Sigman Mariano, Etchemendy Pablo, Slezak Diego Fernández, Cecchi Guillermo A
Physics Department, School of Sciences, University of Buenos Aires Buenos Aires, Argentina.
Front Neurosci. 2010 Oct 7;4:60. doi: 10.3389/fnins.2010.00060. eCollection 2010.
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.
快棋提供了一个无与伦比的实验室，用于理解自然环境中的决策过程。在一场国际象棋比赛中，玩家要在有限的时间内连续做出大约40步决策。由于当前的国际象棋算法能够精确估计局面的价值，因此每个决策的优劣都可以进行量化确定。基于网络的国际象棋每天会产生大量数据，数以百万计的决策，这是传统心理实验所无法比拟的。我们生成了一个快棋比赛中反应时间（RTs）和局面价值的数据库。我们测量了稳健的涌现统计可观测量：（1）反应时间分布呈长尾状，并且在比赛的不同阶段呈现出质的不同形式，（2）连续走法的反应时间对于玩家内部和玩家之间的走法都高度相关。这些发现具有理论意义，因为它们否定了顺序决策算法的两个基本假设：反应时间不是固定不变的，也不能由状态函数生成。我们的结果也具有实际意义。首先，我们刻画了失误和得分波动预测玩家实力的能力，这在国际象棋软件中仍然是一个未解决的问题。其次，我们表明获胜可能性可以通过剩余时间和局面评估的加权组合可靠地估计出来。
