Geologists, Paleontologists and other historical scientists are frequently concerned with narrative explanations targeting single cases. I show that two distinct explanatory strategies are employed in narratives, simple and complex. A simple narrative has minimal causal detail and is embedded in a regularity, whereas a complex narrative is more detailed and not embedded. The distinction is illustrated through two case studies: the ‘snowball earth’ explanation of Neoproterozoic glaciation and recent attempts to explain gigantism in Sauropods. This distinction is revelatory of historical (...) science. I argue that at least sometimes which strategy is appropriate is not a pragmatic issue, but turns on the nature of the target. Moreover, the distinction reveals a counterintuitive pattern of progress in some historical explanation: shifting from simple to complex. Sometimes, historical scientists rightly abandon simple, unified explanations in favour of disunified, complex narratives. Finally I compare narrative and mechanistic explanation, arguing that mechanistic approaches are inappropriate for complex narrative explanations. (shrink)

According to a causal model of explanation, we explain phenomena by giving their causes or, where the phenomena are themselves causal regularities, we explain them by giving a mechanism linking cause and effect. If we explain why smoking causes cancer, we do not give the cause of this causal connection, but we do give the causal mechanism that makes it. The claim that to explain is to give a cause is not only natural and plausible, but it also avoids many (...) of the objections to other accounts of explanation, such as the views that to explain is to give a reason to believe the phenomenon occurred, to somehow make the phenomenon familiar, or to give a Deductive-Nomological argument. Unlike the reason for belief account, a causal model makes a clear distinction between understanding why a phenomenon occurs and merely knowing that it does, and the model does so in a way that makes understanding unmysterious and objective. Understanding is not some sort of super-knowledge, but simply more knowledge: knowledge of the phenomenon and knowledge of its causal history. A causal model makes it clear how something can explain without itself being explained, and so avoids the regress of whys, since we can know a phenomenon's cause without knowing the cause of the cause. It also accounts for legitimate self-evidencing explanations, explanations where the phenomenon is an essential part of the evidence that the explanation is correct, so the explanation can not supply a non-circular reason for believing the phenomenon occurred. There is no barrier to knowing a cause through its effects and also knowing that it is their cause. The speed of recession of a star explains its observed red-shift, even though the shift is an essential part of the evidence for its speed of recession. The model also avoids the most serious objection to the familiarity view, which is that some phenomena are familiar yet not understood, since a phenomenon can be perfectly familiar, such as the blueness of the sky or the fact that the same side of the moon always faces the earth, even if we do not know its cause. Finally, a causal model avoids many of the objections to the Deductive-Nomological model. Ordinary explanations do not have to meet the requirements of the Deductive-Nomological model, because one does not need to give a law to give a cause, and one does not need to know a law to have good reason to believe that a cause is a cause. As for the notorious over-permissiveness of the Deductive-Nomological model, the reason recession explains red-shift but not conversely, is simply that causes explain effects but not conversely, and the reason a conjunction of laws does not explain its conjuncts is that conjunctions do not cause their conjuncts. (shrink)

Philosophers of science increasingly recognize the importance of idealization: the intentional introduction of distortion into scientific theories. Yet this recognition has not yielded consensus about the nature of idealization. e literature of the past thirty years contains disparate characterizations and justifications, but little evidence of convergence towards a common position.

Some have argued that chance and determinism are compatible in order to account for the objectivity of probabilities in theories that are compatible with determinism, like Classical Statistical Mechanics (CSM) and Evolutionary Theory (ET). Contrarily, some have argued that chance and determinism are incompatible, and so such probabilities are subjective. In this paper, I argue that both of these positions are unsatisfactory. I argue that the probabilities of theories like CSM and ET are not chances, but also that they are (...) not subjective probabilities either. Rather, they are a third type of probability, which I call counterfactual probability. The main distinguishing feature of counterfactual-probability is the role it plays in conveying important counterfactual information in explanations. This distinguishes counterfactual probability from chance as a second concept of objective probability. (shrink)

This paper attempts to elucidate three characteristics of causal relationships that are important in biological contexts. Stability has to do with whether a causal relationship continues to hold under changes in background conditions. Proportionality has to do with whether changes in the state of the cause “line up” in the right way with changes in the state of the effect and with whether the cause and effect are characterized in a way that contains irrelevant detail. Specificity is connected both to (...) David Lewis’ notion of “influence” and also with the extent to which a causal relation approximates to the ideal of one cause–one effect. Interrelations among these notions and their possible biological significance are also discussed. (shrink)

Approaches to explanation -- Causal and explanatory relevance -- The kairetic account of /D making -- The kairetic account of explanation -- Extending the kairetic account -- Event explanation and causal claims -- Regularity explanation -- Abstraction in regularity explanation -- Approaches to probabilistic explanation -- Kairetic explanation of frequencies -- Kairetic explanation of single outcomes -- Looking outward -- Looking inward.

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.

outlined. This account is partly inspired by the work of C.S. Peirce. When we want to consider how degree of confirmation varies with changing I show that a large class of quantitative Bayesian measures of con-.

Experimental research is commonly held up as the paradigm of "good" science. Although experiment plays many roles in science, its classical role is testing hypotheses in controlled laboratory settings. Historical science is sometimes held to be inferior on the grounds that its hypothesis cannot be tested by controlled laboratory experiments. Using contemporary examples from diverse scientific disciplines, this paper explores differences in practice between historical and experimental research vis-à-vis the testing of hypotheses. It rejects the claim that historical research is (...) epistemically inferior. For as I argue, scientists engage in two very different patterns of evidential reasoning and, although there is overlap, one pattern predominates in historical research and the other pattern predominates in classical experimental research. I show that these different patterns of reasoning are grounded in an objective and remarkably pervasive time asymmetry of nature. (shrink)

Scientists often make surprising claims about things that no one can observe. In physics, chemistry, and molecular biology, scientists can at least experiment on those unobservable entities, but what about researchers in fields such as paleobiology and geology who study prehistory, where no such experimentation is possible? Do scientists discover facts about the distant past or do they, in some sense, make prehistory? In this book Derek Turner argues that this problem has surprising and important consequences for the scientific realism (...) debate. His discussion covers some of the main positions in philosophy of science - realism, social constructivism, empiricism, and the natural ontological attitude - and shows how they relate to issues in paleobiology and geology. His original and thought-provoking book will be of wide interest to philosophers and scientists alike. (shrink)

The historical sciences, such as geology, evolutionary biology, and archaeology, appear to have no means to test hypotheses. However, on closer examination, reasoning in the historical sciences relies upon regularities, regularities that can be tested. I outline the role of regularities in the historical sciences, and in the process, blur the distinction between the historical sciences and the experimental sciences: all sciences deploy theories about the world in their investigations.

Reconstructing the Past seeks to clarify and help resolve the vexing methodological issues that arise when biologists try to answer such questions as whether human beings are more closely related to chimps than they are to gorillas. It explores the case for considering the philosophical idea of simplicity/parsimony as a useful principle for evaluating taxonomic theories of evolutionary relationships. For the past two decades, evolutionists have been vigorously debating the appropriate methods that should be used in systematics, the field that (...) aims at reconstructing phylogenetic relationships among species. This debate over phylogenetic inference, Elliott Sober observes, raises broader questions of hypothesis testing and theory evaluation that run head on into long standing issues concerning simplicity/parsimony in the philosophy of science. Sober treats the problem of phylogenetic inference as a detailed case study in which the philosophical idea of simplicity/parsimony can be tested as a principle of theory evaluation. Bringing together philosophy and biology, as well as statistics, Sober builds a general framework for understanding the circumstances in which parsimony makes sense as a tool of phylogenetic inference. Along the way he provides a detailed critique of parsimony in the biological literature, exploring the strengths and limitations of both statistical and nonstatistical cladistic arguments. (shrink)

The vagaries of evidential reasoning in archaeology are notorious: the material traces that comprise the archaeological record are fragmentary and profoundly enigmatic, and the inferential gap that archaeologists must cross to constitute them as evidence of the cultural past is a peren­nial source of epistemic anxiety. And yet we know a great deal about the cultural past, including vast reaches of the past for which this material record is our only source of evidence. The contents of this record stand as (...) evidence only under interpretation, but however much a construct it is, archaeological evidence has a striking capacity to disrupt settled assump­tions, redirecting inquiry and expanding interpretive horizons in direc­tions no one could have anticipated. It is this capacity for constraining inference and interpretation that I am concerned to understand. I outline a model of evidential reasoning based on archaeological practice that integrates insights drawn from philosophical theories of confirmation, model building and hypothesis testing. Given growing interest in the uses of material evidence in fields that had been resolutely text-based, the archaeological principles of evidential reasoning may have much wider reach than this particular social/historical discipline. (shrink)

This article deals with a type of functional explanation, viability explanation, that has been overlooked in recent philosophy of science. Viability explanations relate traits of organisms and their environments in terms of what an individual needs to survive and reproduce. I show that viability explanations are neither causal nor historical and that, therefore, they should be accounted for as a distinct type of explanation.

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, defences, objections, and replies into a convincing defence of the core of his theory, which is that we can analyse causation by appeal to the notion of manipulation.

Biologists studying complex causal systems typically identify some factors as causes and treat other factors as background conditions. For example, when geneticists explain biological phenomena, they often foreground genes and relegate the cellular milieu to the background. But factors in the milieu are as causally necessary as genes for the production of phenotypic traits, even traits at the molecular level such as amino acid sequences. Gene-centered biology has been criticized on the grounds that because there is parity among causes, the (...) “privileging” of genes reflects a reductionist bias, not an ontological difference. The idea that there is an ontological parity among causes is related to a philosophical puzzle identified by John Stuart Mill: what, other than our interests or biases, could possibly justify identifying some causes as the actual or operative ones, and other causes as mere background? The aim of this paper is to solve this conceptual puzzle and to explain why there is not an ontological parity among genes and the other factors. It turns out that solving this puzzle helps answer a seemingly unrelated philosophical question: what kind of causal generality matters in biology? (shrink)

The epistemology of the historical sciences has been debated recently. Cleland argued that the effects of the past overdetermine it. Turner argued that the past is underdetermined by its effects because of the decay of information from the past. I argue that the extent of over- and underdetermination cannot be approximated by philosophical inquiry. It is an empirical question that each historical science attempts to answer. Philosophers should examine how paradigmatic cases of historical science handled underdetermination or utilized overdetermination. I (...) analyze such a paradigmatic case, Darwin’s phylogenetic inferences. Darwin proceeded in three consecutive stages. The initial inference that there was some common cause of homologies was usually overdetermined. The final inference of the character traits of ancestor species was usually underdetermined. The second stage inference of the causal net that connected the species that share some common cause was inbetween. A comparison with Comparative Historical Linguistics demonstrates similar three stages of inference that move from the over- to the underdetermined. (shrink)

Biologists in the last 50 years have increasingly emphasized the role of historical contingency in explaining the distribution and dynamics of biological systems. However, recent work in philosophy of biology has shown that historical contingency carries various interpretations and that we are still lacking a general understanding of historicity, i.e., a framework from which to interpret why and to what extent history matters in biological processes. Building from examples and analyses of the long-term experimental evolution (LTEE) project, this paper argues (...) that historicity possess three essential conditions: (1) multiple possible pasts, (2) multiple possible outcomes at a given instant, and (3) a relationship of causal dependence between these two sets. These criteria can be further specified in two general forms of historicity: dependence on initial conditions and path dependence. More attention is devoted to developing a rigorous account of the latter, which captures the type of historicity displayed by stochastic processes. This paper also highlights that it is often more productive to adopt an instant-relative approach and think in terms of degree of historicity instead of trying to maintain a rigid and absolute dichotomy between historical and ahistorical (completely convergent) processes. (shrink)

The value of optimality modeling has long been a source of contention amongst population biologists. Here I present a view of the optimality approach as at once playing a crucial explanatory role and yet also depending on external sources of confirmation. Optimality models are not alone in facing this tension between their explanatory value and their dependence on other approaches; I suspect that the scenario is quite common in science. This investigation of the optimality approach thus serves as a case (...) study, on the basis of which I suggest that there is a widely felt tension in science between explanatory independence and broad epistemic interdependence, and that this tension influences scientific methodology. (shrink)

How can we know what really happened in the distant past in places like ancient Egypt, Mesopotamia, Palestine, Greece, and Rome, especially since the evidence is fragmentary and ancient cultures are so different from our own frame of reference? Scholars may examine historical documents and archaeological artifacts, and then make reasonable inferences. But in the final analysis there can be no absolute certainty about events far removed from present reality, and the past must be reconstructed by means of hypotheses that (...) coherently organize all available data. Knowledge claims about the past, and about many areas of science as well, rest on a network of interdependence between theory and evidence, and between interpretation and data. In this fascinating study of epistemology, philosopher Peter Kosso argues for a coherence model of epistemic justification. In the first part, the conceptual argument, he proposes a model of knowledge of the past. In the second part he presents three detailed case studies drawn from the work of historians and archaeologists. These studies are used to support and fine-tune the model outlined in the first part. Kosso presents many insights into the limits of knowledge and our ability to know the mental as well as the physical past. Historians, archaeologists, philosophers, and students interested in epistemology will find this accessible work to be of great value. (shrink)

Carl Craver investigates what we are doing when we sue neuroscience to explain what's going on in the brain.

The present paper presents a philosophical analysis of earth science, a discipline that has received relatively little attention from philosophers of science. We focus on the question of whether earth science can be reduced to allegedly more fundamental sciences, such as chemistry or physics. In order to answer this question, we investigate the aims and methods of earth science, the laws and theories used by earth scientists, and the nature of earth-scientific explanation. Our analysis leads to the tentative conclusion that (...) there are emergent phenomena in earth science but that these may be reducible to physics. However, earth science does not have irreducible laws, and the theories of earth science are typically hypotheses about unobservable (past) events or generalised - but not universally valid - descriptions of contingent processes. Unlike more fundamental sciences, earth science is characterised by explanatory pluralism: earth scientists employ various forms of narrative explanations in combination with causal explanations. The main reason is that earth-scientific explanations are typically hampered by local underdetermination by the data to such an extent that complete causal explanations are impossible in practice, if not in principle. (shrink)

This chapter contains sections titled: Introduction Object and Aims of Earth Science The Autonomy of Earth Science Explanation in Earth Science Conclusion Acknowledgment References.

We discuss the scientific task of historical reconstruction and the problem of epistemic access. We argue that strong epistemic support for historical claims consists in the consilience of multiple independent lines of evidence, and analyze the impact hypothesis for the End-Cretaceous mass extinction to illustrate the accrual of epistemic support. Although there are elements of the impact hypothesis that enjoy strong epistemic support, the general conditions for this are strict, and help to clarify the difficulties associated with reconstructing the deep (...) past. (shrink)

A Bayesian account of independent evidential support is outlined. This account is partly inspired by the work of C. S. Peirce. I show that a large class of quantitative Bayesian measures of confirmation satisfy some basic desiderata suggested by Peirce for adequate accounts of independent evidence. I argue that, by considering further natural constraints on a probabilistic account of independent evidence, all but a very small class of Bayesian measures of confirmation can be ruled out. In closing, another application of (...) my account to the problem of evidential diversity is also discussed. (shrink)

Historical scientists frequently face incomplete data, and lack direct experimental access to their targets. This has led some philosophers and scientists to be pessimistic about the epistemic potential of the historical sciences. And yet, historical science often produces plausible, sophisticated hypotheses. I explain this capacity to generate knowledge in the face of apparent evidential scarcity by examining recent work on Thylacoleo carnifex, the ‘marsupial lion’. Here, we see two important methodological features. First, historical scientists are methodological omnivores, that is, they (...) construct purpose-built epistemic tools tailored to generate evidence about highly specific targets. This allows them to produce multiple streams of independent evidence and thus maximize their epistemic reach. Second, investigative scaffolding: research proceeds in a piece-meal fashion, information only gaining evidential relevance once certain hypotheses are well supported. I illustrate scaffolding in a discussion of the nature of functional ascription in paleobiology. Frequently, different senses of ‘function’ are not discriminated during paleobiological investigation—something which can mar adaptationist investigations of extant organisms. However, I argue that, due to scaffolding, conflating senses of ‘function’ can be the right thing to do. Coarse-grained functional hypotheses are required before it is clear what evidence could discriminate between more fine-grained ones. I draw on omnivory and scaffolding to argue that pessimists make a bad empirical bet. It is a bad idea to bet against the epistemic fortunes of such opportunistic and resourceful scientists, especially when we have reason to think we will systematically underestimate the amount of evidence ultimately available to them. (shrink)

The comparative method grants epistemic access to the biological past. Comparing lineages provides empirical traction on both hypotheses about particular lineages and models of trait evolution. Understanding this evidential role is important. Although philosophers have recently turned their attention to relations of descent, little work exists exploring the status of evidence from convergences. I argue that, where they exist, convergences play a central role in the confirmation of adaptive hypotheses. I focus on ‘analogous inferences’, show how such inferences ought to (...) be analysed and suggest three methods for strengthening their evidential weight. 1 Introduction2 Analogous Inferences2.1 Adaptive explanations and analogies2.2 Analogous inferences2.3 Scope, grain, and specificity3 Parallel Modelling, Integrated Explanations, and Convergent Modelling3.1 Parallel modelling3.2 Integrated explanations3.1 Convergent modelling4 Conclusion. (shrink)

In earlier work ( Cleland [2001] , [2002]), I sketched an account of the structure and justification of ‘prototypical’ historical natural science that distinguishes it from ‘classical’ experimental science. This article expands upon this work, focusing upon the close connection between explanation and justification in the historical natural sciences. I argue that confirmation and disconfirmation in these fields depends primarily upon the explanatory (versus predictive or retrodictive) success or failure of hypotheses vis-à-vis empirical evidence. The account of historical explanation that (...) I develop is a version of common cause explanation. Common cause explanation has long been vindicated by appealing to the principle of the common cause. Many philosophers of science (e.g., Sober and Tucker) find this principle problematic, however, because they believe that it is either purely methodological or strictly metaphysical. I defend a third possibility: the principle of the common cause derives its justification from a physically pervasive time asymmetry of causation (a.k.a. the asymmetry of overdetermination). I argue that explicating the principle of the common cause in terms of the asymmetry of overdetermination illuminates some otherwise puzzling features of the practices of historical natural scientists. (shrink)

In the wake of the paleobiological revolution of the 1970s and 1980s, paleontologists continue to investigate far-reaching questions about how evolution works. Many of those questions have a philosophical dimension. How is macroevolution related to evolutionary changes within populations? Is evolutionary history contingent? How much can we know about the causes of evolutionary trends? How do paleontologists read the patterns in the fossil record to learn about the underlying evolutionary processes? Derek Turner explores these and other questions, introducing the reader (...) to exciting recent work in the philosophy of paleontology and to theoretical issues including punctuated equilibria and species selection. He also critically examines some of the major accomplishments and arguments of paleontologists of the last 40 years. (shrink)

How do historians, comparative linguists, biblical and textual critics and evolutionary biologists establish beliefs about the past? How do they know the past? This book presents a philosophical analysis of the disciplines that offer scientific knowledge of the past. Using the analytic tools of contemporary epistemology and philosophy of science the book covers such topics as evidence, theory, methodology, explanation, determination and underdetermination, coincidence, contingency and counterfactuals in historiography. Aviezer Tucker's central claim is that historiography as a scientific discipline should (...) be thought of as an effort to explain the evidence of past events. He also emphasizes the similarity between historiographic methodology to Darwinian evolutionary biology. This is an important, fresh approach to historiography and will be read by philosophers, historians and social scientists interested in the methodological foundations of their disciplines. (shrink)

Carl F. Craver investigates what we are doing when we use neuroscience to explain what's going on in the brain. When does an explanation succeed and when does it fail? Craver offers explicit standards for successful explanation of the workings of the brain, on the basis of a systematic view about what neuroscientific explanations are.