desirable predictive guarantees [@ziebart2008]. These utility weights are then used to mimic the behavior in similar situations through a decision-making algorithm. Unlike the other two communities, it is the predictive performance of the learned model that is most pivotal and noisy observations are expected and managed by those techniques.

This article extends our prior publication—a novel maximum entropy approach for predicting behavior in strategic multi-agent domains [@waugh11; @waugh11arXiv]. We focus on the computationally and statistically efficient recovery of good estimates of behavior (the only observable quantity) by leveraging rationality assumptions. The work presented here further develops those ideas in two key ways. First, we consider distributions over games and parameterized families of deviations using the notion of conditional entropy. Second, this work enables more fine-grained assumptions regarding the players’ possible preferences. Finally, this work presents the analysis of data-sets from both the econometric and decision theory communities, comparing and contrasting the methods presented with statistical methods that are blind to the strategic aspects of the domains.

Before describing our approach, we will introduce the necessary notation and background.

Preliminaries
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Notation {#notation .unnumbered}
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Let ${V}$ be a Hilbert space with an inner product ${{\langle \cdot,\cdot \rangle} : {{V}}\times{V}\rightarrow {\mathbb 