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TWO STYLES OF NEUROECONOMICS

Published online by Cambridge University Press:  01 November 2008

Don Ross
Don Ross*
Affiliation:
University of Cape Town and University of Alabama at Birmingham
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Abstract

I distinguish between two styles of research that are both called “neuroeconomics”. Neurocellular economics (NE) uses the modelling techniques and mathematics of economics – constrained maximization and equilibrium analysis – to model relatively encapsulated functional parts of brains. This approach rests upon the fact that brains are, like markets, massively distributed information-processing networks over which executive systems can exert only limited and imperfect governance. Harrison's (2008) deepest criticisms of neuroeconomics do not apply to NE. However, the more famous style of neuroeconomics is behavioural economics in the scanner. This is often motivated by complaints about conventional economics frequently heard from behavioural economists. It attempts to use neuroimaging data to justify arguments for replacing standard aspects of microeconomic theory by facts and conjectures about human psychology. Harrison's grounds for unease about neuroeconomics apply to most BES, or at least to its explicit methodology. This methodology is naively reductionist and illegitimately assumes that economics should not do what all successful science does, namely, model abstract aspects of its target phenomena instead of would-be complete and fully ecologically situated facsimiles of them.

Type
Essay
Information
Economics & Philosophy , Volume 24 , Issue 3: Neuroeconomics , November 2008 , pp. 473 - 483
Copyright
Copyright © Cambridge University Press 2008

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References

REFERENCES

Camerer, C., Loewenstein, G. and Prelec, D.. 2005. Neuroeconomics: how neuroscience can inform economics. Journal of Economic Literature 43: 9–64.CrossRefGoogle Scholar
Caplin, A. and Dean, M.. 2007. The neuroeconomic theory of learning. American Economic Review 97: 148–52.CrossRefGoogle Scholar
Christensen, W. 2007. The evolutionary origins of volition. In Distributed cognition and the will, ed. Ross, D., Spurrett, D., Kincaid, H. and Stephens, G. L., 255–87. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Clark, A. 1989. Microcognition. Cambridge, MA: MIT Press.Google Scholar
Clark, A. 1997. Being There. Cambridge, MA: MIT Press.Google Scholar
Daw, N. 2003. Reinforcement learning models of the dopamine system and their behavioral implications. Doctoral dissertation, Carnegie Mellon University, Pittsburgh.Google Scholar
Dennett, D. 1991. Consciousness explained. Boston: Little Brown.Google Scholar
Friedman, M. 1953. Essays in positive economics. Chicago: University of Chicago Press.Google Scholar
Gallistel, C. 1990. The organization of learning. Cambridge, MA: MIT Press.Google Scholar
Gallistel, C. and Gibbon, J.. 2000. Time, rate and conditioning. Psychological Review 107: 289–344.CrossRefGoogle ScholarPubMed
Glimcher, P. 2003. Decisions, uncertainty and the brain. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Glimcher, P., Kable, J. and Louie, K.. 2007. Neuroeconomic studies of impulsivity: now or just as soon as possible? American Economic Review 97: 142–7.CrossRefGoogle Scholar
Gul, F. and Pesendorfer, W.. 2008. The case for mindless economics. In The foundations of positive and normative economics, ed. Caplin, A. and Schotter, A., 3–41. Oxford: Oxford University Press.Google Scholar
Harrison, G. W. 2008. Neureconomics: a critical reconsideration. Economics and Philosophy 24.CrossRefGoogle Scholar
Huettel, S., Song, A. and McCarthy, G.. 2004. Functional magnetic resonance imaging. Sunderland, MA: Sinauer.Google ScholarPubMed
Knutson, B., Rick, S., Wimmer, G. E., Prelec, D. and Loewenstein, G.. 2007. Neural predictors of purchases. Neuron 53: 147–56.Google ScholarPubMed
McCabe, K. A. Neuroeconomics and the economic sciences. Economics and Philosophy 24.Google Scholar
McClure, S., Laibson, D., Loewenstein, G. and Cohen, J.. 2004. Separate neural systems value immediate and delayed monetary rewards. Science 306: 503–7.CrossRefGoogle ScholarPubMed
Montague, R. and Berns, G.. 2002. Neural economics and the biological substrates of valuation. Neuron 36: 265–84.Google ScholarPubMed
Montague, R., Dayan, P. and Sejnowski, T.. 1996. A framework for mesencephalic dopamine systems based on predictive Hebbian learning. Journal of Neuroscience 16: 1936–47.Google ScholarPubMed
Platt, M. and Glimcher, P.. 1999. Neural correlates of decision variables in parietal cortex. Nature 400: 233–8.CrossRefGoogle ScholarPubMed
Robbins, L. 1935. An essay on the nature and significance of economic science, 2nd Edn. London: Macmillan.Google Scholar
Ross, D. 2005. Economic theory and cognitive science: Microexplanation. Cambridge, MA: MIT Press.Google Scholar
Ross, D. forthcoming. The economic agent: not human, but important. In Handbook of the Philosophy of Science, Vol. 13. Economics, ed. Mäki, U.. Amsterdam: Elsevier.Google Scholar
Satz, D. and Ferejohn, J.. 1994. Rational choice and social theory. Journal of Philosophy 91: 71–87.CrossRefGoogle Scholar
Schultz, W., Dayan, P. and Montague, P. R.. 1997. A neural substrate of prediction and reward. Science 275: 1593–9.CrossRefGoogle ScholarPubMed
Sen, A. 1977. Rational fools. Philosophy and Public Affairs 6: 317–44.Google Scholar
Sen, A. 1987. On ethics and economics. Oxford: Blackwell.Google Scholar
Stigum, B. 1990. Toward a formal science of economics. Cambridge, MA: MIT Press.Google Scholar
Sutton, R. and Barto, A.. 1998. Reinforcement learning: An introduction. Cambridge, MA: MIT Press.Google Scholar