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Strategic Complexity and the Value of Thinking

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  • Gill, David

    (Purdue University)

  • Prowse, Victoria L.

    (Purdue University)

Abstract

Response times are a simple low-cost indicator of the process of reasoning in strategic games. In this paper, we leverage the dynamic nature of response-time data from repeated strategic interactions to measure the strategic complexity of a situation by how long people think on average when they face that situation (where we categorize situations according to the characteristics of play in the previous round). We find that strategic complexity varies significantly across situations, and we find considerable heterogeneity in how responsive subjects' thinking times are to complexity. We also study how variation in response times at the individual level across rounds affects strategic behavior and success: when a subject thinks for longer than she would normally do in a particular situation, she wins less frequently and earns less. The behavioral mechanism that drives the reduction in performance is a tendency to move away from Nash equilibrium behavior. Finally, cognitive ability and personality have no effect on average response times.

Suggested Citation

  • Gill, David & Prowse, Victoria L., 2022. "Strategic Complexity and the Value of Thinking," IZA Discussion Papers 15275, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp15275
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    Cited by:

    1. Uwe Sunde & Dainis Zegners & Anthony Strittmatter, 2022. "Speed, Quality, and the Optimal Timing of Complex Decisions: Field Evidence," CESifo Working Paper Series 9546, CESifo.
    2. Duarte Gonc{c}alves, 2024. "Speed, Accuracy, and Complexity," Papers 2403.11240, arXiv.org, revised Jun 2024.
    3. Sebastian J. Goerg & Sebastian Kube & Jonas Radbruch, 2019. "The Effectiveness of Incentive Schemes in the Presence of Implicit Effort Costs," Management Science, INFORMS, vol. 65(9), pages 4063-4078, September.
    4. Carlos Alós-Ferrer & Johannes Buckenmaier, 2021. "Cognitive sophistication and deliberation times," Experimental Economics, Springer;Economic Science Association, vol. 24(2), pages 558-592, June.
    5. Larbi Alaoui & Katharina A. Janezic & Antonio Penta, 2022. "Coordination and sophistication," Economics Working Papers 1849, Department of Economics and Business, Universitat Pompeu Fabra.
    6. Grabiszewski, Konrad & Horenstein, Alex, 2020. "Effort is not a monotonic function of skills: Results from a global mobile experiment," Journal of Economic Behavior & Organization, Elsevier, vol. 176(C), pages 634-652.
    7. Alaoui, Larbi & Janezic, Katharina A. & Penta, Antonio, 2020. "Reasoning about others' reasoning," Journal of Economic Theory, Elsevier, vol. 189(C).
    8. Alaoui, Larbi & Janezic, Katharina A. & Penta, Antonio, 2022. "Coordination and Sophistication," TSE Working Papers 22-1394, Toulouse School of Economics (TSE).
    9. Castagnetti, Alessandro & Proto, Eugenio & Sofianos, Andis, 2023. "Anger impairs strategic behavior: A Beauty-Contest based analysis," Journal of Economic Behavior & Organization, Elsevier, vol. 213(C), pages 128-141.
    10. Cary Frydman & Ian Krajbich, 2022. "Using Response Times to Infer Others’ Private Information: An Application to Information Cascades," Management Science, INFORMS, vol. 68(4), pages 2970-2986, April.
    11. Grabiszewski, Konrad & Horenstein, Alex, 2022. "Measuring tree complexity with response times," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 98(C).
    12. Larbi Alaoui & Katharina A. Janezic & Antonio Penta, 2022. "Coordination and Sophistication," Working Papers 1372, Barcelona School of Economics.

    More about this item

    Keywords

    response time; decision time; deliberation time; thinking time; complexity; level-k; game theory; strategic game; repeated games; beauty contest; cognitive ability; personality;
    All these keywords.

    JEL classification:

    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior

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