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On the neural enrichment of economic models: recasting the challenge

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Abstract

In a recent article in this Journal, Fumagalli (Biol Philos 26:617–635, 2011) argues that economists are provisionally justified in resisting prominent calls to integrate neural variables into economic models of choice. In other articles, various authors engage with Fumagalli’s argument and try to substantiate three often-made claims concerning neuroeconomic modelling. First, the benefits derivable from neurally informing some economic models of choice do not involve significant tractability costs. Second, neuroeconomic modelling is best understood within Marr’s three-level of analysis framework for information-processing systems. And third, neural findings enable choice modellers to confirm the causal relevance of variables posited by competing economic models, identify causally relevant variables overlooked by existing models, and explain observed behavioural variability better than standard economic models. In this paper, I critically examine these three claims and respond to the related criticisms of Fumagalli’s argument. Moreover, I qualify and extend Fumagalli’s account of how trade-offs between distinct modelling desiderata hamper neuroeconomists’ attempts to improve economic models of choice. I then draw on influential neuroeconomic studies to argue that even the putatively best available neural findings fail to substantiate current calls for a neural enrichment of economic models.

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Notes

  1. Fumagalli’s (2011) refined argument from tractability differs from former critiques of NE, which prevalently target purported methodological flaws in NEs’ studies (see e.g. Harrison 2008; Harrison and Ross 2010), putative limitations in the accuracy and reliability of NEs’ findings (see e.g. Bernheim 2009; Rubinstein 2008), and the alleged irrelevance of such findings for economic modellers (see e.g. Gul and Pesendorfer 2008). I shall comment on these critiques and their interrelations in various places throughout the paper.

  2. NE findings might foster the development of new methods for measuring individuals’ welfare and evaluating policies’ welfare implications on the basis of neural activity. I gloss over these potential normative contributions since my evaluation focuses on positive economic analyses. For a discussion of NE’s potential contributions to normative economic analyses, see e.g. Bernheim and Rangel 2007; 2008, 2009; Fumagalli 2013, 2016a.

  3. The term ‘tractability’ may be used to designate not just a property of models, but also a property of the activities of modelling, namely “the ease with which modellers can build [a] model or manipulate it” (Colombo 2015, 727). In this paper, I focus on tractability as a property of models—rather than the activities of modelling—since the controversy about NEEM prevalently concerns the former notion (see e.g. Colombo 2015, 727, for a similar remark).

  4. This justificatory requirement may be regarded as more or less demanding depending on how one interprets the expression ‘wide range’. However, this interpretative concern has limited bearing on Fumagalli’s challenge. For on most plausible interpretations of ‘wide range’, pointing to a few selected descriptively accurate and tractable NE models of the neural substrates of choice falls short of indicating that NEs can provide such models for a wide range of decision problems targeted by economists.

  5. This is not the only respect in which Colombo’s defence of the tractability thesis seems to mischaracterize Fumagalli’s argument. Two such mischaracterizations are relevant for appraising NEs’ calls for NEEM. First, Colombo takes Fumagalli to infer that “since modelling choice behaviour at the neural level would involve too high modelling costs in comparison to models incorporating variables at some other level, economists should refrain from modelling choice behaviour at the neural level” (2015, 715). However, Fumagalli (2011, 627–631) repeatedly emphasizes that assessing NEs’ calls for NEEM involves a comparative evaluation of both modelling costs and modelling benefits. Reconstructing his argument as if it concerned exclusively modelling costs oversimplifies both the structure and the implications of such argument (see Fumagalli 2011, 627–631, for discussion). And second, pace Colombo, Fumagalli nowhere asserts that “providing a descriptively accurate and tractable model of choice prevents economists from incorporating variables at the neural level” (2015, 715, italics added). On the contrary, Fumagalli’s argument is premised on the pluralistic assumption that economists “may fruitfully combine neural and other disciplines’ insights in constructing particular models of choice” (2011, 633). In this respect, Colombo appears to miss the pluralistic spirit of Fumagalli’s argument (see Weisberg et al. 2011, 613; see also “The Marr thesis and the goals of NE”).

  6. Different positions as to how exactly each level of analysis is to be conceptualized have been advocated (see e.g. Bechtel and Shagrir 2015; Kitcher 1988; Shagrir 2010). The remarks in the text are sufficiently detailed for the purpose of my evaluation.

  7. A proponent of NE might object that the contrastive character of the question whether ‘human choice behaviour is more conveniently modelled at the neural - rather than some other - level’ does not fit well NEs’ insistence on combining findings from multiple disciplines. However, one may consistently acknowledge that NEs aim to combine findings from multiple disciplines, yet argue that NEs’ attempts to implement such combination face pragmatic and epistemic challenges (see e.g. Fumagalli 2011, 2013). In this respect, no misunderstanding of NE methodology seems inherent in the question whether ‘human choice behaviour is more conveniently modelled at the neural - rather than some other - level’.

  8. Niv et al. might rebut that what they call ‘utility model’ is plausibly regarded as an economic model on the alleged ground that such model “is the standard explanation for risk sensitivity from economics” (Niv et al. 2012, 555). I address this rebuttal in point 4.2.3 below.

  9. This does not exclude that one may capture nonparametrically the testable implications of choice models that contain latent variables. In fact, leading economists have already applied rigorous axiomatic approaches to test reward prediction error (RPE) models of the same class as those compared by Niv et al. For instance, Caplin and Dean (2008b) use an axiomatic approach to test RPE models with neuro-biological data and identify three axiomatic conditions that characterize the entire class of RPE models in a simple, nonparametric way. These axiomatic conditions yield a minimal requirement for RPE models in the sense that if neural activity is to satisfy any one of the class of RPE models, then such activity must also satisfy those axiomatic conditions (Caplin et al. 2010).

  10. A proponent of NEEM might conjecture that the “heterogeneity of risk attitudes at extended timescales might […] be partly explained by people’s varying levels of short-timescale […] risk sensitivity” (Colombo 2015, 726). This conjecture highlights the potential of neural findings to shed light on the neural substrates of intertemporal behavioural patterns. However, due to the limitations constraining the evidential and explanatory relevance of current neural findings for the modelling of the real-life decision problems targeted by economists (see point 4.2.3 below), such conjecture does not presently support the claim that economists should build their models of choice on neuro-biological presuppositions and include neuro-biological variables into their models.

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Acknowledgments

I thank J. McKenzie Alexander, Cristina Bicchieri, Matteo Colombo, Uskali Mäki, David Papineau, Don Ross, Jack Vromen, Michael Weisberg and two anonymous referees for their comments on former versions of this paper. I also received helpful feedback from audiences at the University of California San Diego, the University of Pennsylvania, the Munich Center for Mathematical Philosophy, the University of Cape Town, and New York University.

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  1. University of Bayreuth, Bayreuth, Germany

    Roberto Fumagalli

  2. London School of Economics, London, UK

    Roberto Fumagalli

  3. University of Pennsylvania, Pennsylvania, PA, USA

    Roberto Fumagalli

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Fumagalli, R. On the neural enrichment of economic models: recasting the challenge. Biol Philos 32, 201–220 (2017). https://doi.org/10.1007/s10539-016-9546-y

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