  Progress in machine learning is measured by careful evaluation on problems of
outstanding common interest. However, the proliferation of benchmark suites and
environments, adversarial attacks, and other complications has diluted the
basic evaluation model by overwhelming researchers with choices. Deliberate or
accidental cherry picking is increasingly likely, and designing well-balanced
evaluation suites requires increasing effort. In this paper we take a step back
and propose Nash averaging. The approach builds on a detailed analysis of the
algebraic structure of evaluation in two basic scenarios: agent-vs-agent and
agent-vs-task. The key strength of Nash averaging is that it automatically
adapts to redundancies in evaluation data, so that results are not biased by
the incorporation of easy tasks or weak agents. Nash averaging thus encourages
maximally inclusive evaluation -- since there is no harm (computational cost
aside) from including all available tasks and agents.
