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Verisimilitude: a causal approach

Synthese 190 (9):1471-1488 (2013)

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  1. It’s Just A Feeling: Why Economic Models Do Not Explain.Anna Alexandrova & Robert Northcott - 2013 - Journal of Economic Methodology 20 (3):262 - 267.
    Julian Reiss correctly identified a trilemma about economic models: we cannot maintain that they are false, but nevertheless explain and that only true accounts explain. In this reply we give reasons to reject the second premise ? that economic models explain. Intuitions to the contrary should be distrusted.
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  • Refined Nomic Truth Approximation by Revising Models and Postulates.Theo A. F. Kuipers - 2020 - Synthese 197 (4):1601-1625.
    Assuming that the target of theory oriented empirical science in general and of nomic truth approximation in particular is to characterize the boundary or demarcation between nomic possibilities and nomic impossibilities, I have presented, in my article entitled “Models, postulates, and generalized nomic truth approximation” :3057–3077, 2016. 10.1007/s11229-015-0916-9), the ‘basic’ version of generalized nomic truth approximation, starting from ‘two-sided’ theories. Its main claim is that nomic truth approximation can perfectly be achieved by combining two prima facie opposing views on theories: (...)
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  • A Verisimilitude Framework for Inductive Inference, with an Application to Phylogenetics.Vassend Olav Benjamin - unknown
    Bayesianism and likelihoodism are two of the most important frameworks philosophers of science use to analyse scientific methodology. However, both frameworks face a serious objection: much scientific inquiry takes place in highly idealized frameworks where all the hypotheses are known to be false. Yet, both Bayesianism and likelihoodism seem to be based on the assumption that the goal of scientific inquiry is always truth rather than closeness to the truth. Here, I argue in favor of a verisimilitude framework for inductive (...)
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  • Nonstandard Bayesianism: How Verisimilitude and Counterfactual Degrees of Belief Solve the Interpretive Problem in Bayesian Inference.Olav B. Vassend - unknown
    Scientists and Bayesian statisticians often study hypotheses that they know to be false. This creates an interpretive problem because the Bayesian probability of a hypothesis is typically interpreted as a degree of belief that the hypothesis is true. In this paper, I present and contrast two solutions to the interpretive problem, both of which involve reinterpreting the Bayesian framework in such a way that pragmatic factors directly determine in part how probability assignments are interpreted and whether a given probability assignment (...)
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  • What is Theoretical Progress of Science?Juha Saatsi - 2019 - Synthese 196 (2):611-631.
    The epistemic conception of scientific progress equates progress with accumulation of scientific knowledge. I argue that the epistemic conception fails to fully capture scientific progress: theoretical progress, in particular, can transcend scientific knowledge in important ways. Sometimes theoretical progress can be a matter of new theories ‘latching better onto unobservable reality’ in a way that need not be a matter of new knowledge. Recognising this further dimension of theoretical progress is particularly significant for understanding scientific realism, since realism is naturally (...)
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  • Progress as Approximation to the Truth: A Defence of the Verisimilitudinarian Approach.Gustavo Cevolani & Luca Tambolo - 2013 - Erkenntnis 78 (4):921-935.
    In this paper we provide a compact presentation of the verisimilitudinarian approach to scientific progress (VS, for short) and defend it against the sustained attack recently mounted by Alexander Bird (2007). Advocated by such authors as Ilkka Niiniluoto and Theo Kuipers, VS is the view that progress can be explained in terms of the increasing verisimilitude (or, equivalently, truthlikeness, or approximation to the truth) of scientific theories. According to Bird, VS overlooks the central issue of the appropriate grounding of scientific (...)
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  • Bayesian Statistical Inference and Approximate Truth.Olav B. Vassend - unknown
    Scientists and Bayesian statisticians often study hypotheses that they know to be false. This creates an interpretive problem because the Bayesian probability of a hypothesis is supposed to represent the probability that the hypothesis is true. I investigate whether Bayesianism can accommodate the idea that false hypotheses are sometimes approximately true or that some hypotheses or models can be closer to the truth than others. I argue that the idea that some hypotheses are approximately true in an absolute sense is (...)
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  • A Verisimilitude Framework for Inductive Inference, with an Application to Phylogenetics.Olav B. Vassend - forthcoming - British Journal for the Philosophy of Science.
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  • Truthlikeness: Old and New Debates.Ilkka Niiniluoto - 1990 - Synthese 84 (1):139-152.
    The notion of truthlikeness or verisimilitude has been a topic of intensive discussion ever since the definition proposed by Karl Popper was refuted in 1974. This paper gives an analysis of old and new debates about this notion. There is a fairly large agreement about the truthlikeness ordering of conjunctive theories, but the main rival approaches differ especially about false disjunctive theories. Continuing the debate between Niiniluoto’s min-sum measure and Schurz’s relevant consequence measure, the paper also gives a critical assessment (...)
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