Rationalization: The Inverse Equilibrium Problem'
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Introduction
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Predicting the actions of others in complex and strategic settings is an important facet of intelligence that guides our interactions—from walking in crowds to negotiating multi-party deals. Recovering such behavior from merely a few observations is an important and challenging machine learning task.

While mature computational frameworks for decision-making have been developed to [**prescribe**]{} the behavior that an agent [*should*]{} perform, such frameworks are often ill-suited for [**predicting**]{} the behavior that an agent [*will*]{} perform. Foremost, the standard assumption of decision-making frameworks that a criteria for preferring actions ([*e.g.*]{}, costs, motivations and goals) is known [*a priori*]{} often does not hold. Moreover, real behavior is typically not consistently optimal or completely rational; it may be influenced by factors that are difficult to model or subject to various types of error when executed. Meanwhile, the standard tools of statistical machine learning ([*e.g.*]{}, classification and regression) may be equally poorly matched to modeling purposeful behavior; an agent’s goals often succinctly, but implicitly, encode a strategy that would require a tremendous number of observations to learn.

A natural approach to mitigate the complexity of recovering a full strategy for an agent is to consider identifying 