A Service of
Leibniz-Informationszentrum econstor Wirtschaft Leibniz Information Centre Make Your Publications Visible. zbw for Economics
Zegners, Dainis; Sunde, Uwe; Strittmatter, Anthony
Working Paper Decisions and performance under bounded rationality: A computational benchmarking approach
Discussion Paper, No. 263
Provided in Cooperation with: University of Munich (LMU) and Humboldt University Berlin, Collaborative Research Center Transregio 190: Rationality and Competition
Suggested Citation: Zegners, Dainis; Sunde, Uwe; Strittmatter, Anthony (2020) : Decisions and performance under bounded rationality: A computational benchmarking approach, Discussion Paper, No. 263, Ludwig-Maximilians-Universität München und Humboldt-Universität zu Berlin, Collaborative Research Center Transregio 190 - Rationality and Competition, München und Berlin
This Version is available at: http://hdl.handle.net/10419/233487
Standard-Nutzungsbedingungen: Terms of use:
Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Documents in EconStor may be saved and copied for your Zwecken und zum Privatgebrauch gespeichert und kopiert werden. personal and scholarly purposes.
Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle You are not to copy documents for public or commercial Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich purposes, to exhibit the documents publicly, to make them machen, vertreiben oder anderweitig nutzen. publicly available on the internet, or to distribute or otherwise use the documents in public. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, If the documents have been made available under an Open gelten abweichend von diesen Nutzungsbedingungen die in der dort Content Licence (especially Creative Commons Licences), you genannten Lizenz gewährten Nutzungsrechte. may exercise further usage rights as specified in the indicated licence. www.econstor.eu Decisions and Performance Under Bounded Rationality: A Computational Benchmarking Approach
Dainis Zegners (Erasmus University Rotterdam) Uwe Sunde (LMU Munich) Anthony Strittmatter (CREST-ENSAE)
Discussion Paper No. 263
December 22, 2020
Collaborative Research Center Transregio 190 | www.rationality-and-competition.de Ludwig-Maximilians-Universität München | Humboldt-Universität zu Berlin Spokesperson: Prof. Dr. Klaus M. Schmidt, University of Munich, 80539 Munich, Germany +49 (89) 2180 3405 | [email protected] Decisions and Performance Under Bounded Rationality: A Computational Benchmarking Approach
Dainis Zegners Uwe Sunde Anthony Strittmatter Rotterdam LMU Munich CREST-ENSAE School of Management, CEPR, Ifo, IZA CESifo Erasmus University
This paper presents a novel approach to analyze human decision-making that involves comparing the behavior of professional chess players relative to a computational benchmark of cognitively bounded rationality. This bench- mark is constructed using algorithms of modern chess engines and allows investigating behavior at the level of individual move-by-move observations, thus representing a natural benchmark for computationally bounded opti- mization. The analysis delivers novel insights by isolating deviations from this benchmark of bounded rationality as well as their causes and conse- quences for performance. The findings document the existence of several distinct dimensions of behavioral deviations, which are related to asymmet- ric positional evaluation in terms of losses and gains, time pressure, fatigue, and complexity. The results also document that deviations from the bench- mark do not necessarily entail worse performance. Faster decisions are as- sociated with more frequent deviations from the benchmark, yet they are also associated with better performance. The findings are consistent with an important influence of intuition and experience, thereby shedding new light on the recent debate about computational rationality in cognitive processes. JEL-classification: D01, D9, C7, C8 Keywords: Cognitively Bounded Rationality, Benchmark Computing, Arti- ficial Intelligence, Decision Quality, Decision Time