TY - GEN
T1 - The price is (probably) right
T2 - 20th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2021
AU - Lev, Omer
AU - Patel, Neel
AU - Viswanathan, Vignesh
AU - Zick, Yair
N1 - Funding Information: Lev, Patel and Zick were supported by the Singapore NRF Research Fellowship #R-252-000-750-733. Patel and Zick were also supported by the AI Singapore Award #AISG-RP-2018-009. Viswanathan was supported by the IITKGP Foundation Award. Most of the work was done while all the authors were at the National University of Singapore. The authors would also like to thank the anonymous reviewers of AAAI 2020, AAMAS 2020 and AAMAS 2021 for their informative comments. Funding Information: Lev, Patel and Zick were supported by the Singapore NRF Research Fellowship #R-252-000-750-733. Patel and Zick were also supported by the AI Singapore Award #AISG-RP-2018-009. Viswanathan was Funding Information: supported by the IITKGP Foundation Award. Most of the work was done while all the authors were at the National University of Singapore. The authors would also like to thank the anonymous reviewers of AAAI 2020, AAMAS 2020 and AAMAS 2021 for their informative comments. Publisher Copyright: © 2021 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.
PY - 2021/1/1
Y1 - 2021/1/1
N2 - 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.
AB - 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.
KW - Fisher markets
KW - Market equilibria
KW - PAC learning
UR - http://www.scopus.com/inward/record.url?scp=85112342170&partnerID=8YFLogxK
M3 - Conference contribution
T3 - Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
SP - 755
EP - 763
BT - 20th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2021
Y2 - 3 May 2021 through 7 May 2021
ER -