Ascending-price algorithms for unknown markets
We design a simple ascending-price algorithm to compute a (1 + ϵ )-approximate equilibrium in Arrow- Debreu markets with weak gross substitute property. It applies to an unknown market setting without exact knowledge about the number of agents, their individual utilities, and endowments. Instead, ou...
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sg-ntu-dr.10356-1503002023-02-28T19:25:51Z Ascending-price algorithms for unknown markets Bei, Xiaohui Garg, Jugal Hoefer, Martin School of Physical and Mathematical Sciences Science::Mathematics Market Equilibrium Equilibrium Computation We design a simple ascending-price algorithm to compute a (1 + ϵ )-approximate equilibrium in Arrow- Debreu markets with weak gross substitute property. It applies to an unknown market setting without exact knowledge about the number of agents, their individual utilities, and endowments. Instead, our algorithm only uses price queries to a global demand oracle. This is the first polynomial-time algorithm for most of the known tractable classes of Arrow-Debreu markets, which computes such an equilibrium with a number of calls to the demand oracle that is polynomial in log 1/ϵ and avoids heavy machinery such as the ellipsoid method. Demands can be real-valued functions of prices, but the oracles only return demand values of bounded precision. Due to this more realistic assumption, precision and representation of prices and demands become a major technical challenge, and we develop new tools and insights that may be of independent interest. Furthermore, we give the first polynomial-time algorithm to compute an exact equilibrium for markets with spending constraint utilities. This resolves an open problem posed by Duan and Mehlhorn. Accepted version 2021-05-20T06:48:37Z 2021-05-20T06:48:37Z 2019 Journal Article Bei, X., Garg, J. & Hoefer, M. (2019). Ascending-price algorithms for unknown markets. ACM Transactions On Algorithms, 15(3), 37-. https://dx.doi.org/10.1145/3319394 1549-6325 https://hdl.handle.net/10356/150300 10.1145/3319394 2-s2.0-85067230590 3 15 37 en ACM Transactions on Algorithms © 2019 The Owner/Author(s). All rights reserved. This paper was published by Association for Computing Machinery in ACM Transactions on Algorithms and is made available with permission of The Owner/Author(s). application/pdf |
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Science::Mathematics Market Equilibrium Equilibrium Computation Bei, Xiaohui Garg, Jugal Hoefer, Martin Ascending-price algorithms for unknown markets |
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We design a simple ascending-price algorithm to compute a (1 + ϵ )-approximate equilibrium in Arrow- Debreu markets with weak gross substitute property. It applies to an unknown market setting without exact knowledge about the number of agents, their individual utilities, and endowments. Instead, our algorithm only uses price queries to a global demand oracle. This is the first polynomial-time algorithm for most of the known tractable classes of Arrow-Debreu markets, which computes such an equilibrium with a number of calls to the demand oracle that is polynomial in log 1/ϵ and avoids heavy machinery such as the ellipsoid method. Demands can be real-valued functions of prices, but the oracles only return demand values of bounded precision. Due to this more realistic assumption, precision and representation of prices and demands become a major technical challenge, and we develop new tools and insights that may be of independent interest. Furthermore, we give the first polynomial-time algorithm to compute an exact equilibrium for markets with spending constraint utilities. This resolves an open problem posed by Duan and Mehlhorn. |
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School of Physical and Mathematical Sciences |
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School of Physical and Mathematical Sciences Bei, Xiaohui Garg, Jugal Hoefer, Martin |
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Article |
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Bei, Xiaohui Garg, Jugal Hoefer, Martin |
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Bei, Xiaohui |
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Ascending-price algorithms for unknown markets |
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Ascending-price algorithms for unknown markets |
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Ascending-price algorithms for unknown markets |
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Ascending-price algorithms for unknown markets |
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Ascending-price algorithms for unknown markets |
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ascending-price algorithms for unknown markets |
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2021 |
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https://hdl.handle.net/10356/150300 |
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