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.

cognitive performance, lifetime, artificial intelligence, age–period–cohort decomposition, digitization,
Proceedings of the National Academy of Sciences of the United States of America
Department of Technology and Operations Management

Strittmatter, A., Sunde, U., & Zegners, D.K. (2020). Life Cycle Patterns of Cognitive Performance over the Long Run. Proceedings of the National Academy of Sciences of the United States of America. doi:10.1073/pnas.2006653117