Computing large market equilibria using abstractions
| Autor: | Nicolas E. Stier-Moses, Eric Sodomka, Alexander Peysakhovich, Christian Kroer |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
TheoryofComputation_MISCELLANEOUS
FOS: Computer and information sciences Matrix completion Programming language Computer science Computer Science - Artificial Intelligence Pareto principle TheoryofComputation_GENERAL Social Welfare Regret Management Science and Operations Research computer.software_genre Minimax Competitive equilibrium Computer Science Applications Artificial Intelligence (cs.AI) Computer Science - Computer Science and Game Theory Computer Science - Multiagent Systems Abstraction computer Mathematical economics Fair division Computer Science and Game Theory (cs.GT) Multiagent Systems (cs.MA) |
| Zdroj: | EC |
| DOI: | 10.48550/arxiv.1901.06230 |
| Popis: | Computing market equilibria is an important practical problem for market design, for example in fair division of items. However, computing equilibria requires large amounts of information (typically the valuation of every buyer for every item) and computing power. We consider ameliorating these issues by applying a method used for solving complex games: constructing a coarsened abstraction of a given market, solving for the equilibrium in the abstraction, and lifting the prices and allocations back to the original market. We show how to bound important quantities such as regret, envy, Nash social welfare, Pareto optimality, and maximin share/proportionality when the abstracted prices and allocations are used in place of the real equilibrium. We then study two abstraction methods of interest for practitioners: (1) filling in unknown valuations using techniques from matrix completion, (2) reducing the problem size by aggregating groups of buyers/items into smaller numbers of representative buyers/items and solving for equilibrium in this coarsened market. We find that in real data allocations/prices that are relatively close to equilibria can be computed from even very coarse abstractions. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|