Aspiration Learning in Coordination Games
(2013) In SIAM Journal of Control and Optimization 51(1). p.465-490- Abstract
- We consider the problem of distributed convergence to efficient outcomes in coordination games through dynamics based on aspiration learning. Our first contribution is the characterization of the asymptotic behavior of the induced Markov chain of the iterated process in terms of an equivalent finite-state Markov chain. We then characterize explicitly the behavior of the proposed aspiration learning in a generalized version of coordination games, examples of which include network formation and common-pool games. In particular, we show that in generic coordination games the frequency at which an efficient action profile is played can be made arbitrarily large. Although convergence to efficient outcomes is desirable, in several coordination... (More)
- We consider the problem of distributed convergence to efficient outcomes in coordination games through dynamics based on aspiration learning. Our first contribution is the characterization of the asymptotic behavior of the induced Markov chain of the iterated process in terms of an equivalent finite-state Markov chain. We then characterize explicitly the behavior of the proposed aspiration learning in a generalized version of coordination games, examples of which include network formation and common-pool games. In particular, we show that in generic coordination games the frequency at which an efficient action profile is played can be made arbitrarily large. Although convergence to efficient outcomes is desirable, in several coordination games, such as common-pool games, attainability of fair outcomes, i.e., sequences of plays at which players experience highly rewarding returns with the same frequency, might also be of special interest. To this end, we demonstrate through analysis and simulations that aspiration learning also establishes fair outcomes in all symmetric coordination games, including common-pool games.
Read More: http://epubs.siam.org/doi/abs/10.1137/110852462 (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/3513141
- author
- Chasparis, Georgios LU ; Arapostathis, Ari and Shamma, Jeff S.
- organization
- publishing date
- 2013
- type
- Contribution to journal
- publication status
- published
- subject
- in
- SIAM Journal of Control and Optimization
- volume
- 51
- issue
- 1
- pages
- 465 - 490
- publisher
- Society for Industrial and Applied Mathematics
- external identifiers
-
- wos:000315578000020
- scopus:84876114431
- ISSN
- 1095-7138
- DOI
- 10.1137/110852462
- project
- LCCC
- language
- English
- LU publication?
- yes
- id
- d5a5a9ac-a217-4a7e-a1e6-6acc7556ae5f (old id 3513141)
- date added to LUP
- 2016-04-01 11:16:06
- date last changed
- 2022-05-06 05:47:40
@article{d5a5a9ac-a217-4a7e-a1e6-6acc7556ae5f,
abstract = {{We consider the problem of distributed convergence to efficient outcomes in coordination games through dynamics based on aspiration learning. Our first contribution is the characterization of the asymptotic behavior of the induced Markov chain of the iterated process in terms of an equivalent finite-state Markov chain. We then characterize explicitly the behavior of the proposed aspiration learning in a generalized version of coordination games, examples of which include network formation and common-pool games. In particular, we show that in generic coordination games the frequency at which an efficient action profile is played can be made arbitrarily large. Although convergence to efficient outcomes is desirable, in several coordination games, such as common-pool games, attainability of fair outcomes, i.e., sequences of plays at which players experience highly rewarding returns with the same frequency, might also be of special interest. To this end, we demonstrate through analysis and simulations that aspiration learning also establishes fair outcomes in all symmetric coordination games, including common-pool games.<br/><br>
<br/><br>
<br/><br>
Read More: http://epubs.siam.org/doi/abs/10.1137/110852462}},
author = {{Chasparis, Georgios and Arapostathis, Ari and Shamma, Jeff S.}},
issn = {{1095-7138}},
language = {{eng}},
number = {{1}},
pages = {{465--490}},
publisher = {{Society for Industrial and Applied Mathematics}},
series = {{SIAM Journal of Control and Optimization}},
title = {{Aspiration Learning in Coordination Games}},
url = {{http://dx.doi.org/10.1137/110852462}},
doi = {{10.1137/110852462}},
volume = {{51}},
year = {{2013}},
}