Segregation That No One Seeks

Muldoon, Ryan; Smith, Tony; Weisberg, M. K.

doi:10.1086/663236

Cited by 22 publications

(18 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using a formal language forces the reasoner to explicate implicit assumptions, and makes the comparison of inferences from similar yet different assumptions easier. This is what many sociologists and economists following Schelling's idea have been doing (Bruch & Mare 2006Clark & Fossett 2008;Fossett 2006;Macy & van de Rijt 2006;Muldoon, Smith & Weisberg 2012). They have shown that segregation indeed arises due to the 'tipping point' phenomenon, where the first moves of even a few dissatisfied agents can create an incentive for others to move.…”

Section: Models As Extended Cognitionmentioning

confidence: 89%

“…This accusation is quite unfair, since checkerboard models have been studied for over forty years by a multitude of scholars in various disciplines and thus many of the consequent findings should not be read back to the original models. The segregation equilibrium seems to be a robust outcome in many of the modifications to the model's structural assumptions (Aydinonat 2007;Muldoon, Smith & Weisberg 2012). Most changes in the size of the neighborhood, individual preferences, or the availability of apartments do not change the segregated outcome.…”

mentioning

confidence: 99%

See 1 more Smart Citation

External representations and scientific understanding

Kuorikoski

Ylikoski

2014

Synthese

View full text Add to dashboard Cite

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

show abstract

Section: Models As Extended Cognitionmentioning

confidence: 89%

mentioning

confidence: 99%

External representations and scientific understanding

Kuorikoski

Ylikoski

2014

Synthese

View full text Add to dashboard Cite

show abstract

“…Indeed, it is questionable whether Schelling's result was altogether surprising for other modellers. For although the degree of segregation that emerges in this model is much higher than individuals' preferences for homogeneity, individuals are still assumed to prefer some degree of homogeneity in their respective neighbourhoods (Muldoon et al, 2012). Taken together, these observations make it doubtful that the necessity hypothesis that is allegedly contradicted by Schelling's model was widely endorsed at the time this model was published.…”

Section: Schelling's Checkerboard Modelmentioning

confidence: 93%

“…In fact, recent studies have shown that segregation may emerge even in cases where individuals strictly prefer to live in diverse communities and be surrounded by agents of a different type (see e.g. Muldoon et al, 2012).…”

Section: Schelling's Checkerboard Modelmentioning

confidence: 99%

Why We Cannot Learn from Minimal Models

Fumagalli

2015

Erkenn

View full text Add to dashboard Cite

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.

show abstract

“…We will call it the family of models thesis. It turns out that the segregation equilibrium seems to be a robust outcome in many of the modifications to the model's assumptions (Aydinonat 2007;Muldoon, Smith & Weisberg 2012).…”

Section: Family Of Models Thesismentioning

confidence: 99%

Understanding with theoretical models

Ylikoski

Aydınonat

2014

Journal of Economic Methodology

View full text Add to dashboard Cite

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 ...

show abstract

Segregation That No One Seeks

Cited by 22 publications

References 15 publications

External representations and scientific understanding

External representations and scientific understanding

Why We Cannot Learn from Minimal Models

Understanding with theoretical models

Contact Info

Product

Resources

About