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^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	825-829
Number of pages	5
ISBN (Electronic)	9781538626184
DOIs	https://doi.org/10.1109/DDCLS.2018.8515931
Publication status	Published - 30 Oct 2018
Externally published	Yes
Event	7th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2018 - Enshi, Hubei Province, China Duration: 25 May 2018 → 27 May 2018

Publication series

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

Conference

Conference	7th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2018
Country/Territory	China
City	Enshi, Hubei Province
Period	25/05/18 → 27/05/18

Keywords

Cooperation
Evolutionary dynamics
Game theory
Self-learning rules

Access to Document

10.1109/DDCLS.2018.8515931

Cite this

Zhou, L., Wu, B., Vasconcelos, V. V., & Wang, L. (2018). Evolutionary game gynamics driven by heterogeneous self-learning rules. In Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018 (pp. 825-829). Article 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. et al. / 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. pp. 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",

month = oct,

day = "30",

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.",

pages = "825--829",

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

address = "United States",

}

Zhou, L, Wu, B, Vasconcelos, VV & Wang, L 2018, Evolutionary game gynamics driven by heterogeneous self-learning rules. in 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., pp. 825-829, 7th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2018, Enshi, Hubei Province, China, 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. et al.
Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 825-829 8515931 (Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Evolutionary game gynamics driven by heterogeneous self-learning rules

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

UR - http://www.scopus.com/inward/record.url?scp=85056994351&partnerID=8YFLogxK

U2 - 10.1109/DDCLS.2018.8515931

DO - 10.1109/DDCLS.2018.8515931

M3 - Conference contribution

AN - SCOPUS:85056994351

T3 - Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018

SP - 825

EP - 829

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

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 7th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2018

Y2 - 25 May 2018 through 27 May 2018

ER -

Zhou L, Wu B, Vasconcelos VV, Wang L. Evolutionary game gynamics driven by heterogeneous self-learning rules. In Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 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

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this