Memory Kernel in the Expertise of Chess Players

摘要

In this work we investigate a mechanism for the emergence of long-range time correlations observed in a chronologically ordered database of chess games. We analyze a modified Yule-Simon preferential growth process proposed by Cattuto et al., which includes memory effects by means of a probabilistic kernel. According to the Hurst exponent of different constructed time series from the record of games, artificially generated databases from the model exhibit similar long-range correlations. In addition, the inter-event time frequency distribution is well reproduced by the model for realistic parameter values. In particular, we find the inter-event time distribution properties to be correlated with the expertise of the chess players through the memory kernel extension. Our work provides new information about the strategies implemented by players with different levels of expertise, showing an interesting example of how popularities and long-range correlations build together during a collective learning process.