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Call for Papers
12-13 OCTOBER 2017

Academy of Finland Centre of Excellence in the Philosophy of Social Sciences
University of Helsinki, Helsinki, Finland

What to make of highly unrealistic models? This is one of the big questions in contemporary philosophy of science, especially in philosophy of economics and biology.

Two sets of issues are relevant to answering this question. The first has to do with the ways in which highly unrealistic models should be characterized and the numerous ways in which models can be unrealistic. The key concepts here include those of representation and target, truth and falsity, abstraction and isolation, idealization and simplification, etc. Recent literature on models exhibits conceptual and terminological diversity and disagreement in characterizing unrealistic models. Different authors use different names to refer to highly unrealistic models: ‘toy model’, ‘fictional model’, ‘minimal model’, ‘non-representative model’, ‘model without a target’, ‘substitute model’, etc. Moreover, they sometimes use the same name to refer to different types of models. Neither the precise meanings nor the relations between these notions are clear in the literature.

The second set of issues has to do with the functions and uses of such unrealistic models. What purposes can they serve, and what purposes are actually pursued when using them? The main body of literature points to representational quality as grounding explanatory capacity despite abstraction, isolation, simplification and idealization. Others dispute this idea. Moreover, highly unrealistic models can serve other possible functions, next to their explanatory uses. Debates concerning the appropriate uses of highly unrealistic models need some tidying up.

TINT will host a workshop in Helsinki on 12-13 October 2017 in order to sort out some of the ambiguities and confusions in the literature and to contribute to a better understanding of the interpretations and uses of highly abstract and idealizing models. We are particularly interested in papers that (i) clarify the meaning of commonly used terms such as toy model, minimal model, fictional model, substitute model, etc, and that (ii) clarify the arguments for and against such models having explanatory import or some other epistemic or non-epistemic function. Papers that focus on and compare highly unrealistic models in economics and biology are particularly welcome.

If you would like to join us please send an extended abstract (750 – 1000 words) before 15 August 2017 to N. Emrah Aydinonat ( We plan to publish a selection of papers from the symposium as a journal’s special issue. For this reason, authors accepted for the workshop are required to submit an extended summary of their argument (2000 – 2500 words) before the event. Extended summaries will be distributed to all participants in advance. The workshop will consist of short presentations followed by extensive discussion.

Location: University of Helsinki, Helsinki, Finland
Date: 12-13 October 2017
Deadline for abstract submission:  15 August 2017
Announcement of accepted abstracts: 1 September 2017
Deadline for the extended summary: 1 October 2017

Organizers: N. Emrah Aydinonat, Till Grüne-Yanoff and Uskali Mäki


New Paper: Economic Models as Analogies by Itzhak Gilboa et. al.

New Paper: Economic Models as Analogies by Itzhak Gilboa et. al.

Gilboa, Itzhak; Andrew Postlewaite; Larry Samuelson & David Schmeidler (2011) “Economic Models as Analogies”, Penn Institute for Economic Research (PIER) Working Paper no.12-001, Online:

Abstract: People often wonder why economists analyze models whose assumptions are known to be false, while economists feel that they learn a great deal from such exercises. We suggest that part of the knowledge generated by academic economists is case-based rather than rule-based. That is, instead of offering general rules or theories that should be contrasted with data, economists often analyze models that are “theoretical cases”, which help understand economic problems by drawing analogies between the model and the problem. According to this view, economic models, empirical data, experimental results and other sources of knowledge are all on equal footing, that is, they all provide cases to which a given problem can be compared. We offer some complexity arguments that explain why case-based reasoning may sometimes be the method of choice; why economists prefer simple examples; and why a paradigm may be useful even if it does not produce theories.

Keywords: Methodology, Case-based reasoning
JEL: B40

Interesting discussion: 5 themes for Serious Economics!

Interesting discussion: 5 themes for Serious Economics!

Here is what interests me at the moment: the cluster of related models! Right now I am working on a paper (with Petri Ylikoski) which deals with the clustered nature of models in economics. (Our point concerns the philosophy of models.) So I’ll just note (for myself) the comments that relates to this issue.

Bruce Edmonds says:

“5. Recognising the need for clusters of related models of many kinds and levels. Following on from the last point, we are faced with a dilemma – complex models that relate more directly to what is observed but are hard to understand and analyse (i.e. relevance); or simpler models that dont relate to observations (at best to our ideas about what we observe) but that can Social phenomena are not only complex but that can be thoroughly understood (i.e. rigour). The truth is we need both rigour and relevance, which means we will not achieve this using one model or one technique. Rather we will have to make do with “clusters” of related models capturing the phenomena – different aspects, at different granularities, and at different levels of abstration. So, for example, we might acquire a series of representations of evidence from many different sources (ethnographic, statistical, social network, interviews, observations, lab experiments etc.), theser might be related to complex “data integration” simulation models that are consistent with as many of these as possible. Then this complex simulation might be a safe target for simplification and abstraction in other, simulation and alaytic models, since these can be adequately tested for relevance against the simulation model they are about.” Bruce Edmonds

Geoff Davies comments:

” “Clusters of related models”. Yes, you can’t hope, at the beginning, to make a model that includes everything that might be relevant. Even if you did, you wouldn’t understand its behaviour any better than you understand the real world. You have to start with simplified models that are not only tractable (with or without computers) but whose behaviour you can understand. This is true even though we know we’re dealing with a system that has emergent properties, and so you can’t use a simple reductionist approach. An example of a good approach I think is Steve Keen’s macro modelling of Minsky’s financial instability hypothesis. He has been progressively adding factors, and looking at the resulting (nonlinear, sometimes counter-intuitive) behaviour to see if it captures anything of the qualities of real world behaviour. (See, for example, . That’s all right Steve, you can scratch my back sometime.)

As you accumulate simplified models of various phenomena, you have to worry if they’re compatible. In my field, geophysicists and geochemists came to quite different pictures of Earth’s mantle, one layered, the other not. Such incompatibilities tell you there’s something important you (collectively) don’t understand. It has taken about three decades to begin to bring the two disciplines into compatibility, and the arguing is far from over.” Geoff Davies