Abstract
Socio-economic systems can often successfully be treated as complex systems in the statistical physics sense. This means that the complexity resides in the emerging dynamical behaviour, not in a complicated composition. In order to understand why physics can help to understand socio-economic phenomena with complex behaviour in this sense, I argue that it is necessary to adopt a structural perspective. Accordingly, one has to modify the notion of mechanistic explanations, partly by broadening it. One crucial tool for finding mechanistic explanations in such a structural sense, are minimal models, i.e. models that abstract from micro details in a drastic way. I will show why mechanistic explanations using minimal models for complex dynamical behaviour naturally lead to a structural notion of mechanisms. Among other things I will deal with two diverging views. One of these is that the application of physics-based models to socio-economic phenomena is (mostly) unwarranted to begin with. The other diverging view is that, while the use is in fact warranted, the resulting explanation of socio-economic phenomena is either non-causal or, if causal, not in a mechanistic sense.
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Notes
- 1.
For a more detailed discussion of this difference, see Kuhlmann (2011).
- 2.
- 3.
Mantegna and Stanley (2000) is arguably still the best-known introduction to econophysics.
- 4.
See Beinhocker’s (2007) book The Origin of Wealth: Evolution, Complexity, and the Radical Remaking of Economics for a comprehensive up-to-date survey of the debate, which addresses a wider audience and stresses many aspects that are interesting for my investigation of econophysics. Note that Beinhocker talks about ‘complexity economics’ in contrast to ‘traditional economics’.
- 5.
The classification ‘stable’ means that the sum of two independent identically distributed random variables of the Lévy type results in a distribution with the same shape. See Bouchaud and Potters (2000: sect. 1.5.3) for details. The Gaussian distribution is a more common example of a stable distribution.
- 6.
Also, see Mandelbrot (1963).
- 7.
See Mandelbrot and Hudson (2004) for a very readable introduction to Mandelbrot’s contributions from an ex post perspective.
- 8.
This model itself builds upon the simpler and less detailed stock-market model by Bouchaud and Cont (1998).
- 9.
The model itself was put forward by Ernst Ising (1925). Only in 1944 Lars Onsager famously supplied an exact computation of the thermodynamical properties of the two-dimensional Ising model at the critical point.
- 10.
- 11.
- 12.
- 13.
Note that Knuuttila and Loettgers look at socio-economic applications in general and not specifically at econophysics. In fact, they don’t even mention the term ‘econophysics’. However, they study the work of some of the founders of econophysics, such as Stanley and Stauffer, and, in any case, their considerations seem equally relevant for econophysics.
- 14.
JPW are primarily concerned with one particular example, namely the financial market model of Johansen et al. (2000) (henceforth “JLS”). It is similar to the example I introduced above, at least for the purposes of the current discussion. They focus on the question to which extent this model does in fact treat crashes and critical phase transitions as analogous.
- 15.
- 16.
See Woodward (2002: sect. 4.3) for a very illuminative discussion.
- 17.
- 18.
Cartwright’s (1983: ch. 8) so-called ‘simulacrum account of explanation’, which is also non-law-based, is arguably the approach where models and explanations stand in the closest connection with each other.
- 19.
Batterman (2005) has a similar point, when he argues that highly idealized and oversimplified models can sometimes be better for the explanation of the dominant phenomenon, e.g. a phase transition, than a detailed model in terms of micro-constituents.
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Kuhlmann, M. (2019). Crossing Boundaries: Why Physics Can Help Understand Economics. In: Falkenburg, B., Schiemann, G. (eds) Mechanistic Explanations in Physics and Beyond. European Studies in Philosophy of Science, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-030-10707-9_10
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