Chapter 1 FROM ORDER TO DISORDER 5 mins. John enters and goes into his office. He says something very quickly about having made a bad mistake. He had sent the review of a paper. . . . The rest of the sentence is inaudible. 5 mins.

Woodward's long awaited book is an attempt to construct a comprehensive account of causation explanation that applies to a wide variety of causal and explanatory claims in different areas of science and everyday life. The book engages some of the relevant literature from other disciplines, as Woodward weaves together examples, counterexamples, criticisms, defenses, objections, and replies into a convincing defense of the core of his theory, which is that we can analyze causation by appeal to the notion of manipulation.

This book presents the concept of “complementary science” which contributes to scientific knowledge through historical and philosophical investigations. It emphasizes the fact that many simple items of knowledge that we take for granted were actually spectacular achievements obtained only after a great deal of innovative thinking, painstaking experiments, bold conjectures, and serious controversies. Each chapter in the book consists of two parts: a narrative part that states the philosophical puzzle and gives a problem-centred narrative on the historical attempts to solve (...) the puzzle; and the analysis part which provides in-depth analyses of certain scientific, historical, and philosophical aspects of the story. (shrink)

This essay uses Györgi Buzsáki's use of EEG data to draw conclusions about brain function as an example to show that investigators sometimes draw conclusions from noisy data by analyzing the noise rather than by extracting a signal from it. The example makes vivid some important differences between McAllister's, Woodward's, and my ideas about how data are interpreted.

Rasmussen (1993) argues that, because electron microscopists did not use robustness and would not have been warranted in using it as a criterion for the reality or the artifactuality of mesosomes, the bacterial mesosome serves as a test case for robustness that it fails. I respond by arguing that a more complete reading of the research literature on the mesosome shows that ultimately the more robust body of data did not support the mesosome and that electron microscopists used and were (...) warranted in using robustness as a criterion for the artifactuality of mesosomes. (shrink)

When a technique purports to provide information that is not available to the unaided senses, it is natural to think that the only way to validate that technique is by appealing to a theory of the processes that lead from the object of study to the raw data. In fact, scientists have a variety of strategies for validating their techniques. Those strategies can yield multiple independent arguments that support the validity of the technique. Thus, it is possible to produce a (...) robust body of data with a single technique. I illustrate and support these claims with a historical case study. *Received September 2009; revised October 2009. †To contact the author, please write to: Department of History and Philosophy of Science, 1017 Cathedral of Learning, University of Pittsburgh, Pittsburgh, PA 15260; e‐mail: [email protected]. (shrink)

With In Search of Mechanisms, Carl F. Craver and Lindley Darden offer both a descriptive and an instructional account of how biologists discover mechanisms. Drawing on examples from across the life sciences and through the centuries, Craver and Darden compile an impressive toolbox of strategies that biologists have used and will use again to reveal the mechanisms that produce, underlie, or maintain the phenomena characteristic of living things. They discuss the questions that figure in the search for mechanisms, characterizing the (...) experimental, observational, and conceptual considerations used to answer them, all the while providing examples from the history of biology to highlight the kinds of evidence and reasoning strategies employed to assess mechanisms. At a deeper level, Craver and Darden pose a systematic view of what biology is, of how biology makes progress, of how biological discoveries are and might be made, and of why knowledge of biological mechanisms is important for the future of the human species. (shrink)

Do neurobiologists aim to discover natural kinds? I address this question in this chapter via a critical analysis of classification practices operative across the 43-year history of research on long-term potentiation. I suggest that this 43-year history supports the idea that the structure of scientific practice surrounding LTP research has remained an obstacle to the discovery of natural kinds as philosophers of science have traditionally conceived them.

This text provides a critique of the subjective Bayesian view of statistical inference, and proposes the author's own error-statistical approach as an alternative framework for the epistemology of experiment. It seeks to address the needs of researchers who work with statistical analysis.

In this paper, I investigate the nature of empirical findings that provide evidence for the characterization of a scientific phenomenon, and the defeasible nature of this evidence. To do so, I explore an exemplary instance of the rejection of a characterization of a scientific phenomenon: memory transfer. I examine the reason why the characterization of memory transfer was rejected, and analyze how this rejection tied to researchers’ failures to resolve experimental issues relating to replication and confounds. I criticize the presentation (...) of the case by Harry Collins and Trevor Pinch, who claim that no sufficient reason was provided to abandon research on memory transfer. I argue that skeptics about memory transfer adopted what I call a defeater strategy, in which researchers exploit the defeasibility of the evidence for a characterization of a phenomenon. (shrink)

In this essay, we examine the use of resting state fMRI data for psychological inferences. We argue that resting state studies hold the paired promises of discovering novel functional brain networks, and of avoiding some of the limitations of task-based fMRI. However, we argue that the very features of experimental design that enable resting state fMRI to support exploratory science also generate a novel confound. We argue that seemingly key features of resting state functional connectivity networks may be artefacts resulting (...) from sampling a ‘mixture distribution’ of diverse brain networks active at different times during the scan. We explore the consequences of this ‘mixture view’ for attempts to theorize about the cognitive or psychological functions of resting state networks, as well as the value of exploratory experiments. (shrink)

This paper is concerned with the problem of experimental error. The prevalent view that experimental errors can be dismissed as a tiresome but trivial blemish on the method of experimentation is criticized. It is stressed that the occurrence of errors in experiments constitutes a permanent feature of the attempt to test theories in the physical world, and this feature deserves proper attention. It is suggested that a classification of types of experimental error may be useful as a heuristic device in (...) studying the nature of these errors. However, the standard classification of systematic and random errors is mathematically based does not focus on the causes of the errors, their origins, or the contexts in which they arise. A new typology of experimental errors is therefore proposed whose criterion is epistemological. This typology reflects the various stages that can be discerned in the execution of an experiment, each stage constituting a category of a certain type of experimental error. The proposed classification consists of four categories which are illustrated by historical cases. (shrink)

(No abstract is available for this citation).

Bayesian probabilistic explication of inductive inference conflates neutrality of supporting evidence for some hypothesis H (“not supporting H”) with disfavoring evidence (“supporting not-H”). This expressive inadequacy leads to spurious results that are artifacts of a poor choice of inductive logic. I illustrate how such artifacts have arisen in simple inductive inferences in cosmology. In the inductive disjunctive fallacy, neutral support for many possibilities is spuriously converted into strong support for their disjunction. The Bayesian “doomsday argument” is shown to rely entirely (...) on a similar artifact, for the result disappears in a reanalysis that employs fragments of inductive logic able to represent evidential neutrality. Finally, the mere supposition of a multiverse is not yet enough to warrant the introduction of probabilities without some factual analog of a randomizer over the multiverses. (shrink)

Controversies in economics often fizzle out unresolved. One reason is that, despite their professed empiricism, economists find it hard to agree on the interpretation of the relevant empirical evidence. In this paper I will present an example of a controversial issue first raised and then solved by recourse to laboratory experimentation. A major theme of this paper, then, concerns the methodological advantages of controlled experiments. The second theme is the nature of experimental artefacts and of the methods devised to detect (...) them. Recent studies of experimental science have stressed that experimenters are often merely concerned about determining whether a certain phenomeonon exists or not, or whether, when, and where it can be produced, without necessarily engaging in proving or disproving any theoretical explanation of the phenomenon itself. In this paper I shall be concerned mainly with such a case, and focus on the example of preference reversals, a phenomenon whose existence was until quite recently denied by the majority of economists. Their favourite strategy consisted in trying to explain the phenomenon away as an artefact of the experimental techniques used to observe it. By controlled experimentation, as we shall see, such an interpretation has been discredited, and now preference reversals are generally accepted as real. The problem of distinguishing an artefact from a real phenomenon is related to methodological issues traditionally discussed by philosophers of science, such as the theory-ladenness of observation and Duhem's problem. Part of this paper is devoted to clarifying these two philosophical problems, and to arguing that only the latter is relevant to the case in hand. The solutions to Duhem's problem devised by economic experimentalists will be presented and discussed. I shall show that they belong in two broad categories: independent tests of new predictions derived from the competing hypotheses at stake, and ‘no- miracle arguments’ from different experimental techniques delivering converging results despite their being theoretically independent. (shrink)

Bogen and Woodward claim that the function of scientific theories is to account for 'phenomena', which they describe both as investigator-independent constituents of the world and as corresponding to patterns in data sets. I argue that, if phenomena are considered to correspond to patterns in data, it is inadmissible to regard them as investigator-independent entities. Bogen and Woodward's account of phenomena is thus incoherent. I offer an alternative account, according to which phenomena are investigator-relative entities. All the infinitely many patterns (...) that data sets exhibit have equal intrinsic claim to the status of phenomenon: each investigator may stipulate which patterns correspond to phenomena for him or her. My notion of phenomena accords better both with experimental practice and with the historical development of science. (shrink)

expose some gaps and difficulties in the argument for the causal Markov condition in our essay ‘Independence, Invariance and the Causal Markov Condition’ ([1999]), and we are grateful for the opportunity to reformulate our position. In particular, Cartwright disagrees vigorously with many of the theses we advance about the connection between causation and manipulation. Although we are not persuaded by some of her criticisms, we shall confine ourselves to showing how our central argument can be reconstructed and to casting doubt (...) on Cartwright's claim that the causal Markov condition typically fails when there are indeterministic by-products. Why believe the causal Markov condition? Causation and manipulation The argument Indeterministic by-products and the causal Markov condition The chemical factory counterexample and PM2 Conclusions: causation and manipulability. (shrink)

Judea Pearl has been at the forefront of research in the burgeoning field of causal modeling, and Causality is the culmination of his work over the last dozen or so years. For philosophers of science with a serious interest in causal modeling, Causality is simply mandatory reading. Chapter 2, in particular, addresses many of the issues familiar from works such as Causation, Prediction and Search by Peter Spirtes, Clark Glymour, and Richard Scheines. But philosophers with a more general interest in (...) causation will also profit from reading Pearl’s book, especially the material in chapters 7, 9, and 10, which is self-contained and less technical than other parts of the book. The present review is aimed primarily at readers of the second type. (shrink)

In a 1993 paper, I argued that empirical treatments of the epistemologyused by scientists in experimental work are too abstract in practice tocounter relativist efforts to explain the outcome of scientificcontroversies by reference to sociological forces. This was because, atthe rarefied level at which the methodology of scientists is treated byphilosophers, multiple mutually inconsistent instantiations of theprinciples described by philosophers are employed by contestingscientists. These multiple construals change within a scientificcommunity over short time frames, and these different versions ofscientific methodology (...) can determine the outcome of a controversy. Iillustrated with a comparatively detailed analysis of the methodologyused by biologists debating the existence of an entity called thebacterial mesosome between the mid-1950s and the mid-1970s. This 1993piece has drawn several critiques in the philosophical literature. Inthis present piece I respond to these critiques and argue that they failto address the core argument of the original paper, and I reflectfurther on the methodologies of philosophers of science pursuingempirical or `naturalistic' epistemology. (shrink)

Long-Term Potentiation (LTP) is a kind of synaptic plasticity that many contemporary neuroscientists believe is a component in mechanisms of memory. This essay describes the discovery of LTP and the development of the LTP research program. The story begins in the 1950's with the discovery of synaptic plasticity in the hippocampus (a medial temporal lobe structure now associated with memory), and it ends in 1973 with the publication of three papers sketching the future course of the LTP research program. The (...) making of LTP was a protracted affair. Hippocampal synaptic plasticity was initially encountered as an experimental tool, then reported as a curiosity, and finally included in the ontic store of the neurosciences. Early researchers were not investigating the hippocampus in search of a memory mechanism; rather, they saw the hippocampus as a useful experimental model or as a structure implicated in the etiology of epilepsy. The link between hippocampal synaptic plasticity and learning or memory was a separate conceptual achievement. That link was formulated in at least three different ways at different times: reductively (claiming that plasticity is identical to learning), analogically (claiming that plasticity is an example or model of learning), and mechanistically (claiming that plasticity is a component in learning or memory mechanisms). The hypothesized link with learning or memory, coupled with developments in experimental techniques and preparations, shaped how researchers understood LTP itself. By 1973, the mechanistic formulation of the link between LTP and memory provided an abstract framework around which findings from multiple perspectives could be integrated into a multifield research program. (shrink)

Hunting Causes and Using Them argues that causation is not one thing, as commonly assumed, but many. There is a huge variety of causal relations, each with different characterizing features, different methods for discovery and different uses to which it can be put. In this collection of new and previously published essays, Nancy Cartwright provides a critical survey of philosophical and economic literature on causality, with a special focus on the currently fashionable Bayes-nets and invariance methods - and it exposes (...) a huge gap in that literature. Almost every account treats either exclusively how to hunt causes or how to use them. But where is the bridge between? It's no good knowing how to warrant a causal claim if we don't know what we can do with that claim once we have it. This book will interest philosophers, economists and social scientists. (shrink)

This essay attempts to provide additional motivation for the data/phenomena framework advocated in Bogen and Woodward, “Saving the Phenomena”.

The world is complex, but acknowledging its complexity requires an appreciation for the many roles context plays in shaping natural phenomena. In _Unsimple Truths, _Sandra Mitchell argues that the long-standing scientific and philosophical deference to reductive explanations founded on simple universal laws, linear causal models, and predict-and-act strategies fails to accommodate the kinds of knowledge that many contemporary sciences are providing about the world. She advocates, instead, for a new understanding that represents the rich, variegated, interdependent fabric of many levels (...) and kinds of explanation that are integrated with one another to ground effective prediction and action. Mitchell draws from diverse fields including psychiatry, social insect biology, and studies of climate change to defend “integrative pluralism”—a theory of scientific practices that makes sense of how many natural and social sciences represent the multi-level, multi-component, dynamic structures they study. She explains how we must, in light of the now-acknowledged complexity and contingency of biological and social systems, revise how we conceptualize the world, how we investigate the world, and how we act in the world. Ultimately _Unsimple Truths _argues that the very idea of what should count as legitimate science itself should change. (shrink)

Examines the history and inherent flaws of the tests science has used to measure intelligence.

Once upon a time, logic was the philosopher’s tool for analyzing scientific reasoning. Nowadays, probability and statistics have largely replaced logic, and their most popular application—Bayesianism—has replaced the qualitative deductive relationship between a hypothesis h and evidence e with a quantitative measure of h’s probability in light of e.