Machine Learning
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Eye-tracking was used to predict choices made during play of a series of computer-generated simultaneous normal-form games. Four normal-form games were used as the test bed for the eye-tracking experiment: the Coordination Game, Battle of the Sexes, the Game of Chicken, and Prisoner's Dilemma. These games are abstractions of real-life scenarios where a person must make a choice to either cooperate with another person for some common good, or not cooperate, given a specific "payoff" for cooperating or not cooperating. The other player was always an automated agent whose goal was to predict the choice of the human player. Players were found to cluster into different types according to a numeric index specific to the game played. An eye-tracking experiment confirms that attention deployed to particular areas of interest varies according to the game played and the type to which a player belongs. This enabled a decision tree to be created from the eye-tracking data which was used by the agent to classify each player as a specific type, allowing a prediction to be made about a player's likely choice.