Bridging the Micro and Macro: Calibration of Agent-Based Model Using Mean-Field Dynamics
Ye, Peijun1; Chen, Yuanyuan1; Zhu, Fenghua1; Lv, Yisheng1; Lu, Wanze2; Wang, Fei-Yue1,3,4
Source PublicationIEEE TRANSACTIONS ON CYBERNETICS
ISSN2168-2267
2021-07-07
Volume52Issue:11Pages:11397-11406
Abstract

Calibration of agent-based models (ABM) is an essential stage when they are applied to reproduce the actual behaviors of distributed systems. Unlike traditional methods that suffer from the repeated trial and error and slow convergence of iteration, this article proposes a new ABM calibration approach by establishing a link between agent microbehavioral parameters and systemic macro-observations. With the assumption that the agent behavior can be formulated as a high-order Markovian process, the new approach starts with a search for an optimal transfer probability through a macrostate transfer equation. Then, each agent's microparameter values are computed using mean-field approximation, where his complex dependencies with others are approximated by an expected aggregate state. To compress the agent state space, principal component analysis is also introduced to avoid high dimensions of the macrostate transfer equation. The proposed method is validated in two scenarios: 1) population evolution and 2) urban travel demand analysis. Experimental results demonstrate that compared with the machine-learning surrogate and evolutionary optimization, our method can achieve higher accuracies with much lower computational complexities.

KeywordCalibration Computational modeling Mathematical model Machine learning Aggregates Optimization Bayes methods Agent-based model (ABM) calibration Markovian process
DOI10.1109/TCYB.2021.3089712
WOS KeywordOPTIMIZATION
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[62076237] ; National Natural Science Foundation of China[U1811463] ; National Natural Science Foundation of China[61903363] ; Key-Area Research and Development Program of Guangdong Province[2020B0909050001] ; Youth Innovation Promotion Association of Chinese Academy of Sciences[2021130]
Funding OrganizationNational Natural Science Foundation of China ; Key-Area Research and Development Program of Guangdong Province ; Youth Innovation Promotion Association of Chinese Academy of Sciences
WOS Research AreaAutomation & Control Systems ; Computer Science
WOS SubjectAutomation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS IDWOS:000732918500001
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Sub direction classification人工智能+交通
planning direction of the national heavy laboratory复杂系统建模与推演
Paper associated data
Citation statistics
Cited Times:7[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/46921
Collection多模态人工智能系统全国重点实验室_平行智能技术与系统团队
中国科学院自动化研究所
Corresponding AuthorLv, Yisheng
Affiliation1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Beijing Univ Technol, Sch Artificial Intelligence & Automat, Beijing 100124, Peoples R China
3.Qingdao Acad Intelligent Ind, Parallel Intelligence Res Ctr, Qingdao 266109, Peoples R China
4.Macau Univ Sci & Technol, Inst Syst Engn, Macau, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Ye, Peijun,Chen, Yuanyuan,Zhu, Fenghua,et al. Bridging the Micro and Macro: Calibration of Agent-Based Model Using Mean-Field Dynamics[J]. IEEE TRANSACTIONS ON CYBERNETICS,2021,52(11):11397-11406.
APA Ye, Peijun,Chen, Yuanyuan,Zhu, Fenghua,Lv, Yisheng,Lu, Wanze,&Wang, Fei-Yue.(2021).Bridging the Micro and Macro: Calibration of Agent-Based Model Using Mean-Field Dynamics.IEEE TRANSACTIONS ON CYBERNETICS,52(11),11397-11406.
MLA Ye, Peijun,et al."Bridging the Micro and Macro: Calibration of Agent-Based Model Using Mean-Field Dynamics".IEEE TRANSACTIONS ON CYBERNETICS 52.11(2021):11397-11406.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Ye, Peijun]'s Articles
[Chen, Yuanyuan]'s Articles
[Zhu, Fenghua]'s Articles
Baidu academic
Similar articles in Baidu academic
[Ye, Peijun]'s Articles
[Chen, Yuanyuan]'s Articles
[Zhu, Fenghua]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Ye, Peijun]'s Articles
[Chen, Yuanyuan]'s Articles
[Zhu, Fenghua]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.