2022/01/26 by Uwe Sunde, Dainis Zēgners, Sunde, Uwe +4 · 1 voice · 2 citations
Computer Science · Decision Sciences · Economics, Econometrics and Finance · Mathematics · #Algorithm #Applications (stat.AP) #Artificial Intelligence (cs.AI) #Artificial intelligence #Benchmark (surveying) #Business decision mapping #Computer science #Data mining #Decision analysis #Decision field theory #Decision model #Decision problem #Decision quality #Decision support system #Decision tree #Decision-Making and Behavioral Economics #Empirical evidence #Evidential reasoning approach #FOS: Computer and information sciences #FOS: Economics and business #Field (mathematics) #General Economics (econ.GN) #Knowledge management #Machine learning #Mathematics #Optimal decision #Quality (philosophy) #Relation (database) #Statistics #cs.AI #econ.GN #q-fin.EC #stat.AP
paper · pdf · doi:10.48550/arxiv.2201.10808
arxiv created 2022/01/26 · openalex publication_date 2022/01/26 · arxiv published 2022/01/26 · arxiv updated 2022/01/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.