  We consider the classic principal-agent model of contract theory, in which a
principal designs an outcome-dependent compensation scheme to incentivize an
agent to take a costly and unobservable action. When all of the model
parameters---including the full distribution over principal rewards resulting
from each agent action---are known to the designer, an optimal contract can in
principle be computed by linear programming. In addition to their demanding
informational requirements, such optimal contracts are often complex and
unintuitive, and do not resemble contracts used in practice.
  This paper examines contract theory through the theoretical computer science
lens, with the goal of developing novel theory to explain and justify the
prevalence of relatively simple contracts, such as linear (pure commission)
contracts. First, we consider the case where the principal knows only the first
moment of each action's reward distribution, and we prove that linear contracts
are guaranteed to be worst-case optimal, ranging over all reward distributions
consistent with the given moments. Second, we study linear contracts from a
worst-case approximation perspective, and prove several tight parameterized
approximation bounds.
