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Belief Adjustment: A Double Hurdle Model and Experimental Evidence

Author

Listed:
  • Timo Henckel
  • Gordon D. Menzies
  • Peter G. Moffatt
  • Daniel J. Zizzo

Abstract

We present an experiment where subjects sequentially receive signals about the true state of the world and need to form beliefs about which one is true, with payoffs related to reported beliefs. We control for risk aversion using the Offerman et al. (2009) technique. Against the baseline of Bayesian updating, we test for belief adjustment under-reaction and over-reaction and model the decision making process of the agent as a double hurdle model where agents first decide whether to adjust their beliefs and then, if so, decide by how much. We find evidence for periods of belief inertia interspersed with belief adjustment. This is due to a combination of: random belief adjustment; state dependent belief adjustment, with many subjects requiring considerable evidence to change their beliefs; and Quasi-Bayesian belief adjustment, with insufficient belief adjustment when a belief change does occur.

Suggested Citation

  • Timo Henckel & Gordon D. Menzies & Peter G. Moffatt & Daniel J. Zizzo, 2018. "Belief Adjustment: A Double Hurdle Model and Experimental Evidence," CAMA Working Papers 2018-01, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    • Handle: RePEc:een:camaaa:2018-01
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    File URL: https://crawford.anu.edu.au/sites/default/files/2025-01/1_2018_henckel_menzies_moffatt_zizzo.pdf
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    Cited by:

    1. Kawakami, Hajime, 2023. "Doob’s consistency of a non-Bayesian updating process," Statistics & Probability Letters, Elsevier, vol. 203(C).
    2. Anna Lou Abatayo & Tongzhe Li, 2024. "Public goods and bads with vulnerable individuals: How information and social nudges change behaviour," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 57(2), pages 556-587, May.
    3. Mel W Khaw & Luminita Stevens & Michael Woodford, 2021. "Individual differences in the perception of probability," PLOS Computational Biology, Public Library of Science, vol. 17(4), pages 1-25, April.
    4. Henckel, Timo & Menzies, Gordon D. & Moffatt, Peter & Zizzo, Daniel J., 2019. "Three dimensions of central bank credibility and inferential expectations: The Euro zone," Journal of Macroeconomics, Elsevier, vol. 60(C), pages 294-308.
    5. Timo Henckel & Gordon D. Menzies & Peter Moffat & Daniel J. Zizzo, 2019. "Three Dimensions of Central Bank Credibility and Inferential Expectations: The Euro Zone," Working Paper Series 56, Economics Discipline Group, UTS Business School, University of Technology, Sydney.
    6. Corazzini, Luca & Galavotti, Stefano & Valbonesi, Paola, 2019. "An experimental study on sequential auctions with privately known capacities," Games and Economic Behavior, Elsevier, vol. 117(C), pages 289-315.
    7. Bauer, Kevin & von Zahn, Moritz & Hinz, Oliver, 2022. "Expl(AI)ned: The impact of explainable Artificial Intelligence on cognitive processes," SAFE Working Paper Series 315, Leibniz Institute for Financial Research SAFE, revised 2022.
    8. Clement A. Tisdell, 2018. "Diversity In Economic Decision-Making And Behaviour: A New Brief Review," Advances in Decision Sciences, Asia University, Taiwan, vol. 22(1), pages 351-368, December.

    More about this item

    Keywords

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    JEL classification:

    • C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • D03 - Microeconomics - - General - - - Behavioral Microeconomics: Underlying Principles
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • E03 - Macroeconomics and Monetary Economics - - General - - - Behavioral Macroeconomics

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