Economic Modelling as Robustness Analysis

Kuorikoski, Jaakko; Lehtinen, Aki; Marchionni, Caterina

doi:10.1093/bjps/axp049

Cited by 143 publications

(114 citation statements)

References 39 publications

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“…The idea of robustness analysis has also been applied to pure mathematics: Krömer (2012) discusses a potential role for robustness in the foundations of mathematics, and Corfield (2010) focuses on mathematical structures exhibiting a surprising "confluence" of structural properties. The idea of robustness analysis has also been applied to cases from biology in Knuuttila and Loettgers (2011) and to economics in Kuorikoski, Lehtinen, and Marchionni (2010).…”

Section: Robustness Analysismentioning

confidence: 99%

The Volterra Principle Generalized

Räz¹

2017

Philos. of Sci.

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argue that the Volterra Principle can be derived from multiple predator-prey models and that, therefore, the Volterra Principle is a prime example for robustness analysis. In the current article, I give new results regarding the Volterra Principle, extending Weisberg's and Reisman's work, and I discuss the consequences of these results for robustness analysis. I argue that we do not end up with multiple, independent models but rather with one general model. I identify the kind of situation in which this generalization approach may occur, and I analyze the generalized Volterra Principle from an explanatory perspective.

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Section: Robustness Analysismentioning

confidence: 99%

The Volterra Principle Generalized

Räz¹

2017

Philos. of Sci.

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“…In particular, some note that models' implications frequently rest on unrealistic assumptions and hold that derivational robustness analysis can justifiably increase modellers' confidence in the robust theorems which connect these assumptions to specific modelling results (see e.g. Kuorikoski et al, 2010 and. A proponent of LMM may draw on these contributions to argue that scientific modellers can justifiably increase their confidence in hypotheses about real-world targets by relying on clusters of minimal models.…”

Section: Argument From Clusters Of Modelsmentioning

confidence: 99%

Why We Cannot Learn from Minimal Models

Fumagalli

2015

Erkenn

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Philosophers of science have developed several accounts of how consideration of scientific models can prompt learning about real-world targets. In recent years, various authors advocated the thesis that consideration of so-called minimal models can prompt learning about such targets. In this paper, I draw on the philosophical literature on scientific modelling and on widely cited illustrations from economics and biology to argue that this thesis fails to withstand scrutiny. More specifically, I criticize leading proponents of such thesis for failing to explicate in virtue of what properties or features minimal models supposedly prompt learning and for substantially overstating the epistemic import of minimal models. I then examine and refute several arguments one may put forward to demonstrate that consideration of minimal models can prompt learning about real-world targets. In doing so, I illustrate the implications of my critique for the wider debate on the epistemology of scientific modelling.

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“…Many other example RAs include cases from cognitive psychology (StolarzFantino et al 2003;Crupi et al 2008), "arguments from coincidence" in physics (Hacking 1983;Cartwright 1991;Mayo 1996), experimental biology (Culp 1994), climate science (Lloyd 2010;Parker 2011), and modeling in economics (Woodward 2006;Kuorikoski et al 2010). As should be evident from the range of these cases, I mean for the terms "results" and "means of detection" to be quite generic.…”

Section: Robustness Analysis In Sciencementioning

confidence: 99%

Robustness, Diversity of Evidence, and Probabilistic Independence

Schupbach

2015

Recent Developments in the Philosophy of Science: EPSA13 Helsinki

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In robustness analysis, hypotheses are supported to the extent that a result proves robust, and a result is robust to the extent that we detect it in diverse ways. But what precise sense of diversity is at work here? In this paper, I show that the formal explications of evidential diversity most often appealed to in work on robustnesswhich all draw in one way or another on probabilistic independence -fail to shed light on the notion of diversity relevant to robustness analysis. I close by briefly outlining a promising alternative approach inspired by Horwich's (1982) eliminative account of evidential diversity. Robustness Analysis in ScienceTo verify that results are not simply artifacts of the particular means used to detect them, scientists often attempt to duplicate those results using other, diverse means. To the extent that a result is detected via numerous, diverse means, it is said to be robust. Robustness analysis (henceforth, "RA") is a mode of reasoning in which one supports a hypothesis via an analysis of the conditions under which a result is robust.Examples of RA from scientific practice abound. Famously, biologist Richard Levins (1966) proposes RA as a general means for deciphering, when using simplified models to study complex systems, whether a result "depends on the essentials of a model or on the details of the simplifying assumptions:"[W]e attempt to treat the same problem with several alternative models each with different simplifications but with a common biological assumption. Then, if these models, despite their different assumptions, lead to similar results we have what we can call a robust theorem which is relatively free of the details of the model. Hence our truth is the intersection of independent lies.

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Economic Modelling as Robustness Analysis

Cited by 143 publications

References 39 publications

The Volterra Principle Generalized

The Volterra Principle Generalized

Why We Cannot Learn from Minimal Models

Robustness, Diversity of Evidence, and Probabilistic Independence

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