Adaptive behaviors can improve the system consilience of a network system. (February 2018)

• Record Type: Journal Article
• Authors: Hu, Xiao-Bing; Shi, Peijun; Wang, Ming; Leeson, Mark S
• Is Part Of: Adaptive behavior. Volume 26:Number 1(2018)
• Page Start: 3
• Page End: 19
• Publication Date: 2018-02
• Subjects: Adaptive behavior -- complex network -- consilience -- disturbance; Animal behavior -- Periodicals; Animals -- Adaptation -- Periodicals; Adaptability (Psychology) -- Periodicals; Adaptation, Psychological -- Periodicals; Artificial intelligence -- Periodicals; 591.5
• DOI: 10.1177/1059712317747951
• Languages: English
• ISSNs: 1741-2633

Abstract

As a recently reported network property, consilience degree (CSD) indicates how well a network system integrates its topology and node activities together to serve a specific systemic goal. As is well known, many natural and man-made systems are complex networks where, besides network topology, node activity states also play an important role in determining system performance. For example, a collaborative project involving friends is more likely to succeed than one involving enemies, even though the topology of network organization is the same. The concept of CSD can quantitatively distinguish the difference between the involvement of friends and the involvement of enemies. This article reports a simulation study on the adaptive behaviors of nodes based on the selfish rule and the following-others rule, and the simulation results show that based on such adaptive behaviors of nodes, a network system will automatically evolve to a high level of system consilience. The simulation study also demonstrates that a high level of system consilience resulting from adaptive behaviors will contribute to increased system resistance to external disturbances. The generality of adaptive behaviors in reality implies that CSD is an inherent attribute of real-world network systems, and therefore, the concept of CSD has significant application potential in the study of adaptive behaviors in network systems.