  We study a multi-agent decision problem in population games, where agents
select from multiple available strategies and continually revise their
selections based on the payoffs associated with these strategies. Unlike
conventional population game formulations, we consider a scenario where agents
must estimate the payoffs through local measurements and communication with
their neighbors. By employing task allocation games -- dynamic extensions of
conventional population games -- we examine how errors in payoff estimation by
individual agents affect the convergence of the strategy revision process. Our
main contribution is an analysis of how estimation errors impact the
convergence of the agents' strategy profile to equilibrium. Based on the
analytical results, we propose a design for a time-varying strategy revision
rate to guarantee convergence. Simulation studies illustrate how the proposed
method for updating the revision rate facilitates convergence to equilibrium.
