Understanding with theoretical models
Petri Ylikoski and
N. Emrah Aydinonat ()
Journal of Economic Methodology, 2014, vol. 21, issue 1, 19-36
Abstract:
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 a 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.
Date: 2014
References: Add references at CitEc
Citations: View citations in EconPapers (5)
Downloads: (external link)
http://hdl.handle.net/10.1080/1350178X.2014.886470 (text/html)
Access to full text is restricted to subscribers.
Related works:
Working Paper: Understanding with Theoretical Models (2017)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:jecmet:v:21:y:2014:i:1:p:19-36
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RJEC20
DOI: 10.1080/1350178X.2014.886470
Access Statistics for this article
Journal of Economic Methodology is currently edited by John Davis and D Wade Hands
More articles in Journal of Economic Methodology from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().