Open Access
Learning and aciclicity in the market game
Loading...
Files
MWP_2021_06.pdf (605.73 KB)
Full-text in Open Access
License
Attribution 4.0 International
Cadmus Permanent Link
Full-text via DOI
ISBN
ISSN
1830-7728
Issue Date
Type of Publication
LC Subject Heading
Other Topic(s)
EUI Research Cluster(s)
Initial version
Published version
Succeeding version
Preceding version
Published version part
Earlier different version
Initial format
Author(s)
Citation
EUI MWP; 2021/06
Cite
DOLGOPOLOV, Artur, MARTINELLI, César, Learning and aciclicity in the market game, EUI MWP, 2021/06 - https://hdl.handle.net/1814/72420
Abstract
We show that strategic market games, the non-cooperative implementation of a matching with transfers or an assignment game, are weakly acyclic. This property ensures that many common learning algorithms will converge to Nash equilibria in these games, and that the allocation mechanism can therefore be decentralized. Convergence hinges on the appropriate price clearing rule and has different properties for better- and best-response dynamics. We tightly characterize the robustness of this convergence in terms of so-called schedulers for both types of dynamics.