TY - JOUR
T1 - Towards Quantitatively Understanding the Complexity of Social-Ecological Systems—From Connection to Consilience
AU - Hu, Xiao Bing
AU - Shi, Peijun
AU - Wang, Ming
AU - Ye, Tao
AU - Leeson, Mark S.
AU - Van Der Leeuw, Sander
AU - Wu, Jianguo
AU - Renn, Ortwin
AU - Jaeger, Carlo
N1 - Publisher Copyright:
© 2017, The Author(s).
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2017/12/1
Y1 - 2017/12/1
N2 - The complexity of social-ecological systems (SES) is rooted in the outcomes of node activities connected by network topology. Thus far, in network dynamics research, the connectivity degree (CND), indicating how many nodes are connected to a given node, has been the dominant concept. However, connectivity focuses only on network topology, neglecting the crucial relation to node activities, and thereby leaving system outcomes largely unexplained. Inspired by the phenomenon of “consensus of wills and coordination of activities” often observed in disaster risk management, we propose a new concept of network characteristic, the consilience degree (CSD), aiming to measure the way in which network topology and node activities together contribute to system outcomes. The CSD captures the fact that nodes may assume different states that make their activities more or less compatible. Connecting two nodes with in/compatible states will lead to outcomes that are un/desirable from the perspective of the SES in question. We mathematically prove that the CSD is a generalized CND, and the CND is a special case of CSD. As a general, fundamental concept, the CSD can facilitate the development of a new framework of network properties, models, and theories that allows us to understand patterns of network behavior that cannot be explained in terms of connectivity alone. We further demonstrate that a co-evolutionary mechanism can naturally improve the CSD. Given the generality of co-evolution in SES, we argue that the CSD is an inherent attribute rather than an artificial concept, which underpins the fundamental importance of the CSD to the study of SES.
AB - The complexity of social-ecological systems (SES) is rooted in the outcomes of node activities connected by network topology. Thus far, in network dynamics research, the connectivity degree (CND), indicating how many nodes are connected to a given node, has been the dominant concept. However, connectivity focuses only on network topology, neglecting the crucial relation to node activities, and thereby leaving system outcomes largely unexplained. Inspired by the phenomenon of “consensus of wills and coordination of activities” often observed in disaster risk management, we propose a new concept of network characteristic, the consilience degree (CSD), aiming to measure the way in which network topology and node activities together contribute to system outcomes. The CSD captures the fact that nodes may assume different states that make their activities more or less compatible. Connecting two nodes with in/compatible states will lead to outcomes that are un/desirable from the perspective of the SES in question. We mathematically prove that the CSD is a generalized CND, and the CND is a special case of CSD. As a general, fundamental concept, the CSD can facilitate the development of a new framework of network properties, models, and theories that allows us to understand patterns of network behavior that cannot be explained in terms of connectivity alone. We further demonstrate that a co-evolutionary mechanism can naturally improve the CSD. Given the generality of co-evolution in SES, we argue that the CSD is an inherent attribute rather than an artificial concept, which underpins the fundamental importance of the CSD to the study of SES.
KW - Co-evolution
KW - Complex networks
KW - Consilience degree
KW - Disaster risk reduction
KW - Social-ecological systems
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U2 - 10.1007/s13753-017-0146-5
DO - 10.1007/s13753-017-0146-5
M3 - Article
AN - SCOPUS:85038887934
VL - 8
SP - 343
EP - 356
JO - International Journal of Disaster Risk Science
JF - International Journal of Disaster Risk Science
SN - 2095-0055
IS - 4
ER -