrey-Rudner debate. We also argue that there is an overlooked component to the role of values in science that of second-order inductive risk, and that it makes the relative role of values in the two methods different from what it first appears to be. In fact, neither method helps us to escape social values, and be more scientifically ″objective″ in the sense of being removed or detached from human values and interests. The probabilistic approach does not succeed in doing so, contrary to the claims of its proponents. This is important to understand, because neither method is, fundamentally, a successful strategy for climate scientists to avoid making value judgments.Intellectual legacies are part of historians' concerns, when they study the evolution of ideas. There are, however, no guidelines to help characterize the reception of intellectual legacies. This article provides preliminary tools to fill this gap, with a typology (faithful, formal, substantial legacies), and with two criteria to assess the conformity between the heir's and her inspirer's proposals. The objective is not to judge the legitimacy of this or that reception, but to facilitate its characterization, for a better understanding of the transmission of ideas. One case study from the history of economic thought, Nicholas Georgescu-Roegen's bioeconomics and its legacies, is provided to illustrate the operability of the toolbox.