Manipulation Game Considering No-Regret Strategies

被引:0
|
作者
Clempner, Julio B. [1 ]
机构
[1] Inst Politecn Nacl, Mexico City 07320, Mexico
关键词
Machiavellianism; manipulation; no regret; Markov chains; game theory; STOCK-PRICE MANIPULATION; BAYESIAN PERSUASION; INFORMATION;
D O I
10.3390/math13020184
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
This paper examines manipulation games through the lens of Machiavellianism, a psychological theory. It analyzes manipulation dynamics using principles like hierarchical perspectives, exploitation tactics, and the absence of conventional morals to interpret interpersonal interactions. Manipulators intersperse unethical behavior within their typical conduct, deploying deceptive tactics before resuming a baseline demeanor. The proposed solution leverages Lyapunov theory to establish and maintain Stackelberg equilibria. A Lyapunov-like function supports each asymptotically stable equilibrium, ensuring convergence to a Nash/Lyapunov equilibrium if it exists, inherently favoring no-regret strategies. The existence of an optimal solution is demonstrated via the Weierstrass theorem. The game is modeled as a three-level Stackelberg framework based on Markov chains. At the highest level, manipulators devise strategies that may not sway middle-level manipulated players, who counter with best-reply strategies mirroring the manipulators' moves. Lower-level manipulators adjust their strategies in response to the manipulated players to sustain the manipulation process. This integration of stability analysis and strategic decision-making provides a robust framework for understanding and addressing manipulation in interpersonal contexts. A numerical example focusing on the oil market and its regulations highlights the findings of this work.
引用
收藏
页数:18
相关论文
共 50 条
  • [31] No-regret bayesian optimization with unknown hyperparameters
    Berkenkamp, Felix
    Schoellig, Angela P.
    Krause, Andreas
    Journal of Machine Learning Research, 2019, 20
  • [32] No-Regret Bayesian Optimization with Unknown Hyperparameters
    Berkenkamp, Felix
    Schoellig, Angela P.
    Krause, Andreas
    JOURNAL OF MACHINE LEARNING RESEARCH, 2019, 20
  • [33] No-Regret Linear Bandits beyond Realizability
    Liu, Chong
    Yin, Ming
    Wang, Yu-Xiang
    UNCERTAINTY IN ARTIFICIAL INTELLIGENCE, 2023, 216 : 1294 - 1303
  • [34] Weighted Voting Via No-Regret Learning
    Haghtalab, Nika
    Noothigattu, Ritesh
    Procaccia, Ariel D.
    THIRTY-SECOND AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTIETH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / EIGHTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2018, : 1055 - 1062
  • [35] No-regret Exploration in Contextual Reinforcement Learning
    Modi, Aditya
    Tewari, Ambuj
    CONFERENCE ON UNCERTAINTY IN ARTIFICIAL INTELLIGENCE (UAI 2020), 2020, 124 : 829 - 838
  • [36] No-Regret Online Prediction with Strategic Experts
    Sadeghi, Omid
    Fazel, Maryam
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 36 (NEURIPS 2023), 2023,
  • [37] No-Regret Caching via Online Mirror Descent
    Salem, Tareq Si
    Neglia, Giovanni
    Ioannidis, Straus
    IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC 2021), 2021,
  • [38] Doubly Optimal No-Regret Learning in Monotone Games
    Cai, Yang
    Zheng, Weiqiang
    INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 202, 2023, 202
  • [39] No-Regret and Incentive-Compatible Online Learning
    Freeman, Rupert
    Pennock, David M.
    Podimata, Chara
    Vaughan, Jennifer Wortman
    INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 119, 2020, 119
  • [40] Mechanisms for a No-Regret Agent: Beyond the Common Prior
    Camara, Modibo K.
    Hartline, Jason D.
    Johnsen, Aleck
    2020 IEEE 61ST ANNUAL SYMPOSIUM ON FOUNDATIONS OF COMPUTER SCIENCE (FOCS 2020), 2020, : 259 - 270