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Life cycle patterns of cognitive performance over the long run

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  • Strittmatter, Anthony
  • Sunde, Uwe
  • Zegners, Dainis

Abstract

Little is known about how the age pattern in individual performance in cognitively demanding tasks changed over the past century. The main difficulty for measuring such life cycle performance patterns and their dynamics over time is related to the construction of a reliable measure that is comparable across individuals and over time and not affected by changes in technology or other environmental factors. This study presents evidence for the dynamics of life cycle patterns of cognitive performance over the past 125 y based on an analysis of data from professional chess tournaments. Individual move-by-move performance in more than 24,000 games is evaluated relative to an objective benchmark that is based on the respective optimal move suggested by a chess engine. This provides a precise and comparable measurement of individual performance for the same individual at different ages over long periods of time, exploiting the advantage of a strictly comparable task and a comparison with an identical performance benchmark. Repeated observations for the same individuals allow disentangling age patterns from idiosyncratic variation and analyzing how age patterns change over time and across birth cohorts. The findings document a hump-shaped performance profile over the life cycle and a long-run shift in the profile toward younger ages that is associated with cohort effects rather than period effects. This shift can be rationalized by greater experience, which is potentially a consequence of changes in education and training facilities related to digitization.

Suggested Citation

  • Strittmatter, Anthony & Sunde, Uwe & Zegners, Dainis, 2020. "Life cycle patterns of cognitive performance over the long run," Munich Reprints in Economics 84764, University of Munich, Department of Economics.
    • Handle: RePEc:lmu:muenar:84764
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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. Dainis Zegners & Uwe Sunde & Anthony Strittmatter, 2020. "Decisions and Performance Under Bounded Rationality: A Computational Benchmarking Approach," CESifo Working Paper Series 8341, CESifo.
    3. Vélez-Coto, María & Rute-Pérez, Sandra & Pérez-García, Miguel & Caracuel, Alfonso, 2021. "Unemployment and general cognitive ability: A review and meta-analysis," Journal of Economic Psychology, Elsevier, vol. 87(C).
    4. Künn, Steffen & Seel, Christian & Zegners, Dainis, 2020. "Cognitive Performance in the Home Office - Evidence from Professional Chess," Research Memorandum 021, Maastricht University, Graduate School of Business and Economics (GSBE).
    5. Mehmet S. Ismail, 2023. "Human and Machine Intelligence in n-Person Games with Partial Knowledge: Theory and Computation," Papers 2302.13937, arXiv.org, revised Feb 2024.
    6. Minkyu Shin & Jin Kim & Minkyung Kim, 2020. "Measuring Human Adaptation to AI in Decision Making: Application to Evaluate Changes after AlphaGo," Papers 2012.15035, arXiv.org, revised Jan 2021.
    7. Jose De Sousa, "undated". "Peer competition: Evidence from 5- to 95-year-olds," French Stata Users' Group Meetings 2022 03, Stata Users Group.
    8. Farbmacher, Helmut & Kögel, Heinrich & Spindler, Martin, 2021. "Heterogeneous effects of poverty on attention," Labour Economics, Elsevier, vol. 71(C).

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