  The advent of machine learning models that surpass human decision-making
ability in complex domains has initiated a movement towards building AI systems
that interact with humans. Many building blocks are essential for this
activity, with a central one being the algorithmic characterization of human
behavior. While much of the existing work focuses on aggregate human behavior,
an important long-range goal is to develop behavioral models that specialize to
individual people and can differentiate among them.
  To formalize this process, we study the problem of behavioral stylometry, in
which the task is to identify a decision-maker from their decisions alone. We
present a transformer-based approach to behavioral stylometry in the context of
chess, where one attempts to identify the player who played a set of games. Our
method operates in a few-shot classification framework, and can correctly
identify a player from among thousands of candidate players with 98% accuracy
given only 100 labeled games. Even when trained on amateur play, our method
generalises to out-of-distribution samples of Grandmaster players, despite the
dramatic differences between amateur and world-class players. Finally, we
consider more broadly what our resulting embeddings reveal about human style in
chess, as well as the potential ethical implications of powerful methods for
identifying individuals from behavioral data.
