The No Alternatives Argument

Dawid, Richard; Hartmann, Stephan; Sprenger, Jan

doi:10.1093/bjps/axt045

Cited by 59 publications

(56 citation statements)

References 11 publications

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“…In philosophy of science, a standard way of analyzing scientific methodology is by seeing whether the methodology makes sense from a Bayesian perspective. For example, in this way, Sober (2015) analyzes parsimony inference, 12 Dawid et al (2015) analyze no-alternatives arguments in physics, Schupbach (2018) analyzes robustness analysis, and Myrvold (2016) evaluates the epistemic value of unification. Since the preceding analyses take place in a Bayesian framework, they inherit the limitations and assumptions of Bayesianism.…”

Section: Resultsmentioning

confidence: 99%

Justifying the Norms of Inductive Inference

Vassend¹

2022

The British Journal for the Philosophy of Science

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Abstract Bayesian inference is limited in scope because it cannot be applied in idealized contexts where none of the hypotheses under consideration is true and because it is committed to always using the likelihood as a measure of evidential favouring, even when that is inappropriate. The purpose of this article is to study inductive inference in a very general setting where finding the truth is not necessarily the goal and where the measure of evidential favouring is not necessarily the likelihood. I use an accuracy argument to argue for probabilism and I develop a new kind of argument to argue for two general updating rules, both of which are reasonable in different contexts. One of the updating rules has standard Bayesian updating, Bissiri et al.’s ([2016]) general Bayesian updating, Douven’s ([2016]) IBE-based updating, and my (Vassend ([forthcoming]) quasi-Bayesian updating as special cases. The other updating rule is novel. 1Introduction2Why Crediblity Functions Should Be Probabilistic3Deriving the Updating Rules3.1The combination step3.2The normalization step3.3Characterizations of inferential and predictive updating4Discussion of Inferential and Predictive Updating4.1The difference between inferential updating and predictive updating4.2The relationship between inferential updating and other updating procedures5ConclusionAppendix

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Section: Resultsmentioning

confidence: 99%

Justifying the Norms of Inductive Inference

Vassend¹

2022

The British Journal for the Philosophy of Science

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“…By treating these frameworks as normative, it is possible to get a better understanding of when and why, exactly, an inferential rule or principle may be expected to lead scientists towards the truth. In this way, philosophers have in recent years given likelihoodist or Bayesian accounts of, for example, inference to the best explanation (Cabrera [2017]), the comparative method in historical linguistics (Okayasu [2017]), parsimony reasoning (Sober [2015]), 'no-alternatives' style arguments (Dawid et al [2015]), and robustness analysis (Schupbach [2018]).…”

Section: Introductionmentioning

confidence: 99%

A Verisimilitude Framework for Inductive Inference, with an Application to Phylogenetics

Vassend

2020

The British Journal for the Philosophy of Science

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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 inference. In the verisimilitude framework, scientific inquiry is conceived of, in part, as a process where inference methods ought to be calibrated to appropriate measures of closeness to the truth. To illustrate the verisimilitude framework, I offer a reconstruction of parsimony evaluations of scientific theories, and I give a reconstruction and extended analysis of the use of parsimony inference in phylogenetics. By recasting phylogenetic inference in the verisimilitude framework, it becomes possible to both raise and address objections to phylogenetic methods that rely on parsimony. 1 Introduction 2 Problems With the Law of Likelihood 3 Introducing Verisimilitude-based Inference 4 Examples of Verisimilitude-based Inference Procedures 4.1 Parsimony inference over theories 4.2 Parsimony inference in phylogenetics 5 Conclusion

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“…In some cases like string theory or eternal inflation, those strategies, in the understanding of many of the given theories' exponents, justify a fairly high degree of trust in the theory's viability. [3,4,6] has analyzed the question whether arguments of non-empirical theory assessment can be epistemically significant. It was claimed that a specific class of those arguments can be reconstructed in a way that demonstrates their epistemic significance and, from a Bayesian perspective, justifies calling them "non-empirical confirmation".…”

Section: Introductionmentioning

confidence: 99%

Delimiting the Unconceived

Dawid

2018

Found Phys

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It has been argued in Dawid (String theory and the scientific method, Cambridge University Press, Cambridge, [4]) that physicists at times generate substantial trust in an empirically unconfirmed theory based on observations that lie beyond the theory's intended domain. A crucial role in the reconstruction of this argument of "non-empirical confirmation" is played by limitations to scientific underdetermination. The present paper discusses the question as to how generic the role of limitations to scientific underdetermination really is. It is argued that assessing such limitations is essential for generating trust in any theory's predictions, be it empirically confirmed or not. The emerging view suggests that empirical and non-empirical confirmation are more closely related to each other than one may expect at first glance.

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The No Alternatives Argument

Cited by 59 publications

References 11 publications

Justifying the Norms of Inductive Inference

Justifying the Norms of Inductive Inference

A Verisimilitude Framework for Inductive Inference, with an Application to Phylogenetics

Delimiting the Unconceived

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