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Decision analysis deals with modeling and enhancing decision processes. Aprincipal challenge in improving behavior is in obtaining a transparentdescription of existing behavior in the first place. In this paper, we developan expressive, unifying perspective on inverse decision modeling: a frameworkfor learning parameterized representations of sequential decision behavior.First, we formalize the forward problem (as a normative standard), subsumingcommon classes of control behavior. Second, we use this to formalize theinverse problem (as a descriptive model), generalizing existing work onimitation/reward learning -- while opening up a much broader class of researchproblems in behavior representation. Finally, we instantiate this approach withan example (inverse bounded rational control), illustrating how this structureenables learning (interpretable) representations of (bounded) rationality --while naturally capturing intuitive notions of suboptimal actions, biasedbeliefs, and imperfect knowledge of environments.

Inverse Decision Modeling: Learning Interpretable Representations of Behavior