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Speed, Quality, and the Optimal Timing of Complex Decisions: Field Evidence

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  • Uwe Sunde
  • Dainis Zegners
  • Anthony Strittmatter

Abstract

This paper presents an empirical investigation of the relation between decision speed and decision quality for a real-world setting of cognitively-demanding decisions in which the timing of decisions is endogenous: professional chess. Move-by-move data provide exceptionally detailed and precise information about decision times and decision quality, based on a comparison of actual decisions to a computational benchmark of best moves constructed using the artificial intelligence of a chess engine. The results reveal that faster decisions are associated with better performance. The findings are consistent with the predictions of procedural decision models like drift-diffusion-models in which decision makers sequentially acquire information about decision alternatives with uncertain valuations.

Suggested Citation

  • Uwe Sunde & Dainis Zegners & Anthony Strittmatter, 2022. "Speed, Quality, and the Optimal Timing of Complex Decisions: Field Evidence," CESifo Working Paper Series 9546, CESifo.
  • Handle: RePEc:ces:ceswps:_9546
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    References listed on IDEAS

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    1. Larbi Alaoui & Antonio Penta, 2016. "Endogenous Depth of Reasoning," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 83(4), pages 1297-1333.
    2. Drew Fudenberg & Philipp Strack & Tomasz Strzalecki, 2018. "Speed, Accuracy, and the Optimal Timing of Choices," American Economic Review, American Economic Association, vol. 108(12), pages 3651-3684, December.
    3. Anthony Strittmatter & Uwe Sunde & Dainis Zegners, 2020. "Life cycle patterns of cognitive performance over the long run," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 117(44), pages 27255-27261, November.
    4. Ariel Rubinstein, 2007. "Instinctive and Cognitive Reasoning: A Study of Response Times," Economic Journal, Royal Economic Society, vol. 117(523), pages 1243-1259, October.
    5. Ian Krajbich & Björn Bartling & Todd Hare & Ernst Fehr, 2015. "Rethinking fast and slow based on a critique of reaction-time reverse inference," Nature Communications, Nature, vol. 6(1), pages 1-9, November.
    6. David Gill & Victoria Prowse, 2023. "Strategic Complexity and the Value of Thinking," The Economic Journal, Royal Economic Society, vol. 133(650), pages 761-786.
    7. Xavier Gabaix & David Laibson & Guillermo Moloche & Stephen Weinberg, 2006. "Costly Information Acquisition: Experimental Analysis of a Boundedly Rational Model," American Economic Review, American Economic Association, vol. 96(4), pages 1043-1068, September.
    8. Anja Achtziger & Carlos Alós-Ferrer, 2014. "Fast or Rational? A Response-Times Study of Bayesian Updating," Management Science, INFORMS, vol. 60(4), pages 923-938, April.
    9. Arad, Ayala & Rubinstein, Ariel, 2012. "Multi-dimensional iterative reasoning in action: The case of the Colonel Blotto game," Journal of Economic Behavior & Organization, Elsevier, vol. 84(2), pages 571-585.
    10. Christopher F. Chabris & David Laibson & Carrie L. Morris & Jonathon P. Schuldt & Dmitry Taubinsky, 2009. "The Allocation of Time in Decision-Making," Journal of the European Economic Association, MIT Press, vol. 7(2-3), pages 628-637, 04-05.
    11. Arkady Konovalov & Ian Krajbich, 2016. "Revealed Indifference: Using Response Times to Infer Preferences," Working Papers 16-01, Ohio State University, Department of Economics.
    12. Leonidas Spiliopoulos & Andreas Ortmann, 2018. "The BCD of response time analysis in experimental economics," Experimental Economics, Springer;Economic Science Association, vol. 21(2), pages 383-433, June.
    13. Drew Fudenberg & Whitney Newey & Philipp Strack & Tomasz Strzalecki, 2020. "Testing the drift-diffusion model," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 117(52), pages 33141-33148, December.
    14. Andrew Schotter & Isabel Trevino, 2021. "Is response time predictive of choice? An experimental study of threshold strategies," Experimental Economics, Springer;Economic Science Association, vol. 24(1), pages 87-117, March.
    15. Carlos Alós-Ferrer & Johannes Buckenmaier, 2021. "Cognitive sophistication and deliberation times," Experimental Economics, Springer;Economic Science Association, vol. 24(2), pages 558-592, June.
    16. Marco Sahm & Robert K. Weizsäcker, 2016. "Reason, Intuition, and Time," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 37(3), pages 195-207, April.
    17. Daniel Kahneman, 2003. "Maps of Bounded Rationality: Psychology for Behavioral Economics," American Economic Review, American Economic Association, vol. 93(5), pages 1449-1475, December.
    18. Ariel Rubinstein, 2007. "Instinctive and Cognitive Reasoning: Response Times Study," Levine's Bibliography 321307000000001011, UCLA Department of Economics.
    19. Ryan Webb, 2019. "The (Neural) Dynamics of Stochastic Choice," Management Science, INFORMS, vol. 65(1), pages 230-255, January.
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    2. Johannes Carow & Niklas M. Witzig, 2024. "Time Pressure and Strategic Risk-Taking in Professional Chess," Working Papers 2404, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.

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    More about this item

    Keywords

    response times; speed-performance profile; drift-diffusion model; uncertain evaluations;
    All these keywords.

    JEL classification:

    • D01 - Microeconomics - - General - - - Microeconomic Behavior: Underlying Principles
    • D90 - Microeconomics - - Micro-Based Behavioral Economics - - - General
    • C70 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - General
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General

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