observation, the algorithm favors repeating previous actions [@erev2010]. Memory limitations, or more generally bounded rationality, have also led to novel equilibrium concepts such as the quantal response equilibrium [@mckelvey1995]. This concept assumes the players’ strategies have faults, but that small errors, in terms of forgone utility, are much more common than large errors. Contrasting with the econometricians, the decision theory community is mainly interested in the algorithmic process of human decision-making. The players’ preferences are known and observed behavior serves only to validate or inform an experimental hypothesis.

Finally, the machine learning community is interested in predicting and imitating the behavior of humans and expert systems. Much work in this area focuses on the single-agent setting and in such cases it is known as *inverse optimal control* or *inverse reinforcement learning* [@abbeel2004; @ng2000algorithms]. Here, the observed behavior is assumed to be an approximately optimal solution to an unknown decision problem. At a high level, known solutions typically summarize the behavior as parameters to a low dimensional utility function. A number of methods have been introduced to learn these weights, including margin-based methods [@ratliff2006] that can utilize black box optimal control or planning software, as well as maximum entropy-based methods with 