A task is randomly drawn from a finite set of tasks and is described using a fixed number of bits. All the tasks that share its description must be performed. Upper and lower bounds on the minimum ρth moment of the number of performed tasks are derived. The case where a sequence of tasks is produced by a source and n tasks are jointly described using nR bits is considered. If R is larger than the Rényi entropy rate of the source of order 1/(1 + ρ) (provided it exists), then the ρth moment of the ratio of performed tasks to n can be driven to one as n tends to infinity. If R is smaller than the Rényi entropy rate, this moment tends to infinity. The results are generalized to account for the presence of side-information. In this more general setting, the key quantity is a conditional version of Rényi entropy that was introduced by Arimoto. For IID sources, two additional extensions are solved, one of a rate-distortion flavor and the other where different tasks may have different nonnegative costs. Finally, a divergence that was identified by Sundaresan as a mismatch penalty in the Massey-Arikan guessing problem is shown to play a similar role here.