Robustness analysis disclaimer: please read the manual before use!

Kuorikoski, Jaakko; Lehtinen, Aki; Marchionni, Caterina

doi:10.1007/s10539-012-9329-z

Cited by 29 publications

(25 citation statements)

References 15 publications

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“…But rather than constraining themselves to the robustness of mathematical models, they also consider varying modeling assumptions, detection methods and experimental set-ups (such as experimental species)-see Culp (1995) for more on robustness and experimental setups. Woodward (2006) calls this derivational robustness, whose confirmational value has recently been argued for (Eronen 2015;Kuorikoski et al 2012;Lehtinen 2018). Again we find a notion of diversity playing a key role: the more divergent the group of experimental species for which a robust result is found, the less likely the observations are owed to idiosyncrasies of the individual species-in other words, the more likely it is that the observed phenomenon is also manifest in the species of interest.…”

Section: Robustness/sensitivity Analysismentioning

confidence: 72%

Evidence amalgamation in the sciences: an introduction

2018

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Amalgamating evidence from heterogeneous sources and across levels of inquiry is becoming increasingly important in many pure and applied sciences. This special issue provides a forum for researchers from diverse scientific and philosophical perspectives to discuss evidence amalgamation, its methodologies, its history, its pitfalls, and its potential. We situate the contributions therein within six themes from the broad literature on this subject: the variety-of-evidence thesis, the philosophy of meta-analysis, the role of robustness/sensitivity analysis for evidence amalgamation, its bearing on questions of extrapolation and external validity of experiments, its connection with theory development, and its interface with causal inference, especially regarding causal theories of cancer.

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Section: Robustness/sensitivity Analysismentioning

confidence: 72%

Evidence amalgamation in the sciences: an introduction

2018

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“…See Katzav (2013; for a version of this criticism that is specifically targeted at climate models. Kuorikoski, Lehtinen & Marchionni (2012) provide a rejoinder to Odenbaugh & Alexandrova's version of this argument.…”

Section: The Climate Sceptics' Argument and A Variety Of Evidencementioning

confidence: 88%

Derivational Robustness and Indirect Confirmation

Lehtinen

2017

Erkenn

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Derivational robustness may increase the degree to which various pieces of evidence indirectly confirm a robust result. There are two ways in which this increase may come about. First, if one can show that a result is robust, and that the various individual models used to derive it also have other confirmed results, these other results may indirectly confirm the robust result. Confirmation derives from the fact that data not known to bear on a result are shown to be relevant when it is shown to be robust. Second, robustness may increase the degree to which the robust result is indirectly confirmed if it increases the weight with which existing evidence indirectly confirms it. This may happen when it strengthens the connection between the core and the robust result by showing that auxiliaries are not responsible for the result.

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“…In recent years, there has been a resurgence of work on robustness (e.g., Weisberg 2006;Woodward 2006;Kuorikoski et al 2010Kuorikoski et al , 2012Soler et al 2012;Raerinne 2013;Hudson 2014).…”

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confidence: 99%

“…According to Wimsatt (1981Wimsatt ( , 1994, robustness is a criterion for the real. Weisberg (2006) and Kuorikoski et al (2010Kuorikoski et al ( , 2012 argue that robust theorems characterize or give information about the underlying causal relationships.…”

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confidence: 99%

“…Most of the recent discussion has focused on the sort of robustness analysis introduced by Levins and elaborated by Weisberg (2006), Woodward (2006), Kuorikoski et al (2010Kuorikoski et al ( , 2012, and others (from now on derivational robustness, DR). The idea of DR is that a modeler can use several idealized or simplified models of a system or phenomenon to derive a robust result that does not depend on any of the individual simplifying assumptions.…”

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confidence: 99%

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Robustness and reality

Eronen

2015

Synthese

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Robustness is often presented as a guideline for distinguishing the true or real from mere appearances or artifacts. Most of recent discussions of robustness have focused on the kind of derivational robustness analysis introduced by Levins, while the related but distinct idea of robustness as multiple accessibility, defended by Wimsatt, has received less attention. In this paper, I argue that the latter kind of robustness, when properly understood, can provide justification for ontological commitments. The idea is that we are justified in believing that things studied by science are real insofar as we have robust evidence for them. I develop and analyze this idea in detail, and based on concrete examples show that it plays an important role in science. Finally, I demonstrate how robustness can be used to clarify the debate on scientific realism and to formulate new arguments.

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Robustness analysis disclaimer: please read the manual before use!

Cited by 29 publications

References 15 publications

Evidence amalgamation in the sciences: an introduction

Evidence amalgamation in the sciences: an introduction

Derivational Robustness and Indirect Confirmation

Robustness and reality

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