Prediction in Science

The received view of an ad hochypothesis is that it accounts for only the observation(s) it was designed to account for, and so non-ad hocness is generally held to be necessary or important for an introduced hypothesis or modification to a theory. Attempts by Popper and several others to convincingly explicate this view, however, prove to be unsuccessful or of doubtful value, and familiar and firmer criteria for evaluating the hypotheses or modified theories so classified are characteristically available. These points (...) are obscured largely because the received view fails to adequately separate psychology from methodology or to recognise ambiguities in the use of ''ad hoc''. (shrink)

Chaos-related obstructions to predictability have been used to challenge accounts of theory validation based on the agreement between theoretical predictions and experimental data. These challenges are incomplete in two respects: they do not show that chaotic regimes are unpredictable in principle and, as a result, that there is something conceptually wrong with idealized expectations of correct predictions from acceptable theories, and they do not explore whether chaos-induced predictive failures of deterministic models can be remedied by stochastic modeling. In this paper (...) we appeal to an asymptotic analysis of state space trajectories and their numerical approximations to show that chaotic regimes are deterministically unpredictable even with unbounded resources. Additionally, we explain why stochastic models of chaotic systems, while predictively successful in some cases, are in general predictively as limited as deterministic ones. We conclude by suggesting that the way in which scientists deal with such principled obstructions to predictability calls for a more comprehensive approach to theory validation, on which experimental testing is augmented by a multifaceted mathematical analysis of theoretical models, capable of identifying chaos-related predictive failures as due to principled limitations which the world itself imposes on any less-than-omniscient epistemic access to some natural systems. (shrink)

Climate models don’t give us probabilistic forecasts. To interpret their results, alternatively, as serious possibilities seems problematic inasmuch as climate models rely on contrary-to-fact assumptions: why should we consider their implications as possible if their assumptions are known to be false? The paper explores a way to address this possibilistic challenge. It introduces the concepts of a perfect and of an imperfect credible world, and discusses whether climate models can be interpreted as imperfect credible worlds. That would allow one to (...) use models for possibilistic prediction and salvage widespread scientific practice. (shrink)

In einer Welt, in der der Umgang mit Komplexität und Unsicherheit an Bedeutung gewinnt, sind politische Entscheidungsträger immer stärker auf eine wissenschaftliche Beratung angewiesen. Trotz des Bedarfs der politischen Akteure nach konkreten Handlungsempfehlungen sollte seriöse Politikberatung die grundlegenden Werte wissenschaftlichen Arbeitens nicht aus den Augen verlieren.

Predictive success as an aim of science -- On the very possibility of prediction in the social sciences -- Empirical facts about social prediction: its mode, object and performance -- Understanding poor forecast performance.

Frank Knight (1921) famously distinguished the epistemic modes of certainty, risk, and uncertainty in order to characterize situations where deterministic, probabilistic or possibilistic foreknowledge is available. Because our probabilistic knowledge is limited, i.e. because many systems, e.g. the global climate, cannot be described and predicted probabilistically in a reliable way, Knight's third category, possibilistic foreknowledge, is not simply swept by the probabilistic mode. This raises the question how to justify possibilistic predictionsincluding the identication of the worst case. The development of (...) such a modal methodology is particularly vital with respect to predictions of climate change. I show that a methodological dilemma emerges when possibilistic predictions are framed in traditional terms and argue that a more nuanced conceptual framework, distinguishing dierent types of possibility, should be used in order to convey our uncertain knowledge about the future. The new conceptual scheme, however, questions the applicability of standard rules of rational decision-making, thus generating new challenges. (shrink)

As climate policy decisions are decisions under uncertainty, being based on a range of future climate change scenarios, it becomes a crucial question how to set up this scenario range. Failing to comply with the precautionary principle, the scenario methodology widely used in the Third Assessment Report of the International Panel on Climate Change (IPCC) seems to violate international environmental law, in particular a provision of the United Nations Framework Convention on Climate Change. To place climate policy advice on a (...) sound methodological basis would imply that climate simulations which are based on complex climate models had, in stark contrast to their current hegemony, hardly an epistemic role to play in climate scenario analysis at all. Their main function might actually consist in ‘foreseeing future ozone-holes’. In order to argue for these theses, I explain first of all the plurality of climate models used in climate science by the failure to avoid the problem of underdetermination. As a consequence, climate simulation results have to be interpreted as modal sentences, stating what is possibly true of our climate system. This indicates that climate policy decisions are decisions under uncertainty. Two general methodological principles which may guide the construction of the scenario range are formulated and contrasted with each other: modal inductivism and modal falsificationism. I argue that modal inductivism, being the methodology implicitly underlying the third IPCC report, is severely flawed. Modal falsificationism, representing the sound alternative, would in turn require an overhaul of the IPCC practice. (shrink)

Climate policy decisions are decisions under uncertainty and are, therefore, based on a range of future climate scenarios, describing possible consequences of alternative policies. Accordingly, the methodology for setting up such a scenario range becomes pivotal in climate policy advice. The preferred methodology of the Intergovernmental Panel on Climate Change will be characterised as ,,modal verificationism"; it suffers from severe shortcomings which disqualify it for scientific policy advice. Modal falsificationism, as a more sound alternative, would radically alter the way the (...) climate scenario range is set up. Climate science's inability to find robust upper bounds for future temperature rise in line with modal falsificationism does not disprove that methodology, rather, this very fact prescribes even more drastic efforts to curb CO2 emissions than currently proposed. (shrink)

Our aim in this paper is to bring the woefully neglected literature on predictive modeling to bear on some central questions in the philosophy of science. The lesson of this literature is straightforward: For a very wide range of prediction problems, statistical prediction rules (SPRs), often rules that are very easy to implement, make predictions than are as reliable as, and typically more reliable than, human experts. We will argue that the success of SPRs forces us to reconsider our views (...) about what is involved in understanding, explanation, good reasoning, and about how we ought to do philosophy of science. (shrink)

There are different kinds of uncertainty. I outline some of the various ways that uncertainty enters science, focusing on uncertainty in climate science and weather prediction. I then show how we cope with some of these sources of error through sophisticated modelling techniques. I show how we maintain confidence in the face of error.

WE AIM HERE to outline a theory of evidence for use. More specifically we lay foundations for a guide for the use of evidence in predicting policy effectiveness in situ, a more comprehensive guide than current standard offerings, such as the Maryland rules in criminology, the weight of evidence scheme of the International Agency for Research on Cancer (IARC), or the US ‘What Works Clearinghouse’. The guide itself is meant to be well-grounded but at the same time to give practicable (...) advice, that is, advice that can be used by policy-makers not expert in the natural and social sciences, assuming they are well-intentioned and have a reasonable but limited amount of time and resources available for searching out evidence and deliberating. (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)

According to the no miracles argument, scientific realism provides the only satisfactory explanation of the predictive success of science. It is argued in the present article that a different explanatory strategy, based on the posit of limitations to the underdetermination of scientific theory building by the available empirical data, offers a more convincing understanding of scientific success.

RATIONALE, AIMS AND OBJECTIVES: Increased awareness of the gap between controlled research and medical practice has raised concerns over whether the special attention of doctors to probability estimates from clinical trials really improves the care of individuals. Evidence-based medicine has acknowledged that research results are not applicable to all kinds of patients, and consequently, has attempted to overcome this limitation by introducing improvements in the design and analysis of clinical trials. METHODS: A clinical case is used to highlight the premises (...) required to support reasonable extrapolations from controlled research to individuals. Then, the prospects of two key methodological improvements - pragmatic randomized controlled trials and subgroup analysis - are critically appraised. RESULTS: A principle to guide therapeutic inferences is suggested. According to this principle, the probabilities of interest for purposes of therapeutic decision making are those of the set defined by everything that is relevant to the patient and the outcome of interest at the time of the decision. It is argued that the conditions necessary to authorize automatic extrapolations of research results to specific patients are highly demanding. Furthermore, these requirements are rarely accomplished in real practice, even in the event that probability estimates come from samples generally taken as representative and are derived from specific subsets of patients. CONCLUSIONS: Clinicians should generally avoid unreflective extrapolations from research and address, as explicitly as possible, the challenge of estimating probabilities for individual patients. A key element of this task is the integration of data from research and non-research sources. (shrink)

The paper provides a formal proof that efficient estimates of parameters, which vary as as little as possible when measurements are repeated, may be expected to provide more accurate predictions. The definition of predictive accuracy is motivated by the work of Akaike (1973). Surprisingly, the same explanation provides a novel solution for a well known problem for standard theories of scientific confirmation — the Ravens Paradox. This is significant in light of the fact that standard Bayesian analyses of the paradox (...) fail to account for the predictive utility of universal laws like All ravens are black. (shrink)

With the ascent of modern epidemiology in the Twentieth Century came a new standard model of prediction in public health and clinical medicine. In this article, we describe the structure of the model. The standard model uses epidemiological measures-most commonly, risk measures-to predict outcomes (prognosis) and effect sizes (treatment) in a patient population that can then be transformed into probabilities for individual patients. In the first step, a risk measure in a study population is generalized or extrapolated to a target (...) population. In the second step, the risk measure is particularized or transformed to yield probabilistic information relevant to a patient from the target population. Hence, we call the approach the Risk Generalization-Particularization (Risk GP) Model. There are serious problems at both stages, especially with the extent to which the required assumptions will hold and the extent to which we have evidence for the assumptions. Given that there are other models of prediction that use different assumptions, we should not inflexibly commit ourselves to one standard model. Instead, model pluralism should be standard in medical prediction. (shrink)

In a recent article in this Journal, Fumagalli argues that economists are provisionally justified in resisting prominent calls to integrate neural variables into economic models of choice. In other articles, various authors engage with Fumagalli’s argument and try to substantiate three often-made claims concerning neuroeconomic modelling. First, the benefits derivable from neurally informing some economic models of choice do not involve significant tractability costs. Second, neuroeconomic modelling is best understood within Marr’s three-level of analysis framework for information-processing systems. And third, (...) neural findings enable choice modellers to confirm the causal relevance of variables posited by competing economic models, identify causally relevant variables overlooked by existing models, and explain observed behavioural variability better than standard economic models. In this paper, I critically examine these three claims and respond to the related criticisms of Fumagalli’s argument. Moreover, I qualify and extend Fumagalli’s account of how trade-offs between distinct modelling desiderata hamper neuroeconomists’ attempts to improve economic models of choice. I then draw on influential neuroeconomic studies to argue that even the putatively best available neural findings fail to substantiate current calls for a neural enrichment of economic models. (shrink)

Abstract Reichenbach emphasizes the central importance of prediction, which is?for him?the principal aim of science. This paper offers a critical reconstruction of his concept of prediction, taking into account the different periods of his thought. First, prediction is studied as a key factor in rejecting the positivism of the Vienna Circle. This part of the discussion concentres on the general features of prediction before Experience and Prediction (EP) (section 1). Second, prediction is considered in the context of Reichenbach's disagreements with (...) his contemporaries?Carnap and Popper?(section 2). Pointing out these differences gives an additional basis for understanding how Reichenbach saw ?prediction? in the period when EP was written. Third, Reichenbach's theoretical framework of prediction is analysed following EP. This analysis studies the semantical, logical, epistemological and methodological bases of his concept of prediction (section 3). Fourth, Reichenbach's conception of prediction, based on an objectivist interpretation of probabilities, is compared with the perspective on prediction of subjective Bayesians (the present personalists). This comparison (section 4) illustrates Reichenbach's views regarding the links of prediction with probability. Fifth, innovations and elements of continuity after EP are noted which give a more complete picture of Reichenbach's thought on prediction (section 5). This contributes to a comprehensive characterization of his concept of prediction. Finally, there is an assessment of his whole view of the matter and a presentation of the ingredients for a satisfactory alternative (section 6). (shrink)

The view that large-scale, long-range social theories cannot be predictive other than "in principle" is sufficiently widespread as to be considered the orthodox view. It is widely held that, lacking this predictive quality, social theories are cut off from a crucial form of vindication enjoyed by the experimental sciences. Thus many would agree with Ryan's assessment that while with regard to large-scale social changes "long-range prediction is not in principle impossible," nonetheless as a matter of practical methodology such a goal (...) is of "dubious value." ;The reason commonly proffered as to why social theories cannot be predictive is the causal complexity of social life. Because of this feature, it is held, while we may be able to unearth interesting social generalizations, we will not be able to predict the many initial conditions together with which they predict. Alternately, due to this complexity we are able to achieve no better than tendency laws which do not permit predictions of sufficient precision to allow for predictive testing. This has been held to be true for other causally complex fields as well. Thus, Scriven has argued that Darwin was "the paradigm of the explanatory but non-predictive scientist" due to the constraints imposed on his methodology by the causal complexity of the biosphere. ;As a result of both an uncritical acceptance of the orthodox view and an inadequate analysis of Marx's methodology, Daniel Little has argued that Marxian theory is non-predictive. However, a thorough analysis of Marx's labour process theory shows it to be both clearly predictive and subject to justification by predictive assessment. Moreover, a formalization of the theory indicates that available data confirm it as regards both its central hypothesis and the matrix of social causation it exhibits. ;Little's position in regard to Marxian theory is strongly similar to Scriven's in regard to Darwinian theory. In both cases, faulty theoretical presuppositions combine with inadequate analysis to buttress false conclusions as to the asymmetry of explanation and prediction. Adequate analysis dispels Little's and Scriven's conclusions and exhibits important methodological parallels between Marx and Darwin. (shrink)

All major research ethics policies assert that the ethical review of clinical trial protocols should include a systematic assessment of risks and benefits. But despite this policy, protocols do not typically contain explicit probability statements about the likely risks or benefits involved in the proposed research. In this essay, I articulate a range of ethical and epistemic advantages that explicit forecasting would offer to the health research enterprise. I then consider how some particular confidence levels may come into conflict with (...) the principles of ethical research. (shrink)

Accurate estimation of risk and benefit is integral to good clinical research planning, ethical review, and study implementation. Some commentators have argued that various actors in clinical research systems are prone to biased or arbitrary risk/benefit estimation. In this commentary, we suggest the evidence supporting such claims is very limited. Most prior work has imputed risk/benefit beliefs based on past behavior or goals, rather than directly measuring them. We describe an approach – forecast analysis – that would enable direct and (...) effective measure of the quality of risk/benefit estimation. We then consider some objections and limitations to the forecasting approach. (shrink)

The idea that reason can justify induction was famously criticized by David Hume. Hume concluded that there is no rational justification for inductive inferences and hence, no rational justification for most of our daily beliefs. Many philosophers attempted to solve Hume's problem with no success. Bertrand Russell commented regarding Hume's problem: "[if we cannot justify induction] we have no reason to expect the sun to rise tomorrow, to expect bread to be more nourishing than a stone, or to expect that (...) if we throw ourselves off the roof we shall fall." The New Riddle of Induction was introduced by Nelson Goodman in his book Fact, Fiction and Forecast, published in 1954. Goodman's problem raised some serious doubts about our ability even to describe inductive principles. In this Book Rami Israel is attempting to solve Both Hume's and Goodman's philosophical problems by introducing a new approach to the subject and by drawing a new picture of our inductive practices. (shrink)

In their critique of Klein (2014a), Trafimow and Earp present two theses. First, they argue that, contra Klein, a well-specified theory is not a necessary condition for successful replication. Second, they contend that even when there is a well-specified theory, replication depends more on auxiliary assumptions than on theory proper. I take issue with both claims, arguing that (a) their first thesis confuses a material conditional (what I said) with a modal claim (T&E’s misreading of what I said), and (b) (...) their second thesis has the unfortunate consequence of refuting their first thesis. (shrink)

Even though social scientists continue to discuss the problems posed by self-fulfilling and self-frustrating predictions, philosophers of science have ignored the topic since the 1970s. Back then, the prevailing view was that the methodological problems posed by reflexive predictions are either minor or easily avoided. I believe that this consensus was premature, ultimately relying on an overly narrow understanding of the phenomenon. I present an improved way to understand reflexive predictions (framed in probabilistic terms) and show that, once such predictions (...) are understood this way, the methodological problems they pose may turn out to be neither minor nor easily avoided. (shrink)

The aim of this paper is to defend some of C. G. Hempel's basic theses concerning the logic of explanation and prediction against criticisms recently made by W. Stegmüller. It is argued (very concisely) thatthese is no need for essentially pragmatic conditions in DN-arguments;only the structural identity sub-thesis “Every adequate prediction is ... an adequate explanation” can be held instead of the one Hempel has in mind;the notion of the ambiguity of probabilistic explanations should be reformulated;there is no need for (...) more than one knowledge situation;systematizations with low inductive probability do not give rational predictions or explanations of singular events. (shrink)

Evidence for a hypothesis typically confirms the hypothesis more if the evidence was predicted than if it was accommodated. Or so I argued in previous papers, where I also developed an analysis of why this should be so. But this was all a mistake if Howson and Franklin (1991) are to be believed. In this paper, I show why they are not to be believed. I also identify a grain of truth that may have been dimly grasped by those Bayesians (...) who deny the confirmatory value of prediction. (shrink)

It is taken for granted that the explanation of the Universe’s space-time dimension belongs to the host of the arguments that exhibit the superiority of modern (inflationary) cosmology over the standard model. In the present paper some doubts are expressed . They are based upon the fact superstring theory is too formal to represent genuine unification of general relativity and quantum field theory. Neveretheless, the fact cannot exclude the opportunity that in future the superstring theory can become more physical. Hence (...) this paper does not aim to query neither string cosmology, nor superstring theory; it asks for “tolerance in the matters cosmological”. It advices the researchers not to dwell on the common way of unification and to take into consideration the other ways as well. (shrink)

Philosophers and historians of philosophy have come to recognize that at the core of logical positivism was an attachment to prediction as the necessary condition for scientific knowledge.1 The inheritors of their tradition, especially the Bayesians among us, continue to seek a theory of confirmation that reflects this epistemic commitment. The importance of prediction in the growth of scientific knowledge is a commitment I share with the positivists, so I do not blanch at that designation, much less employ it as (...) a term of abuse. Precisely expressing and conclusively establishing the claims of prediction as a necessary condition for certifying claims as increments of knowledge is a goal that has so far eluded us post-positivists. Philosophers know the problems well: defining a positive instance, distinguishing projectible from nonprojectible predicates, deciding whether retrodiction is as epistemically probative as prediction. 2 But I can't help thinking that these problems are technicalities - important and arresting, but not impediments to embracing the positivist demand that increments in scientific knowledge withstand tests of predictive success. (shrink)

This paper investigates whether there is a discrepancy between the stated and actual aims in biomechanical research, particularly with respect to hypothesis testing. We present an analysis of one hundred papers recently published in The Journal of Experimental Biology and Journal of Biomechanics, and examine the prevalence of papers which (a) have hypothesis testing as a stated aim, (b) contain hypothesis testing claims that appear to be purely presentational (i.e. which seem not to have influenced the actual study), and (c) (...) have exploration as a stated aim. We found that whereas no papers had exploration as a stated aim, 58% of papers had hypothesis testing as a stated aim. We had strong suspicions, at the bare minimum, that presentational hypotheses were present in 31% of the papers in this latter group. (shrink)

From the early-1950s on, F.A. Hayek was concerned with the development of a methodology of sciences that study systems of complex phenomena. Hayek argued that the knowledge that can be acquired about such systems is, in virtue of their complexity (and the comparatively narrow boundaries of human cognitive faculties), relatively limited. The paper aims to elucidate the implications of Hayek’s methodology with respect to the specific dimensions along which the scientist’s knowledge of some complex phenomena may be limited. Hayek’s fallibilism (...) was an essential (if not always explicit) aspect of his arguments against the defenders of both socialism ([1935] 1948, [1940] 1948) and countercyclical monetary policy ([1975] 1978); yet, despite the fact that his conceptions of both complex phenomena and the methodology appropriate to their investigation imply that ignorance might beset the scientist in multiple respects, he never explicated all of these consequences. The specificity of a scientific prediction depends on the extent of the scientist’s knowledge concerning the phenomena under investigation. The paper offers an account of the considerations that determine the extent to which a theory’s implications prohibit the occurrence of particular events in the relevant domain. This theory of “predictive degree” both expresses and – as the phenomena of scientific prediction are themselves complex in Hayek’s sense – exemplifies the intuition that the specificity of a scientific prediction depends on the relevant knowledge available. (shrink)

The thesis of theory-ladenness of observations, in its various guises, is widely considered as either ill-conceived or harmless to the rationality of science. The latter view rests partly on the work of the proponents of New Experimentalism who have argued, among other things, that experimental practices are efficient in guarding against any epistemological threat posed by theory-ladenness. In this paper I show that one can generate a thesis of theory-ladenness for experimental practices from an influential New Experimentalist account. The notion (...) I introduce for this purpose is the concept of ‘theory-driven data reliability judgments’, according to which theories which are sought to be tested with a particular set of data guide reliability judgments about those very same data. I provide various prominent historical examples to show that TDRs are used by scientists to resolve data conflicts. I argue that the rationality of the practices which employ TDRs can be saved if the independent support of the theories driving TDRs is construed in a particular way. (shrink)

Response to "Predictive Policies" by R.S.McGowan Mr. McGowan has assumed that there is a clear distinction between inductive inferences and others, that we all know how to make the distinction, that we all agree that the inductive ones are somehow better or more reasonable than the alternatives, and I have criticised all of these assumptions. Further he hasformulated the philosophical problem of induction as the problem of showing why the inductive ones are better, and he has attempted to show that (...) inductive policiesas represented in his flow chart are better than non-inductive and counter-inductive ones. I have criticised some of the details of his argument and put forward the counter-claim that policies based on the weaker principles of desistence are better at avoiding contradictions and conform to past experience more closely than policies based on his strong principle of persistence. Accordingly, some modifications of his predictive rules have been suggested. Perhaps most importantly of all, I have argued that the assertion that one policy or inference is better or more rational than another is an incomplete assertion until a basis of comparison has been specified, since different policies may be better or more rational in relation to different bases, and I have indicated some possible approaches for further investigation of this point. A final line of investigation which should be mentioned is the problem of deciding which of two bases of comparison is better relative to some higher-order basis of comparison, a problem which may turn out to be very important in connexion with justifications of predictive policies. It seems that I have asked more questions than I have answered. Perhaps formulating them will help someone more familiar with the field than I am to find interesting answers. (shrink)

One of the fundamental questions of life sciences is one of whether there are genuinely random biological processes. An affirmative or negative answer to this question may have important methodological consequences. It appears that a number of biological processes are explicitly classified as random. One of them is the so-called somatic hypermutation. However, closer analysis of somatic hypermutation reveals that it is not a genuinely random process. Somatic hypermutation is called random because the exact outcome of this process is difficult (...) to predict in practice. The case of somatic hypermutation suggests that there may be no scientific evidence of a single case of ontologically random process in the biological world. (shrink)

Philosophy can shed light on mathematical modeling and the juxtaposition of modeling and empirical data. This paper explores three philosophical traditions of the structure of scientific theory—Syntactic, Semantic, and Pragmatic—to show that each illuminates mathematical modeling. The Pragmatic View identifies four critical functions of mathematical modeling: (1) unification of both models and data, (2) model fitting to data, (3) mechanism identification accounting for observation, and (4) prediction of future observations. Such facets are explored using a recent exchange between two groups (...) of mathematical modelers in plant biology. Scientific debate can arise from different modeling philosophies. (shrink)

Selectionist evolutionary theory has often been faulted for not making novel predictions that are surprising, risky, and correct. I argue that it in fact exhibits the theoretical virtue of predictive capacity in addition to two other virtues: explanatory unification and model fitting. Two case studies show the predictive capacity of selectionist evolutionary theory: parallel evolutionary change in E. coli and the origin of eukaryotic cells through endosymbiosis. †To contact the author, please write to: Philosophy Department, University of California, Santa Cruz, (...) 1156 High St., Santa Cruz, CA 95064; e‐mail: rgw@ucsc.edu ; rgwinther@gmail.com. (shrink)

The paper presents a further articulation and defence of the view on prediction and accommodation that I have proposed earlier. It operates by analysing two accounts of the issue—by Patrick Maher and by Marc Lange—that, at least at first sight, appear to be rivals to my own. Maher claims that the time-order of theory and evidence may be important in terms of degree of confirmation, while that claim is explicitly denied in my account. I argue, however, that when his account (...) is analysed, Maher reveals no scientifically significant way in which the time-order counts, and that indeed his view is in the end best regarded as a less than optimally formulated version of my own. Lange has also responded to Maher by arguing that the apparent relevance of temporal considerations is merely apparent: what is really involved, according to Lange, is whether or not a hypothesis constitutes an “arbitrary conjunction.” I argue that Lange’s suggestion fails: the correct analysis of his and Maher’s examples is that provided by my account. (shrink)

A major difficulty for currently existing theories of inductive inference involves the question of what to do when novel, unknown, or previously unsuspected phenomena occur. In this paper one particular instance of this difficulty is considered, the so-called sampling of species problem.The classical probabilistic theories of inductive inference due to Laplace, Johnson, de Finetti, and Carnap adopt a model of simple enumerative induction in which there are a prespecified number of types or species which may be observed. But, realistically, this (...) is often not the case. In 1838 the English mathematician Augustus De Morgan proposed a modification of the Laplacian model to accommodate situations where the possible types or species to be observed are not assumed to be known in advance; but he did not advance a justification for his solution. (shrink)