  Empirical game-theoretic analysis (EGTA) is a general framework for reasoning
about complex games using agent-based simulation. Data from simulating select
strategy profiles is employed to estimate a cogent and tractable game model
approximating the underlying game. To date, EGTA methodology has focused on
game models in normal form; though the simulations play out in sequential
observations and decisions over time, the game model abstracts away this
temporal structure. Richer models of \textit{extensive-form games} (EFGs)
provide a means to capture temporal patterns in action and information, using
tree representations. We propose \textit{tree-exploiting EGTA} (TE-EGTA), an
approach to incorporate EFG models into<EMAIL_ADDRESS>TE-EGTA constructs game models
that express observations and temporal organization of activity, albeit at a
coarser grain than the underlying agent-based simulation model. The idea is to
exploit key structure while maintaining tractability. We establish
theoretically and experimentally that exploiting even a little temporal
structure can vastly reduce estimation error in strategy-profile payoffs
compared to the normal-form model. Further, we explore the implications of EFG
models for iterative approaches to EGTA, where strategy spaces are extended
incrementally. Our experiments on several game instances demonstrate that
TE-EGTA can also improve performance in the iterative setting, as measured by
the quality of equilibrium approximation as the strategy spaces are expanded.
