The article revisits the large n problem as it relates to the Jeffreys-Lindley paradox to compare the frequentist, Bayesian, and likelihoodist approaches to inference and evidence. It is argued that what is fallacious is to interpret a rejection of as providing the same evidence for a particular alternative, irrespective of n; this is an example of the fallacy of rejection. Moreover, the Bayesian and likelihoodist approaches are shown to be susceptible to the fallacy of acceptance. The key difference is that (...) in frequentist testing the severity evaluation circumvents both fallacies but no such principled remedy exists for the other approaches. (shrink)

This article calls into question the charge that frequentist testing is susceptible to the base-rate fallacy. It is argued that the apparent similarity between examples like the Harvard Medical School test and frequentist testing is highly misleading. A closer scrutiny reveals that such examples have none of the basic features of a proper frequentist test, such as legitimate data, hypotheses, test statistics, and sampling distributions. Indeed, the relevant error probabilities are replaced with the false positive/negative rates that constitute deductive calculations (...) based on known probabilities among events. As a result, the ampliative dimension of frequentist induction—learning from data about the underlying data-generating mechanism—is missing. *Received August 2009; revised January 2010. †To contact the author, please write to: Department of Economics, Virginia Tech, Blacksburg, VA 24061; e-mail: aris@vt.edu. (shrink)

In empirical modeling, an important desiderata for deeming theoretical entities and processes as real is that they can be reproducible in a statistical sense. Current day crises regarding replicability in science intertwines with the question of how statistical methods link data to statistical and substantive theories and models. Different answers to this question have important methodological consequences for inference, which are intertwined with a contrast between the ontological commitments of the two types of models. The key to untangling them is (...) the realization that behind every substantive model there is a statistical model that pertains exclusively to the probabilistic assumptions imposed on the data. It is not that the methodology determines whether to be a realist about entities and processes in a substantive field. It is rather that the substantive and statistical models refer to different entities and processes, and therefore call for different criteria of adequacy. (shrink)

The main aim of this paper is to revisit the curve fitting problem using the reliability of inductive inference as a primary criterion for the ‘fittest' curve. Viewed from this perspective, it is argued that a crucial concern with the current framework for addressing the curve fitting problem is, on the one hand, the undue influence of the mathematical approximation perspective, and on the other, the insufficient attention paid to the statistical modeling aspects of the problem. Using goodness-of-fit as the (...) primary criterion for ‘best', the mathematical approximation perspective undermines the reliability of inference objective by giving rise to selection rules which pay insufficient attention to ‘accounting for the regularities in the data'. A more appropriate framework is offered by the error-statistical approach, where (i) statistical adequacy provides the criterion for assessing when a curve captures the regularities in the data adequately, and (ii) the relevant error probabilities can be used to assess the reliability of inductive inference. Broadly speaking, the fittest curve (statistically adequate) is not determined by the smallness if its residuals, tempered by simplicity or other pragmatic criteria, but by the nonsystematic (e.g. white noise) nature of its residuals. The advocated error-statistical arguments are illustrated by comparing the Kepler and Ptolemaic models on empirical grounds. ‡I am grateful to Deborah Mayo and Clark Glymour for many valuable suggestions and comments on an earlier draft of the paper; estimating the Ptolemaic model was the result of Glymour's prompting and encouragement. †To contact the author, please write to: Department of Economics, Virginia Tech 3019 Pamplin Hall (0316), Blacksburg, VA 24061; e-mail: aris@vt.edu. (shrink)

Despite the widespread use of key concepts of the Neyman–Pearson (N–P) statistical paradigm—type I and II errors, significance levels, power, confidence levels—they have been the subject of philosophical controversy and debate for over 60 years. Both current and long-standing problems of N–P tests stem from unclarity and confusion, even among N–P adherents, as to how a test's (pre-data) error probabilities are to be used for (post-data) inductive inference as opposed to inductive behavior. We argue that the relevance of error probabilities (...) is to ensure that only statistical hypotheses that have passed severe or probative tests are inferred from the data. The severity criterion supplies a meta-statistical principle for evaluating proposed statistical inferences, avoiding classic fallacies from tests that are overly sensitive, as well as those not sensitive enough to particular errors and discrepancies. Introduction and overview 1.1 Behavioristic and inferential rationales for Neyman–Pearson (N–P) tests 1.2 Severity rationale: induction as severe testing 1.3 Severity as a meta-statistical concept: three required restrictions on the N–P paradigm Error statistical tests from the severity perspective 2.1 N–P test T(): type I, II error probabilities and power 2.2 Specifying test T() using p-values Neyman's post-data use of power 3.1 Neyman: does failure to reject H warrant confirming H? Severe testing as a basic concept for an adequate post-data inference 4.1 The severity interpretation of acceptance (SIA) for test T() 4.2 The fallacy of acceptance (i.e., an insignificant difference): Ms Rosy 4.3 Severity and power Fallacy of rejection: statistical vs. substantive significance 5.1 Taking a rejection of H0 as evidence for a substantive claim or theory 5.2 A statistically significant difference from H0 may fail to indicate a substantively important magnitude 5.3 Principle for the severity interpretation of a rejection (SIR) 5.4 Comparing significant results with different sample sizes in T(): large n problem 5.5 General testing rules for T(), using the severe testing concept The severe testing concept and confidence intervals 6.1 Dualities between one and two-sided intervals and tests 6.2 Avoiding shortcomings of confidence intervals Beyond the N–P paradigm: pure significance, and misspecification tests Concluding comments: have we shown severity to be a basic concept in a N–P philosophy of induction? (shrink)

Although both philosophers and scientists are interested in how to obtain reliable knowledge in the face of error, there is a gap between their perspectives that has been an obstacle to progress. By means of a series of exchanges between the editors and leaders from the philosophy of science, statistics and economics, this volume offers a cumulative introduction connecting problems of traditional philosophy of science to problems of inference in statistical and empirical modelling practice. Philosophers of science and scientific practitioners (...) are challenged to reevaluate the assumptions of their own theories - philosophical or methodological. Practitioners may better appreciate the foundational issues around which their questions revolve and thereby become better 'applied philosophers'. Conversely, new avenues emerge for finally solving recalcitrant philosophical problems of induction, explanation and theory testing. (shrink)

The main objective of the paper is to propose a frequentist interpretation of probability in the context of model-based induction, anchored on the Strong Law of Large Numbers (SLLN) and justifiable on empirical grounds. It is argued that the prevailing views in philosophy of science concerning induction and the frequentist interpretation of probability are unduly influenced by enumerative induction, and the von Mises rendering, both of which are at odds with frequentist model-based induction that dominates current practice. The differences between (...) the two perspectives are brought out with a view to defend the model-based frequentist interpretation of probability against certain well-known charges, including [i] the circularity of its definition, [ii] its inability to assign ‘single event’ probabilities, and [iii] its reliance on ‘random samples’. It is argued that charges [i]–[ii] stem from misidentifying the frequentist ‘long-run’ with the von Mises collective. In contrast, the defining characteristic of the long-run metaphor associated with model-based induction is neither its temporal nor its physical dimension, but its repeatability (in principle); an attribute that renders it operational in practice. It is also argued that the notion of a statistical model can easily accommodate non-IID samples, rendering charge [iii] simply misinformed. (shrink)

The growing availability of computer power and statistical software has greatly increased the ease with which practitioners apply statistical methods, but this has not been accompanied by attention to checking the assumptions on which these methods are based. At the same time, disagreements about inferences based on statistical research frequently revolve around whether the assumptions are actually met in the studies available, e.g., in psychology, ecology, biology, risk assessment. Philosophical scrutiny can help disentangle 'practical' problems of model validation, and conversely, (...) a methodology of statistical model validation can shed light on a number of issues of interest to philosophers of science. (shrink)

Rayleigh and Ramsay discovered the inert gas argon in the atmospheric air in 1895 using a carefully designed sequence of experiments guided by an informal statistical analysis of the resulting data. The primary objective of this article is to revisit this remarkable historical episode in order to make a case that the error‐statistical perspective can be used to bring out and systematize (not to reconstruct) these scientists' resourceful ways and strategies for detecting and eliminating error, as well as dealing with (...) Duhemian ambiguities and underdetermination problems as they arose in the context of their local research settings. *Received December 2009; revised January 2010. †To contact the author, please write to: Department of Economics, 3016 Pamplin Hall, Virginia Tech, Blacksburg, VA 24061; e‐mail: aris@vt.edu. (shrink)

Bernoulli’s 1713 golden theorem is viewed retrospectively in the context of modern model-based frequentist inference that revolves around the concept of a prespecified statistical model Mθx\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathcal{M}}_{{{\varvec{\uptheta}}}} \left( {\mathbf{x}} \right)$$\end{document}, defining the inductive premises of inference. It is argued that several widely-accepted claims relating to the golden theorem and frequentist inference are either misleading or erroneous: (a) Bernoulli solved the problem of inference ‘from probability to frequency’, and thus (b) the golden theorem (...) cannot justify an approximate Confidence Interval (CI) for the unknown parameter θ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\theta$$\end{document}, (c) Bernoulli identified the probability PA\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P\left( A \right)$$\end{document} with the relative frequency 1n∑k=1nxk\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{1}{n}\sum\nolimits_{k = 1}^{n} {x_{k} }$$\end{document} of event A as a result of conflating f(x0|θ)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$f({\mathbf{x}}_{0} |\theta )$$\end{document} with f(θ|x0),\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$f(\theta |{\mathbf{x}}_{0} ),$$\end{document} where x0\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathbf{x}}_{0}$$\end{document} denotes the observed data, and (d) the same ‘swindle’ is currently perpetrated by the p value testers. In interrogating the claims (a)–(d), the paper raises several foundational issues that are particularly relevant for statistical induction as it relates to the current discussions on the replication crises and the trustworthiness of empirical evidence, arguing that: [i] The alleged Bernoulli swindle is grounded in the unwarranted claim θ^nx0≃θ∗,\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hat{\theta }_{n} \left( {{\mathbf{x}}_{0} } \right) \simeq \theta^{*},$$\end{document} for a large enough n, where θ^nX\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hat{\theta }_{n} \left( {\mathbf{X}} \right)$$\end{document} is an optimal estimator of the true value θ∗\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\theta^{*}$$\end{document} of θ. [ii] Frequentist error probabilities are not conditional on hypotheses (H0 and H1) framed in terms of an unknown parameter θ since θ is neither a random variable nor an event. [iii] The direct versus inverse inference problem is a contrived and misplaced charge since neither conditional distribution f(x0|θ)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$f({\mathbf{x}}_{0} |\theta )$$\end{document} and f(θ|x0)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$f(\theta |{\mathbf{x}}_{0} )$$\end{document} exists (formally or logically) in model-based (Mθx\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathcal{M}}_{{{\varvec{\uptheta}}}} \left( {\mathbf{x}} \right)$$\end{document}) frequentist inference. (shrink)

Statistical model specification and validation raise crucial foundational problems whose pertinent resolution holds the key to learning from data by securing the reliability of frequentist inference. The paper questions the judiciousness of several current practices, including the theory-driven approach, and the Akaike-type model selection procedures, arguing that they often lead to unreliable inferences. This is primarily due to the fact that goodness-of-fit/prediction measures and other substantive and pragmatic criteria are of questionable value when the estimated model is statistically misspecified. Foisting (...) one's favorite model on the data often yields estimated models which are both statistically and substantively misspecified, but one has no way to delineate between the two sources of error and apportion blame. The paper argues that the error statistical approach can address this Duhemian ambiguity by distinguishing between statistical and substantive premises and viewing empirical modeling in a piecemeal way with a view to delineate the various issues more effectively. It is also argued that Hendry's general to specific procedures does a much better job in model selection than the theory-driven and the Akaike-type procedures primary because of its error statistical underpinnings. (shrink)

The objective of the paper is threefold. First, to argue that some of Haavelmo's methodological ideas and insights have been neglected because they are largely at odds with the traditional perspective that views empirical modeling in economics as an exercise in curve-fitting. Second, to make a case that this neglect has contributed to the unreliability of empirical evidence in economics that is largely due to statistical misspecification. The latter affects the reliability of inference by inducing discrepancies between the actual and (...) nominal error probabilities. Third, to argue that the ‘bridge pier’ between theory and data, as envisaged by Haavelmo, T [The Probability Approach to Econometrics, Econometrica, 12, 1–115], can be completed by blending some of his key methodological ideas and insights into a refined/extended version of the frequentist approach to inference. This new perspective is used to revisit dynamic stochastic general equilibrium modeling in an attempt to bring.. (shrink)

For model-based frequentist statistics, based on a parametric statistical model ${{\cal M}_\theta }$, the trustworthiness of the ensuing evidence depends crucially on the validity of the probabilistic assumptions comprising ${{\cal M}_\theta }$, the optimality of the inference procedures employed, and the adequateness of the sample size to learn from data by securing –. It is argued that the criticism of the postdata severity evaluation of testing results based on a small n by Rochefort-Maranda is meritless because it conflates [a] misuses (...) of testing with [b] genuine foundational problems. Interrogating this criticism reveals several misconceptions about trustworthy evidence and estimation-based effect sizes, which are uncritically embraced by the replication crisis literature. (shrink)

We argue that a responsible analysis of today's evidence-based risk assessments and risk debates in biology demands a critical or metascientific scrutiny of the uncertainties, assumptions, and threats of error along the manifold steps in risk analysis. Without an accompanying methodological critique, neither sensitivity to social and ethical values, nor conceptual clarification alone, suffices. In this view, restricting the invitation for philosophical involvement to those wearing a "bioethicist" label precludes the vitally important role philosophers of science may be able to (...) play as bioevidentialists. The goal of this paper is to give a brief and partial sketch of how a metascientific scrutiny of risk evidence might work. (shrink)

According to Marr, a computational-level theory consists of two elements, the what and the why. This article highlights the distinct role of the Why element in the computational analysis of vision. Three theses are advanced: that the Why element plays an explanatory role in computational-level theories, that its goal is to explain why the computed function is appropriate for a given visual task, and that the explanation consists in showing that the functional relations between the representing cells are similar to (...) the “external” mathematical relations between the entities that these cells represent. (shrink)

Deborah Mayo's view of science is that learning occurs by severely testing specific hypotheses. Mayo expounded this thesis in her (1996) Error and the Growth of Experimental Knowledge (EGEK). This volume consists of a series of exchanges between Mayo and distinguished philosophers representing competing views of the philosophy of science. The tone of the exchanges is lively, edifying and enjoyable. Mayo's error-statistical philosophy of science is critiqued in the light of positions which place more emphasis on large-scale theories. The result (...) clarifies Mayo's account and highlights her contribution to the philosophy of science -- in particular, her contribution to the philosophy of those sciences that rely heavily on statistical analysis. The second half of the volume considers the application (or extension) of an error-statistical philosophy of science to theory testing in economics, causal modelling and legal epistemology. The volume also includes a contribution to the frequentist philosophy of statistics written by Mayo in collaboration with Sir David Cox. (shrink)

When do data provide good evidence for a hypothesis, evidence that warrants an inference to the hypothesis? Standard answers either reject the legitimacy of induction or else allow warranted inference from data to hypothesis when there are suitable relationships between and among the data and hypotheses. The severity account rejects all of these, maintaining instead that the good evidence relation concerns not only relations between data and hypotheses but also the methods for obtaining the data and the sensitivity of these (...) methods to detecting falsehood. So, for example, the severity account allows two rival hypotheses to fit the data equally well, even to the point of having the same prior and posterior probabilities relative to those data, while the data nonetheless favour one hypothesis over the other by virtue of those data providing a severe test of one hypothesis and not the other. The contributions to Error and Inference variously trace the genesis of the severity account from statistics-oriented sciences, elaborate upon the account’s details, apply the account to various philosophical issues, and offer challenges to the account’s adequacy and replies to major criticisms. The editors have developed the severity account in detail elsewhere, and this anthology is an effort …. (shrink)

This paper examines research on three hypnotic phenomena: suggested amnesia, suggested analgesia, and “trance logic.” For each case a social-psychological interpretation of hypnotic behavior as a voluntary response strategy is compared with the traditional special-process view that “good” hypnotic subjects have lost conscious control over suggestion-induced behavior. I conclude that it is inaccurate to describe hypnotically amnesic subjects as unable to recall the material they have been instructed to forget. Although amnesics present themselves as unable to remember, they in fact (...) retain control over retrieval processes and accommodate their recall (or lack of it) to the social demands of the test situation. Hypnotic suggestions of analgesia do not produce a dissociation of pain from phenomenal awareness. Nonhypnotic suggestions of analgesia and distractor tasks that deflect attention from the'noxious stimuli are as effective as hypnotic suggestions in producing reductions in reported pain. Moreover, when appropriately motivated, subjects low in hypnotic suggestibility report pain reductions as large as those reported by highly suggestible hypnotically analgesic subjects. Finally, the data fail to support the view that a tolerance for logical incongruity (i.e., trance logic) uniquely characterizes hypnotic responding. So-called trance-logic-governed responding appears to reflect the attempts of “good” subjects to meet implicit demands to report accurately what they experience. (shrink)

Assesses the limits and possibilities of humanism for engaging with issues of pressing political and cultural concern. In his book The End of Education: Toward Posthumanism, William V. Spanos critiqued the traditional Western concept of humanism, arguing that its origins are to be found not in ancient Greece’s love of truth and wisdom, but in the Roman imperial era, when those Greek values were adapted in the service of imperialism on a deeply rooted, metaphysical level. Returning to that question (...) of humanism in the context of the United States’ war on terror in the post-9/11 era, Toward a Non-humanist Humanism points out the dehumanizing dynamics of Western modernity in which the rule of law is increasingly made flexible to defend against threats both real and potential. Spanos considers and assesses the work of thinkers such as Giorgio Agamben, Alain Badiou, Judith Butler, Jacques Rancière, and Slavoj Žižek as humanistic reformers and concludes with an effort to imagine a different kind of humanism—a non-humanist humanism—in which the old binary of friend versus foe gives way to a coming community without ethnic, cultural, or sexual divisions. William V. Spanos is Distinguished Professor Emeritus of English and Comparative Literature at Binghamton University, State University of New York. He is the author of many books, including American Exceptionalism in the Age of Globalization: The Specter of Vietnam and Herman Melville and the American Calling: The Fiction after Moby-Dick, 1851–1857, both also published by SUNY Press. (shrink)

Constructions of Intersubjectivity shows that the meaning of grammatical constructions often has more to do with the human cognitive capacity for taking other peoples' points of view than with describing the world. Treating pragmatics, semantics, and syntax in parallel and integrating insights from linguistics, psychology, and animal communication, Arie Verhagen develops a new understanding of linguistic communication. In doing so he shows the continuity between language and animal communication and reveals the nature of human linguistic specialization. Professor Verhagen uses Dutch (...) and English data from a wide variety of sources and considers the contributions of grammar to the coherence of discourse. He argues that important problems in semantics and syntax may be resolved if language is understood as an instrument for exerting influence and coordinating different perspectives. The grammatical phenomena he discusses include negative expressions, the let alone construction, complementation constructions, and discourse connectives.This powerfully argued and original explanation of the nature and operation of communication will interest a wide range of scholars and advanced students in linguistics, cognitive science, and human evolution. (shrink)

One of the challenges educational systems are facing today worldwide is enabling children's voices from silenced, marginalized, and excluded groups. This book analyzes the challenge of various identities and their uniqueness within childhood and offers theoretical and pedagogical-educational solutions within Philosophy for/with Children (P4wC).

The map is not the territory - from ontology to epistemology in knowledge construction -- Dialectics: a mechanism of knowledge construction -- Reflectivity reconsidered -- Reflectivity and the researchers' perspective -- Reflectivity and the participants' perspective -- Ethical differences and similarities as sources of reflection and knowledge construction -- Research relations and power differentials: from resistance to collaboration and in-between -- Frames of reference and the control of knowledge -- Reciprocity: the nature and attributes of research relations and power -- (...) Epilogue: from dialectics to dialog across liminal spaces. (shrink)

The public sphere, be it the Greek agora or the New York Times op-ed page, is the realm of appearances - not citizenship. Its central event is spectacle - not dialogue. Public dialogue, the mantra of many intellectuals and political commentators, is but a contradiction in terms. Marked by an asymmetry between the few who act and the many who watch, the public sphere can undermine liberal democracy, law, and morality. Inauthenticity, superficiality, and objectification are the very essence of the (...) public sphere. But the public sphere also liberates us from the bondages of private life and fosters an existentially vital aesthetic experience. Reign of Appearances uses a variety of cases to reveal the logic of the public sphere, including homosexuality in Victorian England, the 2008 crash, antisemitism in Europe, confidence in American presidents, communications in social media, special prosecutor investigations, the visibility of African-Americans, violence during the French Revolution, the Islamic veil, and contemporary sexual politics. This unconventional account of the public sphere is critical reading for anyone who wants to understand the effects of visibility in urban life, politics, and the media. (shrink)

This work focuses on the conception of God of the medieval Jewish philosopher and legal scholar, Hasdai Crescas. It demonstrates that Crescas’ God is infinitely creative and good and explores the parallel that Crescas implicitly draws between God as creator and legislator.

Longer-term developments shape the present and endogenous futures of institutions and practices of science and technology in society and their governance. Understanding the patterns allows diagnosis and soft intervention, often linked to scenario exercises. The book collects six articles offering key examples of this perspective, addressing ongoing issues in the governance of science and technology, including nanotechnology and responsible research and innovation. And adds two more articles that address background philosophical issues.

Cognitive Science, Volume 45, Issue 11, November 2021.

This study offers a fresh reappraisal of the philosopher, political thinker, and historian of ideas Sir Isaiah Berlin (1909-1997) from childhood to the height of his intellectual career. It provides the first historically contextualized study of Berlin's formative years and identifies different stages in his intellectual development, allowing a reappraisal of his theory of liberalism. Applying a 'double perspective' that examines Berlin both as an East European Jewish émigré; as well as a British Liberal intellectual, author Arie Dubnov stresses the (...) very ambivalent relation between Berlin's liberal philosophy and his pro-Zionist sentiments. (shrink)

Many believe that the ethical problems of donation after cardiocirculatory death (DCD) have been "worked out" and that it is unclear why DCD should be resisted. In this paper we will argue that DCD donors may not yet be dead, and therefore that organ donation during DCD may violate the dead donor rule. We first present a description of the process of DCD and the standard ethical rationale for the practice. We then present our concerns with DCD, including the following: (...) irreversibility of absent circulation has not occurred and the many attempts to claim it has have all failed; conflicts of interest at all steps in the DCD process, including the decision to withdraw life support before DCD, are simply unavoidable; potentially harmful premortem interventions to preserve organ utility are not justifiable, even with the help of the principle of double effect; claims that DCD conforms with the intent of the law and current accepted medical standards are misleading and inaccurate; and consensus statements by respected medical groups do not change these arguments due to their low quality including being plagued by conflict of interest. Moreover, some arguments in favor of DCD, while likely true, are "straw-man arguments," such as the great benefit of organ donation. The truth is that honesty and trustworthiness require that we face these problems instead of avoiding them. We believe that DCD is not ethically allowable because it abandons the dead donor rule, has unavoidable conflicts of interests, and implements premortem interventions which can hasten death. These important points have not been, but need to be fully disclosed to the public and incorporated into fully informed consent. These are tall orders, and require open public debate. Until this debate occurs, we call for a moratorium on the practice of DCD. (shrink)

Within the space of a few years, the idea of Responsible Research and Innovation, and its acronym RRI, catapulted from an obscure phrase to the topic of conferences and attempts to specify and realize it. How did this come about, and against which backdrop? What are the dynamics at present, and what do these imply for the future of RRI as a discourse, and as a patchwork of practices? It is a social innovation which creates opening in existing divisions of (...) moral labour, a notion that is explained with the help of the history of responsibility language. It is filled in for the present situation and ongoing developments. Some elements may stabilize and this creates a path into the future. There will be reductions of the originally open-ended innovation, some productive, others less so. This is a reason to regularly inquire into the value of the reductions and the directions the path is taking. (shrink)