  Many high-stakes decision-making problems, such as those found within
cybersecurity and economics, can be modeled as competitive resource allocation
games. In these games, multiple players must allocate limited resources to
overcome their opponent(s), while minimizing any induced individual losses.
However, existing means of assessing the performance of resource allocation
algorithms are highly disparate and problem-dependent. As a result, evaluating
such algorithms is unreliable or impossible in many contexts and applications,
especially when considering differing levels of feedback. To resolve this
problem, we propose a generalized definition of payoff which uses an arbitrary
user-provided function. This unifies performance evaluation under all contexts
and levels of feedback. Using this definition, we develop metrics for
evaluating player performance, and estimators to approximate them under
uncertainty (i.e., bandit or semi-bandit feedback). These metrics and their
respective estimators provide a problem-agnostic means to contextualize and
evaluate algorithm performance. To validate the accuracy of our estimator, we
explore the Colonel Blotto ($\mathcal{CB}$) game as an example. To this end, we
propose a graph-pruning approach to efficiently identify feasible opponent
decisions, which are used in computing our estimation metrics. Using various
resource allocation algorithms and game parameters, a suite of $\mathcal{CB}$
games are simulated and used to compute and evaluate the quality of our
estimates. These simulations empirically show our approach to be highly
accurate at estimating the metrics associated with the unseen outcomes of an
opponent's latent behavior.
