Causal isolation robustness analysis: the combinatorial strategy of circadian clock research

Knuuttila, Tarja; Loettgers, Andrea

doi:10.1007/s10539-011-9279-x

Cited by 11 publications

(8 citation statements)

References 40 publications

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“…Such an explanation might be "causally adequate" in a broad sense. Robustness 3 analysis in comparative modeling can help establish causal adequacy (Knuuttila and Loettgers 2011), as Weisberg explains.…”

Section: Causal Adequacy (Tv2)mentioning

confidence: 99%

Systematizing the theoretical virtues

Keas¹

2017

Synthese

141

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There are at least twelve major virtues of good theories: evidential accuracy, causal adequacy, explanatory depth, internal consistency, internal coherence, universal coherence, beauty, simplicity, unification, durability, fruitfulness, and applicability. These virtues are best classified into four classes: evidential, coherential, aesthetic, and diachronic. Each virtue class contains at least three virtues that sequentially follow a repeating pattern of progressive disclosure and expansion. Systematizing the theoretical virtues in this manner clarifies each virtue and suggests how they might have a coordinated and cumulative role in theory formation and evaluation across the disciplines-with allowance for discipline specific modification. An informal and flexible logic of theory choice is in the making here. Evidential accuracy (empirical fit), according to my systematization, is not a largely isolated trait of good theories, as some (realists and antirealists) have made it out to be. Rather, it bears multifaceted relationships, constituting significant epistemic entanglements, with other theoretical virtues.

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Section: Causal Adequacy (Tv2)mentioning

confidence: 99%

Systematizing the theoretical virtues

Keas¹

2017

Synthese

141

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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%

“…See Knuuttila and Loettgers (2016) for a discussion of the contrast between Lotka's and Volterra's approach to scientific modeling in general and to their eponymous model in particular. 2.…”

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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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“…I propose that models are primarily informative from a relational perspective where they are connected to other models. The activity of modeling thus has an inherent systemic nature in being coupled to other models and entities, and the modeling activity should be seen in the context of how different models are brought together (see also Wimsatt, 1981;Weisberg, 2006;Winther, 2006;Knuuttila & Loettgers, 2011). The consequence of this view is that it is more important how models help us gain knowledge about an unknown target system through the relations to other representations.…”

Section: The Role Of Models In Knowledge Generationmentioning

confidence: 99%

“…In understanding this step toward an experimental realization, the material resistance of biological phenomena is important. It provides a challenge to purely theoretical forms of coherence, and the coupling of in vivo and in vitro approaches is thus crucial for creating strong resonant links between theoretically and materially constrained representations (Rheinberger, 1997;Knuuttila & Loettgers, 2011). The material constraints of model organisms explain why they often have a special epistemic status.…”

Section: Epistemic Objects At the Intersection Of Constraintsmentioning

confidence: 99%

When one model is not enough: Combining epistemic tools in systems biology

Green

2013

Studies in History and Philosophy of Science Part C: Studies in

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In recent years, the philosophical focus of the modeling literature has shifted from descriptions of general properties of models to an interest in different model functions. It has been argued that the diversity of models and their correspondingly different epistemic goals are important for developing intelligible scientific theories (Levins, 2006;Leonelli, 2007). However, more knowledge is needed on how a combination of different epistemic means can generate and stabilize new entities in science. This paper will draw on Rheinberger's practice-oriented account of knowledge production. The conceptual repertoire of Rheinberger's historical epistemology offers important insights for an analysis of the modelling practice. I illustrate this with a case study on network modeling in systems biology where engineering approaches are applied to the study of biological systems. I shall argue that the use of multiple means of representations is an essential part of the dynamic of knowledge generation. It is because of -rather than in spite of -the diversity of constraints of different models that the interlocking use of different epistemic means creates a potential for knowledge production.

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Causal isolation robustness analysis: the combinatorial strategy of circadian clock research

Cited by 11 publications

References 40 publications

Systematizing the theoretical virtues

Systematizing the theoretical virtues

The Volterra Principle Generalized

When one model is not enough: Combining epistemic tools in systems biology

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