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arXiv 2012.14838cs.GTcs.LG

The Price is (Probably) Right: Learning Market Equilibria from Samples

University of Massachusetts, Amherst(马萨诸塞大学阿默斯特分校);Ben-Gurion University of the Negev(内盖夫本-古里安大学);University of Southern California(南加利福尼亚大学)

Vignesh Viswanathan, Omer Lev, Neel Patel, Yair Zick

英文摘要:

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.

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