Evolutionary game gynamics driven by heterogeneous self-learning rules

Lei Zhou; Bin Wu; Vitor V. Vasconcelos; Long Wang

doi:10.1109/DDCLS.2018.8515931

Evolutionary game gynamics driven by heterogeneous self-learning rules

Lei Zhou, Bin Wu, Vitor V. Vasconcelos, Long Wang^*

^*此作品的通讯作者

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

摘要

How to achieve full cooperation among large numbers of individuals is essential for both artificial and biological systems. Learning rules (or updating rules), which specify how individuals change their behavior over time, are vital to probe this problem. Here, we incorporate individual heterogeneity into the self-evaluation process and propose the heterogeneous self-learning dynamics. When the selection intensity is weak, we analytically derive that the final outcomes of the heterogeneous dynamics can be obtained by combining the outcomes of all the corresponding homogeneous dynamics in well-mixed populations. Meanwhile, a simple condition is found which tells whether one behavior will be more abundant than the other in the long run. All of our analytical results are verified by simulations. Our work thus reveals some interesting characteristics of heterogeneous self-learning dynamics.

源语言	英语
主期刊名	Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018
出版商	Institute of Electrical and Electronics Engineers Inc.
页	825-829
页数	5
ISBN（电子版）	9781538626184
DOI	https://doi.org/10.1109/DDCLS.2018.8515931
出版状态	已出版 - 30 10月 2018
已对外发布	是
活动	7th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2018 - Enshi, Hubei Province, 中国期限: 25 5月 2018 → 27 5月 2018

出版系列

姓名	Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018

会议

会议	7th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2018
国家/地区	中国
市	Enshi, Hubei Province
时期	25/05/18 → 27/05/18

访问文件

10.1109/DDCLS.2018.8515931

其它文件与链接

链接到 Scopus 的出版物

引用此

Zhou, L., Wu, B., Vasconcelos, V. V., & Wang, L. (2018). Evolutionary game gynamics driven by heterogeneous self-learning rules. 在 Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018 (页码 825-829). 文章 8515931 (Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DDCLS.2018.8515931

Zhou, Lei ; Wu, Bin ; Vasconcelos, Vitor V. 等. / Evolutionary game gynamics driven by heterogeneous self-learning rules. Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018. Institute of Electrical and Electronics Engineers Inc., 2018. 页码 825-829 (Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018).

@inproceedings{779a2af841794f29bc62475a745246f2,

title = "Evolutionary game gynamics driven by heterogeneous self-learning rules",

abstract = "How to achieve full cooperation among large numbers of individuals is essential for both artificial and biological systems. Learning rules (or updating rules), which specify how individuals change their behavior over time, are vital to probe this problem. Here, we incorporate individual heterogeneity into the self-evaluation process and propose the heterogeneous self-learning dynamics. When the selection intensity is weak, we analytically derive that the final outcomes of the heterogeneous dynamics can be obtained by combining the outcomes of all the corresponding homogeneous dynamics in well-mixed populations. Meanwhile, a simple condition is found which tells whether one behavior will be more abundant than the other in the long run. All of our analytical results are verified by simulations. Our work thus reveals some interesting characteristics of heterogeneous self-learning dynamics.",

keywords = "Cooperation, Evolutionary dynamics, Game theory, Self-learning rules",

author = "Lei Zhou and Bin Wu and Vasconcelos, {Vitor V.} and Long Wang",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 7th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2018 ; Conference date: 25-05-2018 Through 27-05-2018",

year = "2018",

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doi = "10.1109/DDCLS.2018.8515931",

language = "English",

series = "Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Zhou, L, Wu, B, Vasconcelos, VV & Wang, L 2018, Evolutionary game gynamics driven by heterogeneous self-learning rules. 在 Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018., 8515931, Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018, Institute of Electrical and Electronics Engineers Inc., 页码 825-829, 7th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2018, Enshi, Hubei Province, 中国, 25/05/18. https://doi.org/10.1109/DDCLS.2018.8515931

Evolutionary game gynamics driven by heterogeneous self-learning rules. / Zhou, Lei; Wu, Bin; Vasconcelos, Vitor V. 等.
Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018. Institute of Electrical and Electronics Engineers Inc., 2018. 页码 825-829 8515931 (Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

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AU - Zhou, Lei

AU - Wu, Bin

AU - Vasconcelos, Vitor V.

AU - Wang, Long

PY - 2018/10/30

Y1 - 2018/10/30

N2 - How to achieve full cooperation among large numbers of individuals is essential for both artificial and biological systems. Learning rules (or updating rules), which specify how individuals change their behavior over time, are vital to probe this problem. Here, we incorporate individual heterogeneity into the self-evaluation process and propose the heterogeneous self-learning dynamics. When the selection intensity is weak, we analytically derive that the final outcomes of the heterogeneous dynamics can be obtained by combining the outcomes of all the corresponding homogeneous dynamics in well-mixed populations. Meanwhile, a simple condition is found which tells whether one behavior will be more abundant than the other in the long run. All of our analytical results are verified by simulations. Our work thus reveals some interesting characteristics of heterogeneous self-learning dynamics.

AB - How to achieve full cooperation among large numbers of individuals is essential for both artificial and biological systems. Learning rules (or updating rules), which specify how individuals change their behavior over time, are vital to probe this problem. Here, we incorporate individual heterogeneity into the self-evaluation process and propose the heterogeneous self-learning dynamics. When the selection intensity is weak, we analytically derive that the final outcomes of the heterogeneous dynamics can be obtained by combining the outcomes of all the corresponding homogeneous dynamics in well-mixed populations. Meanwhile, a simple condition is found which tells whether one behavior will be more abundant than the other in the long run. All of our analytical results are verified by simulations. Our work thus reveals some interesting characteristics of heterogeneous self-learning dynamics.

KW - Cooperation

KW - Evolutionary dynamics

KW - Game theory

KW - Self-learning rules

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Zhou L, Wu B, Vasconcelos VV, Wang L. Evolutionary game gynamics driven by heterogeneous self-learning rules. 在 Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018. Institute of Electrical and Electronics Engineers Inc. 2018. 页码 825-829. 8515931. (Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018). doi: 10.1109/DDCLS.2018.8515931

Evolutionary game gynamics driven by heterogeneous self-learning rules

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