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The advent of machine learning models that surpass human decision-makingability in complex domains has initiated a movement towards building AI systemsthat interact with humans. Many building blocks are essential for thisactivity, with a central one being the algorithmic characterization of humanbehavior. While much of the existing work focuses on aggregate human behavior,an important long-range goal is to develop behavioral models that specialize toindividual people and can differentiate among them.  To formalize this process, we study the problem of behavioral stylometry, inwhich the task is to identify a decision-maker from their decisions alone. Wepresent a transformer-based approach to behavioral stylometry in the context ofchess, where one attempts to identify the player who played a set of games. Ourmethod operates in a few-shot classification framework, and can correctlyidentify a player from among thousands of candidate players with 98% accuracygiven only 100 labeled games. Even when trained on amateur play, our methodgeneralises to out-of-distribution samples of Grandmaster players, despite thedramatic differences between amateur and world-class players. Finally, weconsider more broadly what our resulting embeddings reveal about human style inchess, as well as the potential ethical implications of powerful methods foridentifying individuals from behavioral data.

Detecting Individual Decision-Making Style: Exploring Behavioral Stylometry in Chess