Innovation, Preferential Growth and Memory in Chess Playing Behavior

Complexity develops via the incorporation of innovative properties.
Chess is one of the most complex strategy games where expert
contenders exercise decision making by imitating old games or
introducing innovative moves. We found that chess players tend to
innovate with a probability decaying as a power law of the popularity
of the last played move. By popularity we mean how frequently chess
players play that move. Chess players also exploit already known move
sequences following a preferential growth mechanism, ie. by choosing
with larger probabilities the more popular moves. By using these
observations, we build a model able to reproduce the main statistical
properties of the already explored chess game tree. Namely, that it
grows by following a sequence of Heaps' laws and that it produce a
distribution of moves popularities consistent with a Zipf's law.
Finally, we also show that the growth of the already explored game
tree exhibits long range time correlations, a property typically found
in complex systems. These results are important in the context of
decision making in a competitive scenario helping to understand how
the exploitation of know resources is balanced with the exploration of
new possibilities.

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Short bio:
I am a Physicist from Argentina interested in the study of complex
systems by using -mainly- tools borrowed from statistical physics. I
obtained my degree and doctorate in Physics at the Universidad
Nacional de Cordoba (UNC), Argentina. Then, I made my first Postdoc
there at the UNC. Now, I am currently working as a Postdoc at the
Department of Biomedical Engineering and Computational Science (BECS),
Aalto University, in the Complex Networks Research group.