A self-fulfilling prophecy is, roughly, a prediction that brings about its own truth. Although true predictions are hard to fault, self-fulfilling prophecies are often regarded with suspicion. In this article, we vindicate this suspicion by explaining what self-fulfilling prophecies are and what is problematic about them, paying special attention to how their problems are exacerbated through automated prediction. Our descriptive account of self-fulfilling prophecies articulates the four elements that define them. Based on this account, we begin our critique by showing (...) that typical self-fulfilling prophecies arise due to mistakes about the relationship between a prediction and its object. Such mistakes—along with other mistakes in predicting or in the larger practical endeavor—are easily overlooked when the predictions turn out true. Thus we note that self-fulfilling prophecies prompt no error signals; truth shrouds their mistakes from humans and machines alike. Consequently, self-fulfilling prophecies create several obstacles to accountability for the outcomes they produce. We conclude our critique by showing how failures of accountability, and the associated failures to make corrections, explain the connection between self-fulfilling prophecies and feedback loops. By analyzing the complex relationships between accuracy and other evaluatively significant features of predictions, this article sheds light both on the special case of self-fulfilling prophecies and on the ethics of prediction more generally. (shrink)

What are conspiracy theories? And what, if anything, is epistemically wrong with them? I offer an account on which conspiracy theories are a unique way of holding a belief in a conspiracy. Specifically, I take conspiracy theories to be self-insulating beliefs in conspiracies. On this view, conspiracy theorists have their conspiratorial beliefs in a way that is immune to revision by counter-evidence. I argue that conspiracy theories are always irrational. Although conspiracy theories involve an expectation to encounter some seemingly disconfirming (...) evidence (allegedly planted by the conspirators), resistance to all counter- evidence cannot be justified on these grounds. (shrink)

At some point during the 1950s, mainstream American philosophy of science began increasingly to avoid questions about the role of non-cognitive values in science and, accordingly, increasingly to avoid active engagement with social, political and moral concerns. Such questions and engagement eventually ceased to be part of the mainstream. Here we show that the eventual dominance of 'value-free' philosophy of science can be attributed, at least in part, to the policies of the U.S. National Science Foundation's "History and Philosophy of (...) Science" sub-program. In turn, the sub-program's policies were set by logical empiricists who espoused value-free philosophy of science; these philosophers' actions, we also point out, fit a broad pattern, one in which analytic philosophers used institutional control to marginalize rival approaches to philosophy. We go on to draw on existing knowledge of this pattern to suggest two further, similar, contributors to the withdrawal from value-laden philosophy of science, namely decisions by the editors of Philosophy of Science and by the editors of The Journal of Philosophy. Political climate was, we argue, at most an indirect contributor to the withdrawal and was neither a factor that decided whether it occurred nor one that was sufficient to bring it about. Moreover, we argue that the actions at the National Science Foundation went beyond what was required by its senior administrators and are better viewed as part of what drove, rather than as what was being driven by, the adoption of logical empiricism by the philosophy of science community. (shrink)

The present work is a review of the early literature on "reflexive prediction" - the notion that public predictions by policymakers may influence and affect the social systems the predictions are made in relation to - in the disciplines of sociology and economics. It is a relatively complete treatment for the time period that it covers. It is intended for attachment to my January 2006 doctoral thesis as "Appendix B.".

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)

This paper describes the development of theories of scientific explanation since Hempel's earliest models in the 1940ies. It focuses on deductive and probabilistic whyexplanations and their main problems: lawlikeness, explanation-prediction asymmetries, causality, deductive and probabilistic relevance, maximal specifity and homogenity, the height of the probability value. For all of these topic the paper explains the most important approaches as well as their criticism, including the author's own accounts. Three main theses of this paper are: (1) Both deductive and probabilistic explanations (...) are important in science, not reducible to each other. (2) One must distinguish between (cause giving) explanations and (reason giving) justifications and predictions. (3) The adequacy of deductive as well as probabilistic explanations is relative to a pragmatically given background knowledge-which does not exclude, however, the possibility of purely semantic models. (shrink)