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Goals, Methods, and Progress in Neuroeconomics

Author

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  • Colin F. Camerer

    (Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, California 91125)

Abstract

Neuroeconomics shares the main goals of microeconomics: to understand what causes choices, and the welfare properties of choice. The novel goal is linking mathematical constructs and observable behavior to mechanistic details of neural circuitry. Several complementary methods are used. An initial insight from neuroscience is that distinct systems guide choice: Pavlovian and instrumental conditioning (learning) of state-value and response-value associations, overlearned habits, and model- (or goal-) directed value that requires deliberation. These systems can differ economically from rational choice—for example, habitual choices have low utility and price elasticities, whereas model-directed values are often constructed preferences. Neuroeconomics also provides evidence of situations in which utility maximization either works well (in simple binary choice) or benefits from the introduction of behavioral constructs. Neuroeconomics is well equipped to guide the theory of how choices depend on mental states, such as fear or cognitive load. Examples include extensive studies of risk and time preference, finance, and neural decoding of private information.

Suggested Citation

  • Colin F. Camerer, 2013. "Goals, Methods, and Progress in Neuroeconomics," Annual Review of Economics, Annual Reviews, vol. 5(1), pages 425-455, May.
  • Handle: RePEc:anr:reveco:v:5:y:2013:p:425-455
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    File URL: http://www.annualreviews.org/doi/abs/10.1146/annurev-economics-082012-123040
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    Citations

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    Cited by:

    1. Sulka, Tomasz, 2022. "Planning and saving for retirement," DICE Discussion Papers 384, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    2. Daniel Serra, 2021. "Decision-making: from neuroscience to neuroeconomics—an overview," Theory and Decision, Springer, vol. 91(1), pages 1-80, July.
    3. Clithero, John A., 2018. "Improving out-of-sample predictions using response times and a model of the decision process," Journal of Economic Behavior & Organization, Elsevier, vol. 148(C), pages 344-375.
    4. Smith, Trenton G., 2023. "Endocrine state is the physical manifestation of subjective beliefs," Journal of Economic Psychology, Elsevier, vol. 96(C).
    5. Huseynov, Samir & Palma, Marco A. & Ahmad, Ghufran, 2021. "Does the magnitude of relative calorie distance affect food consumption?," Journal of Economic Behavior & Organization, Elsevier, vol. 188(C), pages 530-551.
    6. Piotr Majer & Peter Mohr & Hauke Heekeren & Wolfgang Karl Härdle, 2014. "Portfolio Decisions and Brain Reactions via the CEAD method," SFB 649 Discussion Papers SFB649DP2014-036, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    7. Alexandre Truc, 2022. "Neuroeconomics Hype or Hope? An Answer," GREDEG Working Papers 2022-26, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.
    8. Clithero, John A., 2018. "Response times in economics: Looking through the lens of sequential sampling models," Journal of Economic Psychology, Elsevier, vol. 69(C), pages 61-86.

    More about this item

    Keywords

    fMRI; behavioral economics; neural circuitry; emotion; reward;
    All these keywords.

    JEL classification:

    • D03 - Microeconomics - - General - - - Behavioral Microeconomics: Underlying Principles
    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General

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