  We provide a unifying framework for the design and analysis of
multicalibrated predictors. By placing the multicalibration problem in the
general setting of multi-objective learning -- where learning guarantees must
hold simultaneously over a set of distributions and loss functions -- we
exploit connections to game dynamics to achieve state-of-the-art guarantees for
a diverse set of multicalibration learning problems. In addition to shedding
light on existing multicalibration guarantees and greatly simplifying their
analysis, our approach also yields improved guarantees, such as obtaining
stronger multicalibration conditions that scale with the square-root of group
size and improving the complexity of $k$-class multicalibration by an
exponential factor of $k$. Beyond multicalibration, we use these game dynamics
to address emerging considerations in the study of group fairness and
multi-distribution learning.
