Incredible Worlds, Credible Results

Kuorikoski, Jaakko; Lehtinen, Aki

doi:10.1007/s10670-008-9140-z

Cited by 47 publications

(49 citation statements)

References 31 publications

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“…For example, writing M1 = (A1&A2&A3)├ R means that assumptions A1, A2, and A3 are being used to derive result R from model M1. Although accounts that emphasise the importance of inferential issues in modelling would be particularly suitable (Suárez 2004;Kuorikoski and Lehtinen 2009), this way of presenting modelling inferences does not imply a commitment to any specific interpretation of what models are. As a matter of fact, it would be possible to remove the symbols indicating models (M1, M2 etc.)…”

Section: Indirect Confirmation Models and Robustnessmentioning

confidence: 99%

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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Section: Indirect Confirmation Models and Robustnessmentioning

confidence: 99%

Derivational Robustness and Indirect Confirmation

Lehtinen

2017

Erkenn

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“…How could it be explanatory despite its seeming dissimilarity to everything we know about the formation of ethnically segregated neighborhoods? Recently, a number of philosophers of economics have tried to answer these questions (Cartwright 2009;de Donato and Zamora-Bonilla 2009;Grüne-Yanoff 2009;Knuuttila 2009;Kuorikoski and Lehtinen 2009;Mäki 2009;Sugden 2009). These papers mostly focus on the relation between a model (e.g., the checkerboard model) and its supposed target.…”

Section: Schelling's Checkerboardmentioning

confidence: 99%

Understanding with theoretical models

Ylikoski

Aydınonat

2014

Journal of Economic Methodology

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This paper discusses the epistemic import of highly abstract and simplified theoretical models using Thomas Schelling's checkerboard model as an example. We argue that the epistemic contribution of theoretical models can be better understood in the context of a cluster of models relevant to the explanatory task at hand. The central claim of the paper is that theoretical models make better sense in the context of the menu of possible explanations. In order to justify this claim, we introduce a distinction between causal scenarios and causal mechanism schemes.These conceptual tools help us to articulate the basis for modelers' intuitive confidence that their models make an important epistemic contribution. By focusing on the role of the menu of possible explanations in the evaluation of explanatory hypotheses, it is possible to understand how a causal mechanism scheme can improve our explanatory understanding even in cases where it does not describe the actual cause of a particular phenomenon. Highly abstract and simplified theoretical models have an important role in many sciences, for example, in evolutionary biology and economics. Although both scientists and philosophers have expressed doubts about the epistemic import of these idealized models, many scientists believe that they provide explanatory insight into real-world phenomena. Understanding the epistemic value of these abstract representations is one of the key challenges for philosophers of science who attempt to make sense of scientific modeling. In this paper we will examine ThomasSchelling's checkerboard model as an example of abstract theoretical models and articulate various ways in which it expands the social scientific understanding of segregation processes.We argue that the epistemic contribution of theoretical models can only be fully understood in the context of a cluster of models relevant to the explanatory task at hand. To disambiguate this claim, we introduce a distinction between two different cluster claims. The first, the family of models thesis, states that the epistemic value of theoretical models is not well understood if they are treated as isolated representations. It makes more sense when considered in the context of as a family of related models. Our second cluster claim, the competing causal mechanisms thesis, says that the full epistemic contribution of theoretical models can only be understood in the context of competing explanations for the same phenomenon. In other words, theoretical models make better sense in the context of the menu of possible explanations.The structure of the paper is as follows. In the first section, we start by making some observations about Schelling's famous checkerboard model and introduce our two cluster theses in the second section. In order to understand our second thesis, which brings up the contribution of the menu of possible explanations, it is necessary to understand the nature of how-possibly explanations (HPEs). Thus, in the third section, we introduce an enriched account of HPEs ...

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“…What matters is the ability to use it to make correct what--if inferences about the object of interest. When the inferences from the assumptions to the explanandum are accomplished with the aid of an external medium (such as pen and paper, computer, or a checkerboard), and a set of formal truth--preserving inferential rules, the relevant cognitive system is not the modeler alone, but the model--modeler pair (Kuorikoski and Lehtinen 2009). This makes the extended cognitive system composed of the modeler and the external inferential apparatus the understanding subject, whose inferential performance is constitutive of understanding.…”

Section: Models As Extended Cognitionmentioning

confidence: 99%

“…Finally, we apply our theory of the 1 This should not be taken as a stance in the ontology of models discussion. We do not deny that it makes sense to abstract away from the concrete instantiations of these artifacts to their inferential properties and define the identity in these terms (see Kuorikoski and Lehtinen 2009). dimensions of explanatory power to model--based explanations. We aim to present an overall perspective on model--based understanding, and for this reason we cannot provide a detailed exposition of all aspects of our account.…”

Section: Introductionmentioning

confidence: 97%

“…In this paper we show how these challenges can be met with an inferential conception of understanding. The basic elements of our account have been developed in some of our earlier papers (Kuorikoski 2011, Kuorikoski & Lehtinen 2009, Ylikoski 2013, 2014, Ylikoski & Aydinonat 2014, but this paper brings these ideas together and expands them into a systematic account of model--based explanation. We argue that approaching model--based reasoning as extended cognition and adopting an inferentialist analysis of representation dissolves both of the two central questions, and shows that the sense of paradox associated with model--based explanations arises from faulty philosophical assumptions.…”

Section: Introductionmentioning

confidence: 99%

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External representations and scientific understanding

Kuorikoski

Ylikoski

2014

Synthese

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This paper provides an inferentialist account of model--based understanding by combining a counterfactual account of explanation and an inferentialist account of representation with a view of modeling as extended cognition. This account makes it understandable how the manipulation of surrogate systems like models can provide genuinely new empirical understanding about the world. Similarly, the account provides an answer to the question how models, that always incorporate assumptions that are literally untrue of the model target, can still provide factive explanations. Finally, the paper shows how the contrastive counterfactual theory of explanation can provide tools for assessing the explanatory power of models.2

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Incredible Worlds, Credible Results

Cited by 47 publications

References 31 publications

Derivational Robustness and Indirect Confirmation

Derivational Robustness and Indirect Confirmation

Understanding with theoretical models

External representations and scientific understanding

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