{"title":"先行一步最优控制下的意见动态博弈","authors":"Gabriel Gentil;Amit Bhaya","doi":"10.1109/TCSS.2024.3364611","DOIUrl":null,"url":null,"abstract":"This article generalizes two recently proposed opinion dynamics models with control. The generalized model consists of a standard model of agents interacting with each other, to which affine control inputs from players are added. The controls, influencing the opinions of agents, are exercised by entities called players, who specify targets, possibly conflicting, for agents. Three game-playing procedures are defined: sequential, parallel, and asynchronous. Each player has knowledge of the current state of all agents, but no other information about the other players. The player controls are designed using one step ahead optimization. This leads to several novel results: easily computable control policies for each player that depend only on the player's own information and conditions for convergence to the equilibrium as well as formulas for the latter. Comparisons showing advantages over prior Riccati equation-based methods for networks of different sizes are provided. The code to reproduce all examples and simulations is available on the GitHub site. Overall, the main contribution is the one step ahead optimal control (OSAOC) framework for influencing multiagent opinion dynamics in a decentralized game-theoretic setting.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":4.5000,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Opinion Dynamic Games Under One Step Ahead Optimal Control\",\"authors\":\"Gabriel Gentil;Amit Bhaya\",\"doi\":\"10.1109/TCSS.2024.3364611\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article generalizes two recently proposed opinion dynamics models with control. The generalized model consists of a standard model of agents interacting with each other, to which affine control inputs from players are added. The controls, influencing the opinions of agents, are exercised by entities called players, who specify targets, possibly conflicting, for agents. Three game-playing procedures are defined: sequential, parallel, and asynchronous. Each player has knowledge of the current state of all agents, but no other information about the other players. The player controls are designed using one step ahead optimization. This leads to several novel results: easily computable control policies for each player that depend only on the player's own information and conditions for convergence to the equilibrium as well as formulas for the latter. Comparisons showing advantages over prior Riccati equation-based methods for networks of different sizes are provided. The code to reproduce all examples and simulations is available on the GitHub site. Overall, the main contribution is the one step ahead optimal control (OSAOC) framework for influencing multiagent opinion dynamics in a decentralized game-theoretic setting.\",\"PeriodicalId\":13044,\"journal\":{\"name\":\"IEEE Transactions on Computational Social Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.5000,\"publicationDate\":\"2024-03-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Computational Social Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10462500/\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, CYBERNETICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Computational Social Systems","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10462500/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, CYBERNETICS","Score":null,"Total":0}
Opinion Dynamic Games Under One Step Ahead Optimal Control
This article generalizes two recently proposed opinion dynamics models with control. The generalized model consists of a standard model of agents interacting with each other, to which affine control inputs from players are added. The controls, influencing the opinions of agents, are exercised by entities called players, who specify targets, possibly conflicting, for agents. Three game-playing procedures are defined: sequential, parallel, and asynchronous. Each player has knowledge of the current state of all agents, but no other information about the other players. The player controls are designed using one step ahead optimization. This leads to several novel results: easily computable control policies for each player that depend only on the player's own information and conditions for convergence to the equilibrium as well as formulas for the latter. Comparisons showing advantages over prior Riccati equation-based methods for networks of different sizes are provided. The code to reproduce all examples and simulations is available on the GitHub site. Overall, the main contribution is the one step ahead optimal control (OSAOC) framework for influencing multiagent opinion dynamics in a decentralized game-theoretic setting.
期刊介绍:
IEEE Transactions on Computational Social Systems focuses on such topics as modeling, simulation, analysis and understanding of social systems from the quantitative and/or computational perspective. "Systems" include man-man, man-machine and machine-machine organizations and adversarial situations as well as social media structures and their dynamics. More specifically, the proposed transactions publishes articles on modeling the dynamics of social systems, methodologies for incorporating and representing socio-cultural and behavioral aspects in computational modeling, analysis of social system behavior and structure, and paradigms for social systems modeling and simulation. The journal also features articles on social network dynamics, social intelligence and cognition, social systems design and architectures, socio-cultural modeling and representation, and computational behavior modeling, and their applications.