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Playing with networks: how economists explain

  • Original paper in Philosophy of Economics
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Abstract

Network theory is applied across the sciences to study phenomena as diverse as the spread of SARS, the topology of the cell, the structure of the Internet and job search behaviour. Underlying the study of networks is graph theory. Whether the graph represents a network of neurons, cells, friends or firms, it displays features that exclusively depend on the mathematical properties of the graph itself. However, the way in which graph theory is implemented to the modelling of networks differs significantly across scientific fields. This article compares the economics variant of network theory with those of other fields. It shows how the methodology employed by economists to model networks is shaped by two explanatory desiderata: that the explanandum phenomenon is based on micro-economic foundations and that the explanation is general.

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Notes

  1. The best-known comprehensive discussion of explanation in economics is in Blaug (1992), from which I have borrowed part of this paper’s title. Other contributions include Hausman (1992), Little (1991), Kincaid (2012), Reiss (2008). The views in Hausman (2009) and Mäki (2009) are largely consistent and partly inspired the view I propose here.

  2. Following common usage, I use network theory and graph theory interchangeably, even if network theory refers to the application of the mathematical theory of graphs to the study of networks.

  3. The degree distribution of a network describes how links are distributed across nodes.

  4. The level of clustering of a network measures the frequency with which relations among nodes are transitive. Transitivity indicates the extent to which if node A is linked to node B and B is linked to C, then A is linked to C. A graph is highly clustered when there are many transitivity relations among its nodes.

  5. Distance refers to the length (the number of steps) of the shortest path between two nodes.

  6. That is, P(k) ~ k −γ where P(k) is the fraction of nodes that have k connections to other nodes. Power laws have the property of scale-invariance.

  7. In economics the term ‘stylised fact’ is typically used to refer to a stylised description of a pattern obtained from the analysis of a particular body of data. The following empirical patterns obtained from the Add Health database and pertaining to friendship networks are examples of stylised facts: “larger groups tend to form more same-type ties and fewer other-type ties than small groups;” “larger groups tend to form ties per capita;” “all groups are biased towards same-type relative to demographics […]” (Currarini et al. 2009: 1003).

  8. Or why nodes are more likely to connect with nodes having many connections as in Barabási and Albert (1999). I am not aware of strategic models of network formation that provide micro-foundations to the preferential attachment process. Jackson and Rogers (2007) offer a hybrid model that derives the network from a random process and has implications for welfare.

  9. Clearly this is only so when the behaviour of nodes can be conceptualised in terms of strategic interactions. Therefore, the potential scope of application for economists’ models is narrower than that for physicists’ models. In addition, claiming that economics explanations are deeper does not amount to saying that they are better; they constitute improvements only on this particular dimension of explanatory power.

  10. The claim that the economic mechanisms involve the rational choices of agents is compatible with assumptions of full rationality and of bounded rationality.

  11. The terms “depth” and “breadth” come from Sober (1999).

  12. The narrow focus on a restricted set of explanatory factors and the systematic preference for certain explanatory virtues could find justification in the division of cognitive labour between the sciences dealing with networks. Evaluating the plausibility of this scenario goes beyond the scope of this article.

  13. More generally, a model’s format constrains its content and vice versa (see Morgan 2012). Exploring the multiple aspects of the relation between format and subject matter in a comparison between economics and physics models of networks would be worth a paper of its own.

  14. Whether current claims to unification are warranted does not depend on whether network science will or will not be fragmented. Moreover, even if network science will eventually develop as an autonomous field, this in itself does not warrant claims to the unification of phenomena across disciplinary boundaries.

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Acknowledgements

Earlier versions of this article were presented at the Philosophy of Science/TINT Seminar (Helsinki, 2010), the conference on Causation and Explanation in Physics, Biology and Economics (Barcelona, 2010), the STOREP conference (Trento, 2010) and the EIPE research seminar (Rotterdam, 2011). I thank the participants to these events for stimulating comments. In particular, I wish to thank Jaakko Kuorikoski, Till Grüne-Yanoff, Mario Maggioni, Ivan Moscati, Julian Reiss, Bradley Turner and Petri Ylikoski for their helpful comments and suggestions. I am especially indebted to Andrea Galeotti, who introduced me to network theory and kindly provided some of the initial material. Financial support from the Academy of Finland is also acknowledged. The usual disclaimers apply.

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  1. Finnish Centre of Excellence in the Philosophy of the Social Sciences, Department of Political and Economic Studies, University of Helsinki, P.O. Box 24, Unioninkatu 40A, Helsinki, 00014, Finland

    Caterina Marchionni

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Marchionni, C. Playing with networks: how economists explain. Euro Jnl Phil Sci 3, 331–352 (2013). https://doi.org/10.1007/s13194-013-0070-5

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