Multiagent learning for competitive opinion optimization

From a perspective of designing or engineering for opinion formation games in social networks, the opinion maximization (or minimization) problem has been studied mainly for designing seeding algorithms that aim at selecting a subset of nodes to control their opinions. We first define a two-player z...

Full description

Saved in:
Bibliographic Details
Published in:Theoretical computer science 2024-11, Vol.1017, p.114787, Article 114787
Main Authors: Chen, Po-An, Lu, Chi-Jen, Lin, Chuang-Chieh, Teng, An-Tzu, Fu, Ke-Wei
Format: Article
Language:English
Subjects:
Competitive opinion optimization
Multiagent learning
Optimistic gradient descent ascent
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites cdi_FETCH-LOGICAL-c222t-37426215fbaa951071cb8e583c7f9885acce6f90e1f80b455114c6d625bc1af83
container_end_page
container_issue
container_start_page 114787
container_title Theoretical computer science
container_volume 1017
creator Chen, Po-An
Lu, Chi-Jen
Lin, Chuang-Chieh
Teng, An-Tzu
Fu, Ke-Wei
description From a perspective of designing or engineering for opinion formation games in social networks, the opinion maximization (or minimization) problem has been studied mainly for designing seeding algorithms that aim at selecting a subset of nodes to control their opinions. We first define a two-player zero-sum Stackelberg game of competitive opinion optimization by letting the player under study as the leader minimize the sum of expressed opinions by doing so-called “internal opinion design”, knowing that the other adversarial player as the follower is to maximize the same objective by also conducting her own internal opinion design. We furthermore consider multiagent learning, specifically using the Optimistic Gradient Descent Ascent, and analyze its convergence to equilibria in the simultaneous-game version of competitive opinion optimization.
doi_str_mv 10.1016/j.tcs.2024.114787
format article
fullrecord <record><control><sourceid>elsevier_cross</sourceid><recordid>TN_cdi_crossref_primary_10_1016_j_tcs_2024_114787</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0304397524004043</els_id><sourcerecordid>S0304397524004043</sourcerecordid><originalsourceid>FETCH-LOGICAL-c222t-37426215fbaa951071cb8e583c7f9885acce6f90e1f80b455114c6d625bc1af83</originalsourceid><addsrcrecordid>eNp9j8tOwzAQRb0AiVL4AHb5gQTbiWNHrFDFSypiA2vLmY6rqfKobFMJvh5XZd3ZXM3iXN3D2J3gleCivd9VCWIluWwqIRpt9AVb8Jo3Zd1pdcWuY9zxfEq3C2bev4dEbotTKgZ0YaJpW_g5FDCPe0yU6IDFvKeJ5ilnopF-XcrPDbv0boh4-59L9vX89Ll6LdcfL2-rx3UJUspU1rqRrRTK9851SnAtoDeoTA3ad8YoB4Ct7zgKb3jfKJUXQ7tppepBOG_qJROnXghzjAG93QcaXfixgtujrt3ZrGuPuvakm5mHE4N52IEw2AiEE-CGAkKym5nO0H_NYmAf</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Multiagent learning for competitive opinion optimization</title><source>ScienceDirect Freedom Collection</source><creator>Chen, Po-An ; Lu, Chi-Jen ; Lin, Chuang-Chieh ; Teng, An-Tzu ; Fu, Ke-Wei</creator><creatorcontrib>Chen, Po-An ; Lu, Chi-Jen ; Lin, Chuang-Chieh ; Teng, An-Tzu ; Fu, Ke-Wei</creatorcontrib><description>From a perspective of designing or engineering for opinion formation games in social networks, the opinion maximization (or minimization) problem has been studied mainly for designing seeding algorithms that aim at selecting a subset of nodes to control their opinions. We first define a two-player zero-sum Stackelberg game of competitive opinion optimization by letting the player under study as the leader minimize the sum of expressed opinions by doing so-called “internal opinion design”, knowing that the other adversarial player as the follower is to maximize the same objective by also conducting her own internal opinion design. We furthermore consider multiagent learning, specifically using the Optimistic Gradient Descent Ascent, and analyze its convergence to equilibria in the simultaneous-game version of competitive opinion optimization.</description><identifier>ISSN: 0304-3975</identifier><identifier>DOI: 10.1016/j.tcs.2024.114787</identifier><language>eng</language><publisher>Elsevier B.V</publisher><subject>Competitive opinion optimization ; Multiagent learning ; Optimistic gradient descent ascent</subject><ispartof>Theoretical computer science, 2024-11, Vol.1017, p.114787, Article 114787</ispartof><rights>2024 Elsevier B.V.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c222t-37426215fbaa951071cb8e583c7f9885acce6f90e1f80b455114c6d625bc1af83</cites><orcidid>0000-0001-5145-5839</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27903,27904</link.rule.ids></links><search><creatorcontrib>Chen, Po-An</creatorcontrib><creatorcontrib>Lu, Chi-Jen</creatorcontrib><creatorcontrib>Lin, Chuang-Chieh</creatorcontrib><creatorcontrib>Teng, An-Tzu</creatorcontrib><creatorcontrib>Fu, Ke-Wei</creatorcontrib><title>Multiagent learning for competitive opinion optimization</title><title>Theoretical computer science</title><description>From a perspective of designing or engineering for opinion formation games in social networks, the opinion maximization (or minimization) problem has been studied mainly for designing seeding algorithms that aim at selecting a subset of nodes to control their opinions. We first define a two-player zero-sum Stackelberg game of competitive opinion optimization by letting the player under study as the leader minimize the sum of expressed opinions by doing so-called “internal opinion design”, knowing that the other adversarial player as the follower is to maximize the same objective by also conducting her own internal opinion design. We furthermore consider multiagent learning, specifically using the Optimistic Gradient Descent Ascent, and analyze its convergence to equilibria in the simultaneous-game version of competitive opinion optimization.</description><subject>Competitive opinion optimization</subject><subject>Multiagent learning</subject><subject>Optimistic gradient descent ascent</subject><issn>0304-3975</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp9j8tOwzAQRb0AiVL4AHb5gQTbiWNHrFDFSypiA2vLmY6rqfKobFMJvh5XZd3ZXM3iXN3D2J3gleCivd9VCWIluWwqIRpt9AVb8Jo3Zd1pdcWuY9zxfEq3C2bev4dEbotTKgZ0YaJpW_g5FDCPe0yU6IDFvKeJ5ilnopF-XcrPDbv0boh4-59L9vX89Ll6LdcfL2-rx3UJUspU1rqRrRTK9851SnAtoDeoTA3ad8YoB4Ct7zgKb3jfKJUXQ7tppepBOG_qJROnXghzjAG93QcaXfixgtujrt3ZrGuPuvakm5mHE4N52IEw2AiEE-CGAkKym5nO0H_NYmAf</recordid><startdate>20241121</startdate><enddate>20241121</enddate><creator>Chen, Po-An</creator><creator>Lu, Chi-Jen</creator><creator>Lin, Chuang-Chieh</creator><creator>Teng, An-Tzu</creator><creator>Fu, Ke-Wei</creator><general>Elsevier B.V</general><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0001-5145-5839</orcidid></search><sort><creationdate>20241121</creationdate><title>Multiagent learning for competitive opinion optimization</title><author>Chen, Po-An ; Lu, Chi-Jen ; Lin, Chuang-Chieh ; Teng, An-Tzu ; Fu, Ke-Wei</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c222t-37426215fbaa951071cb8e583c7f9885acce6f90e1f80b455114c6d625bc1af83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Competitive opinion optimization</topic><topic>Multiagent learning</topic><topic>Optimistic gradient descent ascent</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chen, Po-An</creatorcontrib><creatorcontrib>Lu, Chi-Jen</creatorcontrib><creatorcontrib>Lin, Chuang-Chieh</creatorcontrib><creatorcontrib>Teng, An-Tzu</creatorcontrib><creatorcontrib>Fu, Ke-Wei</creatorcontrib><collection>CrossRef</collection><jtitle>Theoretical computer science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chen, Po-An</au><au>Lu, Chi-Jen</au><au>Lin, Chuang-Chieh</au><au>Teng, An-Tzu</au><au>Fu, Ke-Wei</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Multiagent learning for competitive opinion optimization</atitle><jtitle>Theoretical computer science</jtitle><date>2024-11-21</date><risdate>2024</risdate><volume>1017</volume><spage>114787</spage><pages>114787-</pages><artnum>114787</artnum><issn>0304-3975</issn><abstract>From a perspective of designing or engineering for opinion formation games in social networks, the opinion maximization (or minimization) problem has been studied mainly for designing seeding algorithms that aim at selecting a subset of nodes to control their opinions. We first define a two-player zero-sum Stackelberg game of competitive opinion optimization by letting the player under study as the leader minimize the sum of expressed opinions by doing so-called “internal opinion design”, knowing that the other adversarial player as the follower is to maximize the same objective by also conducting her own internal opinion design. We furthermore consider multiagent learning, specifically using the Optimistic Gradient Descent Ascent, and analyze its convergence to equilibria in the simultaneous-game version of competitive opinion optimization.</abstract><pub>Elsevier B.V</pub><doi>10.1016/j.tcs.2024.114787</doi><orcidid>https://orcid.org/0000-0001-5145-5839</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 0304-3975
ispartof Theoretical computer science, 2024-11, Vol.1017, p.114787, Article 114787
issn 0304-3975
language eng
recordid cdi_crossref_primary_10_1016_j_tcs_2024_114787
source ScienceDirect Freedom Collection
subjects Competitive opinion optimization
Multiagent learning
Optimistic gradient descent ascent
title Multiagent learning for competitive opinion optimization
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-22T12%3A26%3A59IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-elsevier_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Multiagent%20learning%20for%20competitive%20opinion%20optimization&rft.jtitle=Theoretical%20computer%20science&rft.au=Chen,%20Po-An&rft.date=2024-11-21&rft.volume=1017&rft.spage=114787&rft.pages=114787-&rft.artnum=114787&rft.issn=0304-3975&rft_id=info:doi/10.1016/j.tcs.2024.114787&rft_dat=%3Celsevier_cross%3ES0304397524004043%3C/elsevier_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c222t-37426215fbaa951071cb8e583c7f9885acce6f90e1f80b455114c6d625bc1af83%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true