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Scientific Knowledge and the Deep Past: History Matters is an accessible tour of the philosophy of the historical sciences. Examining how dinosaurs grew and the emergence of flowering plants in the mid-Cretaceous, it analyses the relationship between knowledge and the past, explaining why narratives play a prominent role in history.

This Element offers an historically informed review of the philosophy of probability and an analytical focus on objective probability. A distinction is drawn between traditional attempts to interpret chance, and a novel methodological study of its application, introducing a radical form of pluralism and exploring this in statistical modelling.

What kind of reductionism does multiple realisability challenge? What does it take to reduce one phenomenon to another? How do we determine which kinds are natural? What is the ontological basis of unity? In this Element, Tuomas Tahko examines these questions from a contemporary perspective, after a historical overview.

Jonathan Y. Tsou examines and defends positions on central issues in philosophy of psychiatry. The positions defended assume a naturalistic and realist perspective and are framed against skeptical perspectives on biological psychiatry.

This Element provides an accessible introduction to the contemporary philosophy of causation. It introduces the reader to central concepts and distinctions and to key tools drawn upon in the contemporary debate. The aim is to fuel the reader's interest in causation, and to equip them with the resources to contribute to the debate themselves.

Objectivity is a key concept both in how we talk about science in everyday life and in the philosophy of science. This Element explores various ways in which recent philosophers of science have thought about the nature, value and achievability of objectivity.

Explores the Bayesian approach to the logic and epistemology of scientific reasoning. It introduces the probability calculus as an appealing generalization of classical logic for uncertain reasoning, explores Bayesian epistemology and applies the formal tools and principles to a handful of topics in the epistemology of scientific reasoning.

This Element introduces the reader to the concept of grounding and some of the key issues that animate contemporary debates around it, such as the question of whether grounding is 'unified' or 'plural' and whether there exists a fundamental level of reality.

The holistic thesis developed by Pierre Duhem challenges the idea that our evidence can conclusively falsify a theory. Given that no scientific theory is tested in isolation, a negative experiment can always be attributed to components other than the theory we test - to the auxiliary hypotheses and background assumptions. How do scientists decide whether the experimental result undermines the theory or points at an error in the underlying assumptions? Duhem argues that we cannot offer a rule that directs when the scientist should employ a radical or conservative strategy in light of a negative result, and ultimately they will appeal to their intuition. More recently philosophers have offered a number of strategies of how to locate error and justify the abandonment of a theory or an auxiliary hypothesis. This Element analyses Duhem's response to holism and subsequent accounts of how the problem can be resolved.

Big Data and methods for analyzing large data sets such as machine learning have in recent times deeply transformed scientific practice in many fields. However, an epistemological study of these novel tools is still largely lacking. After a conceptual analysis of the notion of data and a brief introduction into the methodological dichotomy between inductivism and hypothetico-deductivism, several controversial theses regarding big data approaches are discussed. These include, whether correlation replaces causation, whether the end of theory is in sight and whether big data approaches constitute entirely novel scientific methodology. In this Element, I defend an inductivist view of big data research and argue that the type of induction employed by the most successful big data algorithms is variational induction in the tradition of Mill's methods. Based on this insight, the before-mentioned epistemological issues can be systematically addressed.

'Relativism versus absolutism' is one of the fundamental oppositions that have dominated reflections about science for much of its (modern) history. Often these reflections have been inseparable from wider social-political concerns regarding the position of science in society. Where does this debate stand in the philosophy and sociology of science today? And how does the 'relativism question' relate to current concerns with 'post truth' politics? In Relativism in the Philosophy of Science, Martin Kusch examines some of the most influential relativist proposals of the last fifties years, and the controversies they have triggered. He argues that defensible forms of relativism all deny that any sense can be made of a scientific result being absolutely true or justified, and that they all reject 'anything goes' - that is the thought that all scientific results are epistemically on a par. Kusch concludes by distinguishing between defensible forms of relativism and post-truth thinking.

This Element presents a philosophical exploration of the notion of scientific representation. It does so by focussing on an important class of scientific representations, namely scientific models. Models are important in the scientific process because scientists can study a model to discover features of reality. But what does it mean for something to represent something else? This is the question discussed in this Element. The authors begin by disentangling different aspects of the problem of representation and then discuss the dominant accounts in the philosophical literature: the resemblance view and inferentialism. They find them both wanting and submit that their own preferred option, the so-called DEKI account, not only eschews the problems that beset these conceptions, but further provides a comprehensive answer to the question of how scientific representation works. This title is also available as Open Access on Cambridge Core.

Scientists cannot devise theories, construct models, propose explanations, make predictions, or even carry out observations, without first classifying their subject matter. The goal of scientific taxonomy is to come up with classification schemes that conform to nature's own. Another way of putting this is that science aims to devise categories that correspond to 'natural kinds.' The interest in ascertaining the real kinds of things in nature is as old as philosophy itself, but it takes on a different guise when one adopts a naturalist stance in philosophy, that is when one looks closely at scientific practice and takes it as a guide for identifying natural kinds and investigating their general features. This Element surveys existing philosophical accounts of natural kinds, defends a naturalist alternative, and applies it to case studies in a diverse set of sciences. This title is also available as Open Access on Cambridge Core.