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Environmental risk, cooperation, and communication complexity

Andras, Peter; Roberts, Gilbert; Lazarus, John

Authors

Profile image of Peter Andras

Prof Peter Andras P.Andras@napier.ac.uk
Dean of School of Computing Engineering and the Built Environment

Gilbert Roberts

John Lazarus



Contributors

Eduardo Alonso
Editor

Daniel Kudenko
Editor

Dimitar Kazakov
Editor

Abstract

The evolution of cooperation and communication in communities of individuals is a puzzling problem for a wide range of scientific disciplines, ranging from evolutionary theory to the theory and application of multi-agent systems. A key issue is to understand the factors that affect collaboration and communication evolution. To address this problem, here we choose the environmental risk as a compact descriptor of the environment in a model world of simple agents. We analyse the evolution of cooperation and communication as a function of the environmental risk. Our findings show that collaboration is more likely to rise to high levels within the agent society in a world characterised by high risk than in one characterised by low risk. With respect to the evolution of communication, we found that communities of agents with high levels of collaboration are more likely to use less complex communication than those which show lower levels of collaboration. Our results have important implications for understanding the evolution of both cooperation and communication, and the interrelationships between them.

Citation

Andras, P., Roberts, G., & Lazarus, J. (2003). Environmental risk, cooperation, and communication complexity. In E. Alonso, D. Kudenko, & D. Kazakov (Eds.), Adaptive Agents and Multi-Agent Systems: Adaptation and Multi-Agent Learning (49-65). Springer. https://doi.org/10.1007/3-540-44826-8_4

Online Publication Date May 13, 2003
Publication Date 2003
Deposit Date Nov 8, 2021
Publisher Springer
Pages 49-65
Series Title Lecture Notes in Computer Science
Series Number 2636
Series ISSN 0302-9743
Book Title Adaptive Agents and Multi-Agent Systems: Adaptation and Multi-Agent Learning
ISBN 978-3-540-40068-4
DOI https://doi.org/10.1007/3-540-44826-8_4
Public URL http://researchrepository.napier.ac.uk/Output/2809335