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On the neural enrichment of economic models: tractability, trade-offs and multiple levels of description

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Abstract

In the recent literature at the interface between economics, biology and neuroscience, several authors argue that by adopting an interdisciplinary approach to the analysis of decision making, economists will be able to construct predictively and explanatorily superior models. However, most economists remain quite reluctant to import biological or neural insights into their account of choice behaviour. In this paper, I reconstruct and critique one of the main arguments by means of which economists attempt to vindicate their conservative position. Furthermore, I develop an alternative defense of the thesis that economists justifiably rely on a methodologically distinctive approach to the modelling of choice behaviour.

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Notes

  1. The expression “decision making” is often used to denote both observed choice behaviour and the underlying cognitive and computational processes. In what follows, I employ such an expression to refer to observed choice behaviour, without taking a position as to whether economists qua economists should be concerned with those processes.

  2. Broadly defined, the domain of biological entities, events and processes would include the neural and the psychological domains as proper subsets. For the purpose of this paper, however, I use the term “biological” more narrowly to indicate the portion of the biological domain which is strictly distinct from the neural and the psychological ones.

  3. NE has been characterized in dissimilar ways both by economists and by other researchers. My use of the term is intended to be sufficiently general to encompass most of these characterizations. For a methodological comparison of different conceptions of NE, see e.g. Ross 2008 and Fumagalli 2010.

  4. The proponents of NE advocate the integration of neural insights in relation to both economic models and economic theory. The notions of “model” and “theory” have been characterized in several ways in the economic literature (see e.g. Mäki 1996, Sect. 5; Morgan and Morrison 1999, Chaps. 1–3), with most accounts making important distinctions between them. In this article, however, I speak of a neural enrichment of economic models and economic theory interchangeably, as the cogency of my considerations does not hinge on the difference between these two notions.

  5. In this paper, I use the expression “predictive power” to refer to both predictive accuracy, which denotes the exactness of a model’s observable implications regarding the investigated phenomena, and predictive reliability, which relates to the stability of a model’s predictive performance across distinct choice contexts.

  6. For instance, consider how the predictive gains offered by generalized expected utility models led many economists to alter the more tractable expected utility framework (see e.g. Starmer 2000, for a detailed review).

  7. See e.g. Mäki (1988, 1990, and 1992), on different senses in which the terms “realism” and “realisticness” are used in economics; see also Weisberg (2007a) and Matthewson and Weisberg (2009), on distinct notions of “generality” in biology.

  8. For example, as noted by Glimcher et al. (2005, p. 214), several NEs aim at constructing neurally enriched models that are highly parsimonious and predictive in a wide range of decision contexts. Parsimony and predictive power so frequently pull in contrasting directions that it is an open question whether NEs will manage to accomplish such an ambitious goal.

  9. Kahneman is far from being a strenuous defender of the traditional economic theory of choice. In fact, he was awarded the 2002 Nobel Prize in Economics “for having integrated insights from psychological research into economic science” (Nobel Press Release 2002).

  10. Various authors offer less convincing accounts of economists’ predilection for tractable models. To give one example, consider the observation by Gabaix and Laibson (2008, p. 295) that economists elevated modelling attributes such as tractability out of people’s tendency to “celebrate the things they do best”. Invoking this psychological propensity hardly accounts for the great importance economists attach to tractability. In particular, it fails to substantiate the claim that the reason why economists value this desideratum is because of their excellence at building tractable models.

  11. See e.g. Cartwright (1994), and Jones (2005), on abstractions; Mäki (1992), and Sugden (2000), on isolations; Cartwright (1983), McMullin (1985), Nowak (1972), and Weisberg (2007a), on idealizations.

  12. For example, NEs might replace the economists’ intertemporal discount rate with variables representing the activation patterns of neural areas whose operations influence agents’ intertemporal preferences.

  13. Various questions may be raised regarding how descriptive accuracy is most appropriately defined. My approximate characterization is sufficiently precise to enable me to examine the trade-offs I mention in this paper.

  14. For instance, economists typically aim at knowing not just whether observed choices are consistent with their predictions, but also why that is the case, since “it is only when we know that a theory works for the right reasons that we can be confident that it will continue to work” in different contexts (Schotter 2008, p. 79; see also Weisberg 2007a, p. 17, for a similar claim in the literature on biological modelling).

  15. For example, while rational choice theorists represent observed choices as the maximization of a utility function under some suitably defined constraints, cognitive psychologists account for those data in terms of specific heuristics and cognitive mechanisms, computational neuroscientists attempt to identify what neural algorithms underlie the observed decisions, and so on.

  16. I speak of “higher-level” and “lower-level” insights following an entrenched terminological convention in the philosophy of science literature. My doing so does not commit me to endorse the hierarchical view of the structure of science often held by those who employ these expressions.

  17. Similar remarks have been put forward in the recent literature on NE. For instance, Kuorikoski and Ylikoski (2010, p. 223) argue that the range of what if -questions that can be answered on the basis of the hitherto identified correspondences between neural areas’ activations and observed decisions is “very limited”. In their view, modelling agents’ choices in psychological terms enables one to answer a “broader range of what if -questions concerning possible alterations in the agent’s valuations, knowledge and how the relevant information is presented”.

  18. Among the constraints which will not abate with scientific advances, Matthewson and Weisberg (2009, p. 188) include strict trade-offs, increase trade-offs and Levins trade-offs. Two attributes display a strict trade-off “when an increase in the magnitude of one desideratum necessarily results in a decrease in the magnitude of the second, and vice versa”. An increase trade-off occurs when the magnitudes of two desiderata cannot be simultaneously increased. Finally, two attributes exhibit a Levins trade-off “when the magnitude of both of these attributes cannot be simultaneously maximized”.

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Acknowledgments

I am grateful to J. McKenzie Alexander, Richard Bradley, Francesco Guala, Ivan Moscati and Michael Weisberg for the helpful comments they provided on earlier versions of this paper. I have also benefited from the valuable observations of two anonymous referees and the participants in seminars at Bocconi University, San Raffaele University and the University of Valencia.

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  1. Department of Philosophy, Logic and Scientific Method, London School of Economics, London, UK

    Roberto Fumagalli

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Fumagalli, R. On the neural enrichment of economic models: tractability, trade-offs and multiple levels of description. Biol Philos 26, 617–635 (2011). https://doi.org/10.1007/s10539-011-9272-4

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