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Guessing and gambling

Author

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  • Albert Burgos

    (Universidad de Murcia)

Abstract

Scoring methods in multiple-choice tests are usually designed as fair bets, and thus random guesswork yields zero expected return. This causes the undesired result of forcing risk averse test-takers to pay a premium in the sense of letting unmarked answers for which they have partial but not full knowledge. In this note I use a calibrated model of prospect theory [Tversky and Kahneman (1992, 1995))] to compute a fair rule which is also strategically neutral, (i.e. under partial knowledge answering is beneficial for the representative calibrated agent, while under total uncertainty it is not). This rule is remarkably close to an old rule presented in 1969 by Traub et al. in which there is no penalty for wrong answers but omitted answers are rewarded by 1/M if M is the number of possible answers.

Suggested Citation

  • Albert Burgos, 2004. "Guessing and gambling," Economics Bulletin, AccessEcon, vol. 4(4), pages 1-10.
    • Handle: RePEc:ebl:ecbull:eb-04d80001
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    References listed on IDEAS

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    1. Bradley, Ian, 2003. "The representative bettor, bet size, and prospect theory," Economics Letters, Elsevier, vol. 78(3), pages 409-413, March.
    2. Wakker, Peter P, 2001. "Testing and Characterizing Properties of Nonadditive Measures through Violations of the Sure-Thing Principle," Econometrica, Econometric Society, vol. 69(4), pages 1039-1059, July.
    3. Shlomo Benartzi & Richard H. Thaler, 1995. "Myopic Loss Aversion and the Equity Premium Puzzle," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 110(1), pages 73-92.
    4. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    5. Tversky, Amos & Kahneman, Daniel, 1992. "Advances in Prospect Theory: Cumulative Representation of Uncertainty," Journal of Risk and Uncertainty, Springer, vol. 5(4), pages 297-323, October.
    6. George Wu & Richard Gonzalez, 1999. "Nonlinear Decision Weights in Choice Under Uncertainty," Management Science, INFORMS, vol. 45(1), pages 74-85, January.
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    Citations

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    Cited by:

    1. Marín, Carmen & Rosa-García, Alfonso, 2011. "Gender bias in risk aversion: evidence from multiple choice exams," MPRA Paper 39987, University Library of Munich, Germany.
    2. Pelin Akyol & James Key & Kala Krishna, 2022. "Hit or Miss? Test Taking Behavior in Multiple Choice Exams," Annals of Economics and Statistics, GENES, issue 147, pages 3-50.
    3. MARÍN MARTÍNEZ, Carmen & ROSA GARCÍA, Alfonso, 2014. "Is Gender Bias A Cost Of Failure Issue?," Regional and Sectoral Economic Studies, Euro-American Association of Economic Development, vol. 14(3), pages 19-30.
    4. Pekkarinen, Tuomas, 2015. "Gender differences in behaviour under competitive pressure: Evidence on omission patterns in university entrance examinations," Journal of Economic Behavior & Organization, Elsevier, vol. 115(C), pages 94-110.
    5. Pekkarinen, Tuomas, 2014. "Gender Differences in Strategic Behaviour under Competitive Pressure: Evidence on Omission Patterns in University Entrance Examinations," IZA Discussion Papers 8018, Institute of Labor Economics (IZA).
    6. Hubert Janos Kiss & Adrienn Selei, 2013. "Gambler's fallacy in the classroom?," CERS-IE WORKING PAPERS 1342, Institute of Economics, Centre for Economic and Regional Studies.

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    More about this item

    JEL classification:

    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty
    • A2 - General Economics and Teaching - - Economic Education and Teaching of Economics

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