Prediction in Science

In this paper, I critically evaluate several related, provocative claims made by proponents of data-intensive science and “Big Data” which bear on scientific methodology, especially the claim that scientists will soon no longer have any use for familiar concepts like causation and explanation. After introducing the issue, in section 2, I elaborate on the alleged changes to scientific method that feature prominently in discussions of Big Data. In section 3, I argue that these methodological claims are in tension with a (...) prominent account of scientific method, often called “Inference to the Best Explanation” (IBE). Later on, in section 3, I consider an argument against IBE that will be congenial to proponents of Big Data, namely the argument due to Roche and Sober (2013) that “explanatoriness is evidentially irrelevant”. This argument is based on Bayesianism, one of the most prominent general accounts of theory-confirmation. In section 4, I consider some extant responses to this argument, especially that of Climenhaga (2017). In section 5, I argue that Roche and Sober’s argument does not show that explanatory reasoning is dispensable. In section 6, I argue that there is good reason to think explanatory reasoning will continue to prove indispensable in scientific practice. Drawing on Cicero’s oft-neglected De Divinatione, I formulate what I call the “Ciceronian Causal-nomological Requirement”, (CCR), which states roughly that causal-nomological knowledge is essential for relying on correlations in predictive inference. I defend a version of the CCR by appealing to the challenge of “spurious correlations”, chance correlations which we should not rely upon for predictive inference. In section 7, I offer some concluding remarks. (shrink)

A review of Gary Smith and Jay Cordes: The Phantom Pattern Problem: The Mirage of Big Data. New York: Oxford University Press, 2020.

Affective forecasting refers to the ability to predict future emotions, a skill that is essential to making decisions on a daily basis. Studies of the concept have determined that individuals are often inaccurate in making such affective forecasts. However, the mechanisms of these errors are not yet clear. In order to better understand why affective forecasting errors occur, this article seeks to trace the theoretical roots of this theory with a focus on its multidisciplinary history. The roots of affective forecasting (...) lie mainly in economics, with early claims positing that utility played a role in decision-making. Furthermore, the philosopher Jeremy Bentham’s descriptions of utilitarianism played a major role in our understanding of whether to define utility as a hedonic quality. The birth of behavioural economics resulted in a paradigm shift, introducing the concept of cognitive biases as influences on the accuracy of predicted utility. Daniel Gilbert and Timothy Wilson, the earliest researchers of affective forecasting errors, have proceeded with the concept of the accuracy of predicted affective utility to conduct experiments that seek to determine why our predictions of future affect are inaccurate and how such errors play a role in our decision-making. (shrink)

The pessimist predicts that future scientific theories will replace present scientific theories. However, she does not specify when the predicted events will take place, so we do not have the opportunity to blame her for having made a false prediction, although we might have the chance to praise her for having made a true prediction. Her prediction contrasts with the astronomer’s prediction. The astronomer specifies when the next solar eclipse will happen, so we have both the chance to blame her (...) for having made a false prediction and the chance to praise her for having made a true prediction. The pessimist’s prediction remains unvirtuous until she specifies when scientific revolutions will occur. This critical point applies no less to the selectivist’s prediction. (shrink)

The IHME Covid-19 prediction model has been one of the most influential Covid models in the United States. Early on, it received heavy criticism for understating the extent of the epidemic. I argue that this criticism was based on a misunderstanding of the model. The model was best interpreted not as attempting to forecast the actual course of the epidemic. Rather, it was attempting to make a conditional projection: telling us how the epidemic would unfold, given certain assumptions. This misunderstanding (...) of the IHME’s model prevented the public from seeing how dire the model’s projections actually were. (shrink)

In this paper, I examine Cicero’s oft-neglected De Divinatione, a dialogue investigating the legitimacy of the practice of divination. First, I offer a novel analysis of the main arguments for divination given by Quintus, highlighting the fact that he employs two logically distinct argument forms. Next, I turn to the first of the main arguments against divination given by Marcus. Here I show, with the help of modern probabilistic tools, that Marcus’ skeptical response is far from the decisive, proto-naturalistic assault (...) on superstition that it is sometimes portrayed to be. Then, I offer an extended analysis of the second of the main arguments against divination given by Marcus. Inspired by Marcus’ second main argument, I formulate, explicate, and defend a substantive principle of scientific methodology that I call the “Ciceronian Causal-Nomological Requirement” (CCR). Roughly, this principle states that causal knowledge is essential for relying on correlations in predictive inference. Although I go on to argue that Marcus’ application of the CCR in his debate with Quintus is dialectically inadequate, I conclude that De Divinatione deserves its place in Cicero’s philosophical corpus, and that ultimately, its significance for the history and philosophy of science ought to be recognized. (shrink)

The traditional philosophy of science approach to prediction leaves little room for appreciating the value and potential of imprecise predictions. At best, they are considered a stepping stone to more precise predictions, while at worst they are viewed as detracting from the scientific quality of a discipline. The aim of this paper is to show that imprecise predictions are undervalued in philosophy of science. I review the conceptions of imprecise predictions and the main criticisms levelled against them: (i) that they (...) cannot aid in model selection and improvement, and (ii) that they cannot support effective interventions in practical decision making. I will argue against both criticisms, showing that imprecise predictions have a circumscribed but important and legitimate place in the study of complex, heterogeneous systems. The argument is illustrated and supported by an example from conservation biology, where imprecise models were instrumental in saving the kōkako from extinction. (shrink)

We explore three questions about Earth system modeling that are of both scientific and philosophical interest: What kind of understanding can be gained via complex Earth system models? How can the limits of understanding be bypassed or managed? How should the task of evaluating Earth system models be conceptualized?

It is relatively easy to state that information retrieval is a scientific discipline but it is rather difficult to understand why it is science because what is science is still under debate in the philosophy of science. To be able to convince others that IR is science, our ability to explain why is crucial. To explain why IR is a scientific discipline, we use a theory and a model of scientific study, which were proposed recently. The explanation involves mapping the (...) knowledge structure of IR to that of well-known scientific disciplines like physics. In addition, the explanation involves identifying the common aim, principles and assumptions in IR and in well-known scientific disciplines like physics, so that they constrain the scientific investigation in IR in a similar way as in physics. Therefore, there are strong similarities in terms of the knowledge structure and the constraints of the scientific investigations between IR and scientific disciplines like physics. Based on such similarities, IR is considered a scientific discipline. (shrink)

Has the rise of data-intensive science, or ‘big data’, revolutionized our ability to predict? Does it imply a new priority for prediction over causal understanding, and a diminished role for theory and human experts? I examine four important cases where prediction is desirable: political elections, the weather, GDP, and the results of interventions suggested by economic experiments. These cases suggest caution. Although big data methods are indeed very useful sometimes, in this paper’s cases they improve predictions either limitedly or not (...) at all, and their prospects of doing so in the future are limited too. (shrink)

We identify several ongoing debates related to implicit measures, surveying prominent views and considerations in each debate. First, we summarize the debate regarding whether performance on implicit measures is explained by conscious or unconscious representations. Second, we discuss the cognitive structure of the operative constructs: are they associatively or propositionally structured? Third, we review debates whether performance on implicit measures reflects traits or states. Fourth, we discuss the question of whether a person’s performance on an implicit measure reflects characteristics of (...) the person who is taking the test or characteristics of the situation in which the person is taking the test. Finally, we survey the debate about the relationship between implicit measures and (other kinds of) behavior. (shrink)

Prediction is an important aspect of scientific practice, because it helps us to confirm theories and effectively intervene on the systems we are investigating. In ecology, prediction is a controversial topic: even though the number of papers focusing on prediction is constantly increasing, many ecologists believe that the quality of ecological predictions is unacceptably low, in the sense that they are not sufficiently accurate sufficiently often. Moreover, ecologists disagree on how predictions can be improved. On one side are the ‘theory-driven’ (...) ecologists, those who believe that ecology lacks a sufficiently strong theoretical framework. For them, more general theories will yield more accurate predictions. On the other are the ‘applied’ ecologists, whose research is focused on effective interventions on ecological systems. For them, deeper knowledge of the system in question is more important than background theory. The aim of this paper is to provide a philosophical examination of both sides of the debate: as there are strengths and weaknesses in both approaches to prediction, a pluralistic approach is best for the future of predictive ecology. (shrink)

I critically examine Stewart’s suggestion that we should weigh the various predictions Mendeleev made differently. I argue that in his effort to justify discounting the weight of some of Mendeleev’s failures, Stewart invokes a principle that will, in turn, reduce the weight of some of the successful predictions Mendeleev made. So Stewart’s strategy will not necessarily lead to a net gain in Mendeleev’s favor.

I critically examine Stewart’s suggestion that we should weigh the various predictions Mendeleev made differently. I argue that in his effort to justify discounting the weight of some of Mendeleev’s failures, Stewart invokes a principle that will, in turn, reduce the weight of some of the successful predictions Mendeleev made. So Stewart’s strategy will not necessarily lead to a net gain in Mendeleev’s favor.

Recently, Luk mentioned that scientific knowledge both explains and predicts. Do these two functions of scientific knowledge have equal significance, or is one of the two functions more important than the other? This commentary explains why prediction may be mandatory but explanation may be only desirable and optional.

The paper will compare two methods used in the design of diagnostic strategies. The first one is a method that precises predictive value of diagnostic tests. The second one is based on the use of Bayes’ theorem. The main aim of this article is to identify the epistemological assumptions underlying both of these methods. For the purpose of this objective, example projects of one and multi-stage diagnostic strategy developed using both methods will be considered.

Several authors have claimed that prediction is essentially impossible in the general theory of relativity, the case being particularly strong, it is said, when one fully considers the epistemic predicament of the observer. Each of these claims rests on the support of an underdetermination argument and a particular interpretation of the concept of prediction. I argue that these underdetermination arguments fail and depend on an implausible explication of prediction in the theory. The technical results adduced in these arguments can be (...) related to certain epistemic issues, but can only be misleadingly or mistakenly characterized as related to prediction. (shrink)

Hypothesizing after the results are known, or HARKing, occurs when researchers check their research results and then add or remove hypotheses on the basis of those results without acknowledging this process in their research report (Kerr, 1998). In the present article, I discuss three forms of HARKing: (1) using current results to construct post hoc hypotheses that are then reported as if they were a priori hypotheses; (2) retrieving hypotheses from a post hoc literature search and reporting them as a (...) priori hypotheses; and (3) failing to report a priori hypotheses that are unsupported by the current results. These three types of HARKing are often characterized as being bad for science and a potential cause of the current replication crisis. In the present article, I use insights from the philosophy of science to present a more nuanced view. Specifically, I identify the conditions under which each of these three types of HARKing is most and least likely to be bad for science. I conclude with a brief discussion about the ethics of each type of HARKing. (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)

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)

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)

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)

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 the methodology of scientific research programs (MSRP) there are important features on the problem of prediction, especially regarding novel facts. In his approach, Imre Lakatos proposed three different levels on prediction: aim, process, and assessment. Chapter 5 pays attention to the characterization of prediction in the methodology of research programs. Thus, it takes into account several features: (1) its pragmatic characterization, (2) the logical perspective as a proposition, (3) the epistemological component, (4) its role in the appraisal of research (...) programs, and (5) its place as a value for scientific research. -/- The notion of “novel facts” is highly relevant in his conception, where several aspects are involved: the directions of novel facts, the different kinds of novelty, and the transition from six possible options of “novel facts” to four choices. Thereafter, the prediction of novel facts as the criterion of appraisal is considered. On the one hand, this requires analyzing the theoretical, empirical, and heuristic possibilities of appraisal; and, on the other, whether there is an overemphasis on the role of prediction in methodology of scientific research programs. As a consequence, there is an analysis of Lakatos’ criterion of appraisal in MSRP and economics. (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)

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)

There is a middle ground of imperfect knowledge in fields like medicine and the social sciences. It stands between our day-to-day relatively certain knowledge obtained from ordinary basic observation of regularities in our world and our knowledge from well-validated theories in the physical sciences. -/- The latter enable reliable prediction a great deal of the time of the happening of events never before experienced. The former enable prediction only of what has happened before and beyond that of educated guesses which (...) may sometimes prove right and others not when we test them. -/- The imperfection of our knowledge between those limits is a consequence of complexity. -/- Reductionist empiricism fails when faced with complexity but we all still have to live in a complex world where reductionist science cannot help us devise reliable theories from which we can predict the behaviour of the world around us. We cannot predict reliably. We can only monitor actively and manage actively the world around us if we want to try to control our environment. We have a limited prediction horizon. -/- Science and empiricism work well if we want to send Voyager I and II over 3 billion miles away but not for most other things. -/- In medicine how and when to apply probabilistic, conjectural, incomplete medical theories and explanations requires professional expertise, intuition and judgement (non scientific knowledge) which is essential. Medical diagnosis is a skill of predicting from knowledge of what has happened before that the past will recur in the current patient applying expertise and intuition from knowledge and experience of prior cases and probabilistic medical research and theory. The physician is left to an educated guess as to how the future will develop for the particular individual patient the focus of his or her current attention. (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)

There is considerable disagreement about the epistemic value of novel predictive success, i.e. when a scientist predicts an unexpected phenomenon, experiments are conducted, and the prediction proves to be accurate. We survey the field on this question, noting both fully articulated views such as weak and strong predictivism, and more nascent views, such as pluralist reasons for the instrumental value of prediction. By examining the various reasons offered for the value of prediction across a range of inferential contexts , we (...) can see that neither weak nor strong predictivism captures all of the reasons for valuing prediction available. A third path is presented, Pluralist Instrumental Predictivism; PIP for short. (shrink)

Starting with a survey of the terminology that was used to describe the epistemological status of the mantic arts during the Middle Ages, this article focuses on the connections between the theoretical assumptions of these arts and of other prognostic disciplines of the time. While during the thirteenth century, mantic disciplines, medicine and meteorology were classified altogether as conjectural sciences that were all based on the interpretation of signs, during the fourteenth century, a more differentiated model of scientific prediction developed (...) in medical theory as well as in meteorology. This model took into account the conditional probability of the expected events, which allowed the option to falsify or at least to revise and adapt a prognosis. Against the backdrop of the epistemological models of prognosis, it becomes obvious that when the mantic disciplines were ultimately excluded from the Western canon of the sciences, it was due not alone to moral and theological concerns. (shrink)

‘The problem with simulations is that they are doomed to succeed.’ So runs a common criticism of simulations—that they can be used to ‘prove’ anything and are thus of little or no scientific value. While this particular objection represents a minority view, especially among those who work with simulations in a scientific context, it raises a difficult question: what standards should we use to differentiate a simulation that fails from one that succeeds? In this paper we build on a structural (...) analysis of simulation developed in previous work to provide an evaluative account of the variety of ways in which simulations do fail. We expand the structural analysis in terms of the relationship between a simulation and its real-world target emphasizing the important role of aspects intended to correspond and also those specifically intended not to correspond to reality. The result is an outline both of the ways in which simulations can fail and the scientific importance of those various forms of failure. (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)

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)

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.

Imagine putting together a jigsaw puzzle that works like the board game in the movie “Jumanji”: When you finish, whatever the puzzle portrays becomes real. The children playing “Jumanji” learn to prepare for the reality that emerges from the next throw of the dice. But how would this work for the puzzle of scientific research? How do you prepare for unlocking the secrets of the atom, or assembling from the bottom-up nanotechnologies with unforeseen properties – especially when completion of such (...) puzzles lies decades after the first scattered pieces are tentatively assembled? In the inaugural issue of this journal, Michael Polanyi argued that because the progress of science is unpredictable, society must only move forward with solving the puzzle until the picture completes itself. Decades earlier, Frederick Soddy argued that once the potential for danger reveals itself, one must reorient the whole of one’s work to avoid it. While both scientists stake out extreme positions, Soddy’s approach – together with the action taken by the like-minded Leo Szilard – provides a foundation for the anticipatory governance of emerging technologies. This paper narrates the intertwining stories of Polanyi, Soddy and Szilard, revealing how anticipation influenced governance in the case of atomic weapons and how Polanyi’s claim in “The Republic of Science” of an unpredictable and hence ungovernable science is faulty on multiple levels. (shrink)

This paper investigates whether there is a discrepancy between 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 have hypothesis testing as a stated aim, contain hypothesis testing claims that appear to be purely presentational, and have exploration as a stated aim. We found that whereas no papers had exploration (...) as a stated aim, 58 per cent of papers had hypothesis testing as a stated aim. We had strong suspicions, at the bare minimum, that presentational hypotheses were present in 31 per cent of the papers in this latter group. (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)

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.

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.

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)

This volume, the second in the Springer series Philosophy of Science in a European Perspective, contains selected papers from the workshops organised by the ESF Research Networking Programme PSE (The Philosophy of Science in a European Perspective) in 2009. Five general topics are addressed: 1. Formal Methods in the Philosophy of Science; 2. Philosophy of the Natural and Life Sciences; 3. Philosophy of the Cultural and Social Sciences; 4. Philosophy of the Physical Sciences; 5. History of the Philosophy of Science. (...) This volume is accordingly divided in five sections, each section containing papers coming from the meetings focussing on one of these five themes. However, these sections are not completely independent and detached from each other. For example, an important connecting thread running through a substantial number of papers in this volume is the concept of probability: probability plays a central role in present-day discussions in formal epistemology, in the philosophy of the physical sciences, and in general methodological debates---it is central in discussions concerning explanation, prediction and confirmation. The volume thus also attempts to represent the intellectual exchange between the various fields in the philosophy of science that was central in the ESF workshops. (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)