  Equilibrium computation in markets usually considers settings where player
valuation functions are known. We consider the setting where player valuations
are unknown; using a PAC learning-theoretic framework, we analyze some classes
of common valuation functions, and provide algorithms which output direct PAC
equilibrium allocations, not estimates based on attempting to learn valuation
functions. Since there exist trivial PAC market outcomes with an unbounded
worst-case efficiency loss, we lower-bound the efficiency of our algorithms.
While the efficiency loss under general distributions is rather high, we show
that in some cases (e.g., unit-demand valuations), it is possible to find a PAC
market equilibrium with significantly better utility.
