Relative Best Response Dynamics in finite and convex Network Games

Motivated by theoretical and experimental economics, we propose novel evolutionary dynamics for games on networks, called the h-Relative Best Response (h-RBR) dynamics, that mixes the relative performance considerations of imitation dynamics with the rationality of best responses. Under such a class...

Full description

Saved in:
Bibliographic Details
Main Authors: Govaert, Alain, Cenedese, Carlo, Grammatico, Sergio, Cao, Ming
Format: Conference Proceeding
Language:English
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page 3139
container_issue
container_start_page 3134
container_title
container_volume
creator Govaert, Alain
Cenedese, Carlo
Grammatico, Sergio
Cao, Ming
description Motivated by theoretical and experimental economics, we propose novel evolutionary dynamics for games on networks, called the h-Relative Best Response (h-RBR) dynamics, that mixes the relative performance considerations of imitation dynamics with the rationality of best responses. Under such a class of dynamics, the players optimize their payoffs over the set of strategies employed by a time-varying subset of their neighbors. As such, the h-RBR dynamics share the defining non-innovative characteristic of imitation based dynamics and can lead to equilibria that differ from classic Nash equilibria. We study the asymptotic behavior of the h-RBR dynamics for both finite and convex games in which the strategy spaces are discrete and compact, respectively, and provide preliminary sufficient conditions for finite-time convergence to a generalized Nash equilibrium.
doi_str_mv 10.1109/CDC40024.2019.9029821
format conference_proceeding
fullrecord <record><control><sourceid>ieee_CHZPO</sourceid><recordid>TN_cdi_ieee_primary_9029821</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>9029821</ieee_id><sourcerecordid>9029821</sourcerecordid><originalsourceid>FETCH-LOGICAL-i250t-d5e59359ca145132a79bdbfe94aab5acecb76fbfa2506c98ed57ab115b74d90f3</originalsourceid><addsrcrecordid>eNotj91KwzAYQKMguE2fQIS8QGt-m36XrnNTGApDr8eX9AtEt3Y0Zbq3V3BX5-Zw4DB2L0UppYCHZtEYIZQplZBQglBQK3nBptKpWkoNNVyyibKuKpR24ppNc_4UQgMYPWHzDe1wTEfic8oj31A-9F0mvjh1uE8h89TxmLo0Eseu5aHvjvTDX2n87ocvvsI95Rt2FXGX6fbMGftYPr03z8X6bfXSPK6LpKwYi9aSBW0hoDRWaoUOfOsjgUH0FgMF76roI_7ZVYCaWuvQS2m9My2IqGfs7r-biGh7GNIeh9P2_Kt_AZ8TSv0</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Relative Best Response Dynamics in finite and convex Network Games</title><source>IEEE Xplore All Conference Series</source><creator>Govaert, Alain ; Cenedese, Carlo ; Grammatico, Sergio ; Cao, Ming</creator><creatorcontrib>Govaert, Alain ; Cenedese, Carlo ; Grammatico, Sergio ; Cao, Ming</creatorcontrib><description>Motivated by theoretical and experimental economics, we propose novel evolutionary dynamics for games on networks, called the h-Relative Best Response (h-RBR) dynamics, that mixes the relative performance considerations of imitation dynamics with the rationality of best responses. Under such a class of dynamics, the players optimize their payoffs over the set of strategies employed by a time-varying subset of their neighbors. As such, the h-RBR dynamics share the defining non-innovative characteristic of imitation based dynamics and can lead to equilibria that differ from classic Nash equilibria. We study the asymptotic behavior of the h-RBR dynamics for both finite and convex games in which the strategy spaces are discrete and compact, respectively, and provide preliminary sufficient conditions for finite-time convergence to a generalized Nash equilibrium.</description><identifier>EISSN: 2576-2370</identifier><identifier>EISBN: 1728113989</identifier><identifier>EISBN: 9781728113982</identifier><identifier>DOI: 10.1109/CDC40024.2019.9029821</identifier><language>eng</language><publisher>IEEE</publisher><ispartof>2019 IEEE 58th Conference on Decision and Control (CDC), 2019, p.3134-3139</ispartof><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9029821$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,777,781,786,787,23911,23912,25121,27906,54536,54913</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9029821$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Govaert, Alain</creatorcontrib><creatorcontrib>Cenedese, Carlo</creatorcontrib><creatorcontrib>Grammatico, Sergio</creatorcontrib><creatorcontrib>Cao, Ming</creatorcontrib><title>Relative Best Response Dynamics in finite and convex Network Games</title><title>2019 IEEE 58th Conference on Decision and Control (CDC)</title><addtitle>CDC</addtitle><description>Motivated by theoretical and experimental economics, we propose novel evolutionary dynamics for games on networks, called the h-Relative Best Response (h-RBR) dynamics, that mixes the relative performance considerations of imitation dynamics with the rationality of best responses. Under such a class of dynamics, the players optimize their payoffs over the set of strategies employed by a time-varying subset of their neighbors. As such, the h-RBR dynamics share the defining non-innovative characteristic of imitation based dynamics and can lead to equilibria that differ from classic Nash equilibria. We study the asymptotic behavior of the h-RBR dynamics for both finite and convex games in which the strategy spaces are discrete and compact, respectively, and provide preliminary sufficient conditions for finite-time convergence to a generalized Nash equilibrium.</description><issn>2576-2370</issn><isbn>1728113989</isbn><isbn>9781728113982</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2019</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotj91KwzAYQKMguE2fQIS8QGt-m36XrnNTGApDr8eX9AtEt3Y0Zbq3V3BX5-Zw4DB2L0UppYCHZtEYIZQplZBQglBQK3nBptKpWkoNNVyyibKuKpR24ppNc_4UQgMYPWHzDe1wTEfic8oj31A-9F0mvjh1uE8h89TxmLo0Eseu5aHvjvTDX2n87ocvvsI95Rt2FXGX6fbMGftYPr03z8X6bfXSPK6LpKwYi9aSBW0hoDRWaoUOfOsjgUH0FgMF76roI_7ZVYCaWuvQS2m9My2IqGfs7r-biGh7GNIeh9P2_Kt_AZ8TSv0</recordid><startdate>20191201</startdate><enddate>20191201</enddate><creator>Govaert, Alain</creator><creator>Cenedese, Carlo</creator><creator>Grammatico, Sergio</creator><creator>Cao, Ming</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>20191201</creationdate><title>Relative Best Response Dynamics in finite and convex Network Games</title><author>Govaert, Alain ; Cenedese, Carlo ; Grammatico, Sergio ; Cao, Ming</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i250t-d5e59359ca145132a79bdbfe94aab5acecb76fbfa2506c98ed57ab115b74d90f3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2019</creationdate><toplevel>online_resources</toplevel><creatorcontrib>Govaert, Alain</creatorcontrib><creatorcontrib>Cenedese, Carlo</creatorcontrib><creatorcontrib>Grammatico, Sergio</creatorcontrib><creatorcontrib>Cao, Ming</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Govaert, Alain</au><au>Cenedese, Carlo</au><au>Grammatico, Sergio</au><au>Cao, Ming</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Relative Best Response Dynamics in finite and convex Network Games</atitle><btitle>2019 IEEE 58th Conference on Decision and Control (CDC)</btitle><stitle>CDC</stitle><date>2019-12-01</date><risdate>2019</risdate><spage>3134</spage><epage>3139</epage><pages>3134-3139</pages><eissn>2576-2370</eissn><eisbn>1728113989</eisbn><eisbn>9781728113982</eisbn><abstract>Motivated by theoretical and experimental economics, we propose novel evolutionary dynamics for games on networks, called the h-Relative Best Response (h-RBR) dynamics, that mixes the relative performance considerations of imitation dynamics with the rationality of best responses. Under such a class of dynamics, the players optimize their payoffs over the set of strategies employed by a time-varying subset of their neighbors. As such, the h-RBR dynamics share the defining non-innovative characteristic of imitation based dynamics and can lead to equilibria that differ from classic Nash equilibria. We study the asymptotic behavior of the h-RBR dynamics for both finite and convex games in which the strategy spaces are discrete and compact, respectively, and provide preliminary sufficient conditions for finite-time convergence to a generalized Nash equilibrium.</abstract><pub>IEEE</pub><doi>10.1109/CDC40024.2019.9029821</doi><tpages>6</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext_linktorsrc
identifier EISSN: 2576-2370
ispartof 2019 IEEE 58th Conference on Decision and Control (CDC), 2019, p.3134-3139
issn 2576-2370
language eng
recordid cdi_ieee_primary_9029821
source IEEE Xplore All Conference Series
title Relative Best Response Dynamics in finite and convex Network Games
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-19T18%3A49%3A45IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_CHZPO&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Relative%20Best%20Response%20Dynamics%20in%20finite%20and%20convex%20Network%20Games&rft.btitle=2019%20IEEE%2058th%20Conference%20on%20Decision%20and%20Control%20(CDC)&rft.au=Govaert,%20Alain&rft.date=2019-12-01&rft.spage=3134&rft.epage=3139&rft.pages=3134-3139&rft.eissn=2576-2370&rft_id=info:doi/10.1109/CDC40024.2019.9029821&rft.eisbn=1728113989&rft.eisbn_list=9781728113982&rft_dat=%3Cieee_CHZPO%3E9029821%3C/ieee_CHZPO%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-i250t-d5e59359ca145132a79bdbfe94aab5acecb76fbfa2506c98ed57ab115b74d90f3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=9029821&rfr_iscdi=true