@ARTICLE{10.3389/fnins.2010.00060, AUTHOR={Sigman, Mariano and Etchemendy, Pablo and Fernandez Slezak, Diego and Cecchi, Guillermo}, TITLE={Response Time Distributions in Rapid Chess: A Large-Scale Decision Making Experiment}, JOURNAL={Frontiers in Neuroscience}, VOLUME={4}, YEAR={2010}, URL={https://www.frontiersin.org/articles/10.3389/fnins.2010.00060}, DOI={10.3389/fnins.2010.00060}, ISSN={1662-453X}, ABSTRACT={Rapid chess provides an unparalleled laboratory to understand decision making in a natural environment. In a chess game, players choose consecutively around 40 moves in a finite time budget. The goodness of each choice can be determined quantitatively since current chess algorithms estimate precisely the value of a position. Web-based chess produces vast amounts of data, millions of decisions per day, incommensurable with traditional psychological experiments. We generated a database of response times (RTs) and position value in rapid chess games. We measured robust emergent statistical observables: (1) RT distributions are long-tailed and show qualitatively distinct forms at different stages of the game, (2) RT of successive moves are highly correlated both for intra- and inter-player moves. These findings have theoretical implications since they deny two basic assumptions of sequential decision making algorithms: RTs are not stationary and can not be generated by a state-function. Our results also have practical implications. First, we characterized the capacity of blunders and score fluctuations to predict a player strength, which is yet an open problem in chess softwares. Second, we show that the winning likelihood can be reliably estimated from a weighted combination of remaining times and position evaluation.} }