Response time distributions in rapid chess: a large-scale decision making experiment

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.



  • “I have always defended playing under time pressure, and I do not think a shortage of time is a bad thing. On the contrary, I have always thought that fast playing is a measure of the ability to play chess” (Bronstein and Fürstenberg, 1995).

  • The reduction of chess expertise to speed is, however, overly simplistic: firstly, there is substantial evidence that chess experts do not search “wider”, they do search “deeper” than weaker players (Holding and Reynolds, 1982; Saariluoma, 1990); secondly, as players are forced to play faster, their ability during regular play under normal time controls becomes less predictive of their performance (Van Der Maas and Wagenmakers, 2005). Not surprisingly, even grandmasters make more errors and blunders under conditions in which they have less time than usual to select their moves (Chabris and Hearst, 2003). Thus, time pressure provokes a selective enhancement of rapid object recognition, favoring the best players, but also increases the likelihood of errors and blunders, which in turn tends to equalize the game. Time constraints and playing ability therefore interact in a highly non-trivial manner.

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