  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.
