Markets, correlation, and regret-matching

被引：6

作者：

Hart, Sergiu ^{[1
,2
]}

Mas-Colell, Andreu ^{[3
,4
]}

机构：

[1] Hebrew Univ Jerusalem, Dept Econ, Federmann Ctr Study Rat, IL-9190401 Jerusalem, Israel

[2] Hebrew Univ Jerusalem, Einstein Inst Math, IL-9190401 Jerusalem, Israel

[3] Univ Pompeu Fabra, Dept Econ & Business, Barcelona 08005, Spain

[4] Barcelona Grad Sch Econ, Barcelona 08005, Spain

来源：

GAMES AND ECONOMIC BEHAVIOR | 2015年 / 93卷

基金：

欧洲研究理事会;

关键词：

Exchange economies; Walrasian equilibrium; Correlated equilibrium; Sunspot equilibrium; Dynamics; Regret-matching; Socially concave games; Deterministic regret-matching dynamics; Hannan set; Implementation of Walrasian equilibria; EQUILIBRIUM; EXISTENCE;

D O I：

10.1016/j.geb.2015.06.009

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

Inspired by the existing work on correlated equilibria and regret-based dynamics in games, we carry out a first exploration of the links between equilibria and dynamics in (exchange) economies. The leading equilibrium concept is Walrasian equilibrium, and the dynamics (specifically, regret-matching dynamics) apply to trading games that fit the economic structure and whose pure Nash equilibria implement the Walrasian outcomes. Interestingly, in the case of quasilinear utilities (or "transferable utility"), all the concepts essentially coincide, and we get simple deterministic dynamics converging to Walrasian outcomes. Connections to sunspot equilibria are also studied. (C) 2015 Elsevier Inc. All rights reserved.

引用

页码：42 / 58

页数：17

共 50 条

[1] Neural Regret-Matching for Distributed Constraint Optimization Problems
Deng, Yanchen
Yu, Runshen
Wang, Xinrun
An, Bo
PROCEEDINGS OF THE THIRTIETH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, IJCAI 2021, 2021, : 146 - 153
[2] Predictive Regret-Matching for Cooperating Interceptors to Defeat an Advanced Threat
Rajagopalan, Arvind
Duong Duc Nguyen
Kim, Jijoong
AI 2019: ADVANCES IN ARTIFICIAL INTELLIGENCE, 2019, 11919 : 28 - 40
[3] Simple Adaptive Strategies: From Regret-Matching to Uncoupled Dynamics
Levine, David K.
GAMES AND ECONOMIC BEHAVIOR, 2014, 87 : 652 - 653
[4] Regret, stability & fairness in matching markets with bandit learners
Cen, Sarah H.
Shah, Devavrat
INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 151, 2022, 151
[5] Multi-Agent Task Assignment in Vehicular Edge Computing: A Regret-Matching Learning-Based Approach
Nguyen, Bach Long
Nguyen, Duong D.
Nguyen, Hung X.
Ngo, Duy T.
Wagner, Markus
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE, 2024, 8 (02): : 1527 - 1539
[6] Distributed Interference Mitigation in Two-Tier Wireless Networks Using Correlated Equilibrium and Regret-Matching Learning
Sroka, Pawel
Kliks, Adrian
2014 EUROPEAN CONFERENCE ON NETWORKS AND COMMUNICATIONS (EUCNC), 2014,
[7] Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets
Min, Yifei
Wang, Tianhao
Xu, Ruitu
Wang, Zhaoran
Jordan, Michael I.
Yang, Zhuoran
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 35 (NEURIPS 2022), 2022,
[8] Player-optimal Stable Regret for Bandit Learning in Matching Markets
Kong, Fang
Li, Shuai
PROCEEDINGS OF THE 2023 ANNUAL ACM-SIAM SYMPOSIUM ON DISCRETE ALGORITHMS, SODA, 2023, : 1512 - 1522
[9] Beyond log2(T) Regret for Decentralized Bandits in Matching Markets
Basu, Soumya
Sankararaman, Karthik Abinav
Sankararaman, Abishek
INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 139, 2021, 139
[10] HIDDEN REGRET IN INSURANCE MARKETS
Huang, Rachel J.
Muermann, Alexander
Tzeng, Larry Y.
JOURNAL OF RISK AND INSURANCE, 2016, 83 (01) : 181 - 216

← 1 2 3 4 5 →