Lipton Inference to the Best Explanation

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A clear introduction to Inference to the Best Explanation in the philosophy of science.
  W.H. Newton-Smith (ed)  A Companion to the Philosophy of Science  (Blackwell, 2000) 184-193. Inference to the Best Explanation P ETER L IPTON   Science depends on judgments of the bearing of evidence on theory.Scientists must judge whether an observation or the result of anexperiment supports, disconfirms, or is simply irrelevant to a givenhypothesis. Similarly, scientists may judge that, given all the availableevidence, a hypothesis ought to be accepted as correct or nearly so, rejectedas false, or neither. Occasionally, these evidential judgements can be makeon deductive grounds. If an experimental result strictly contradicts anhypothesis, then the truth of the evidence deductively entails the falsity of the hypothesis. In the great majority of cases, however, the connectionbetween evidence and hypothesis is non-demonstrative or inductive. Inparticular, this is so whenever a general hypothesis is inferred to be correcton the basis of the available data, since the truth of the data will notdeductively entail the truth of the hypothesis. It always remains possiblethat the hypothesis is false even though the data are correct.One of the central aims of the philosophy of science is to give aprincipled account of these judgements and inferences connecting evidenceto theory. In the deductive case, this project is well-advanced, thanks to aproductive stream of research into the structure of deductive argument thatstretches back to antiquity. The same cannot be said for inductiveinferences. Although some of the central problems were presentedincisively by David HUME in the eighteenth century, our currentunderstanding of inductive reasoning remains remarkably poor, in spite of the intense efforts of numerous epistemologists and philosophers of science.The model of Inference to the Best Explanation is designed to give apartial account of many inductive inferences, both in science and inordinary life. One version of the model was developed under the name`abduction' by Charles Sanders PIERCE early in this century, and themodel has been considerably developed and discussed over the last twenty-five years. Its governing idea is that explanatory considerations are a guideto inference, that scientists infer from the available evidence to thehypothesis which would, if correct, best explain that evidence. Many    2inferences are naturally described in this way. Darwin inferred thehypothesis of natural selection because, although it was not entailed by hisbiological evidence, natural selection would provide the best explanationof that evidence. When an astronomer infers that a star is receding fromthe earth with a specified velocity, she does this because the recessionwould be the best explanation of the observed red-shift of the star'scharacteristic spectrum. When a detective infers that it was Moriarty whocommitted the crime, he does so because this hypothesis would bestexplain the fingerprints, blood stains and other forensic evidence. Sherlock Holmes to the contrary, this is not a matter of deduction. The evidence willnot entail that Moriarty is to blame, since it always remains possible thatsomeone else was the perpetrator. Nevertheless, Holmes is right to makehis inference, since Moriarty's guilt would provide a better explanation of the evidence than would anyone else's.Inference to the Best Explanation can be seen as an extension of theidea of `self-evidencing' explanations, where the phenomenon that isexplained in turn provides an essential part of the reason for believing theexplanation is correct. For example, a star's speed of recession explainswhy its characteristic spectrum is red-shifted by a specified amount, but theobserved red-shift may be an essential part of the reason the astronomerhas for believing that the star is receding at that speed. Self-evidencingexplanations exhibit a curious circularity, but this circularity is benign.The recession is used to explain the red-shift and the red-shift is used toconfirm the recession, yet the recession hypothesis may be bothexplanatory and well-supported. According to Inference to the BestExplanation, this is a common situation in science: hypotheses aresupported by the very observations they are supposed to explain.Moreover, on this model, the observations support the hypothesis preciselybecause it would explain them. Inference to the Best Explanation thuspartially inverts an otherwise natural view of the relationship betweeninference and explanation. According to that natural view, inference isprior to explanation. First the scientist must decide which hypotheses toaccept; then, when called upon to explain some observation, she will drawfrom her pool of accepted hypotheses. According to Inference to the BestExplanation, by contrast, it is by only by asking how well varioushypotheses would explain the available evidence that she can determinewhich hypotheses merit acceptance. In this sense, Inference to the Best    3Explanation has it that explanation is prior to inference.There are two different problems that an account of induction inscience might purport to solve. The problem of description is to give anaccount of the principles that govern the way scientists weigh evidence andmake inferences. The problem of justification is to show that thoseprinciples are sound or rational, for example by showing that they tend tolead scientists to accept hypotheses that are true and to reject those that arefalse. Inference to the Best Explanation has been applied to both problems.The difficulties of the descriptive problem are sometimesunderrated, because it is supposed that inductive reasoning follows asimple pattern of extrapolation, with `More of the Same' as its fundamentalprinciple. Thus we predict that the sun will rise tomorrow because it hasrisen every day in the past, or that all ravens are black because all observedravens are black. This model of `enumerative induction' has however beenshown to be strikingly inadequate as an account of inference in science.On the one hand, a series of formal arguments, most notably the so-calledraven paradox and the new riddle of induction (see CONFIRMATION,PARADOXES OF), have shown that the enumerative model is wildlyover-permissive, treating virtually any observation as if it were evidencefor any hypothesis. On the other hand, the model is also much toorestrictive to account for most scientific inferences. Scientific hypothesestypically appeal to entities and processes not mentioned in the evidencethat supports them and often themselves unobservable and not merelyunobserved, so the principle of More of the Same does not apply. Forexample, while the enumerative model might account for the inference thata scientist makes from the observation that the light from one star is red-shifted to the conclusion that the light from another star will be red-shiftedas well, it will not account for the inference from observed red-shift tounobserved recession.The best-known attempt to account for these `vertical' inferencesthat scientists make from observations to hypotheses about the oftenunobservable reality that stands behind them is the Deductive-Nomologicalmodel. According to it, scientists deduce predictions from a hypothesis(along with various other `auxiliary premises') and then determine whetherthose predictions are correct. If some of them are not, the hypothesis isdisconfirmed; if all of them are, the hypothesis is confirmed and mayeventually be inferred. Unfortunately, while this model does make room    4for vertical inferences, it remains, like the enumerative model, far toopermissive, counting data as confirming a hypothesis which are in facttotally irrelevant to it. For example, since a hypothesis (H) entails thedisjunction of itself and any prediction whatever (H or P), and the truth of the prediction establishes the truth of the disjunction (since P also entails(H or P)), any successful prediction will count as confirming anyhypothesis, even if P is the prediction that the sun will rise tomorrow and Hthe hypothesis that all ravens are black.What is wanted is thus an account that permits vertical inferencewithout permitting absolutely everything, and Inference to the BestExplanation promises to fill the bill. Inference to the Best Explanationsanctions vertical inferences, because an explanation of some observedphenomenon may appeal to entities and processes not themselvesobserved; but it does not sanction just any vertical inference, sinceobviously a particular scientific hypothesis would not, if true, explain justany observation. A hypothesis about raven coloration will not, forexample, explain why the sun rises tomorrow. Moreover, Inference to theBest Explanation discriminates between different hypotheses all of whichwould explain the evidence, since the model only sanctions an inference tothe hypothesis which would best explain it.Inference to the Best Explanation thus has the advantages of givinga natural account of many inferences and of avoiding some of thelimitations and excesses of other familiar accounts of non-demonstrativeinference. If, however, it is to provide a serious model of induction,Inference to the Best Explanation needs to be developed and articulated,and this has not proven an easy thing to do. More needs to be said, forexample, about the conditions under which a hypothesis explains anobservation. Explanation is itself a major research topic in the philosophyof science, but the standard models of explanation yield disappointingresults when they are plugged into Inference to the Best Explanation. Forexample, the best-known account of scientific explanation is theDeductive-Nomological model, according to which an event is explainedwhen its description can be deduced from a set of premises that essentiallyincludes at least one law. This model has many flaws (seeEXPLANATION). Moreover, it is isomorphic to the Hypothetico-Deductive model of confirmation, so it would disappointingly reduceInference to the Best Explanation to a version of hypothetico-deductivism.
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