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Title Modelling vicious networks with P-graph causality maps
ID_Doc 6772
Authors Tan, RDR; Aviso, KB; Lao, ANR; Promentilla, MAB
Title Modelling vicious networks with P-graph causality maps
Year 2022
Published Clean Technologies And Environmental Policy, 24, 1
DOI 10.1007/s10098-021-02096-x
Abstract P-graph causality maps were recently proposed as a methodology for systematic analysis of intertwined causal chains forming network-like structures. This approach uses the bipartite representation of P-graph to distinguish system components ("objects" represented by O-type nodes) from the functions they perform ("mechanisms" represented by M-type nodes). The P-graph causality map methodology was originally applied for determining structurally feasible causal networks to enable a desirable outcome to be achieved. In this work, the P-graph causality map methodology is extended to the analysis of vicious networks (i.e., causal networks with adverse outcomes). The maximal structure generation algorithm is first used to assemble the problem elements into a complete causal network; the solution structure generation algorithm is then used to enumerate all structurally feasible causal networks. Such comprehensive analysis gives insights on how to deactivate vicious networks through the removal of keystone objects and mechanisms. The extended methodology is illustrated with an ex post analysis of the 1984 Bhopal industrial disaster. Prospects for other applications to sustainability issues are also discussed.
Author Keywords Causal chain; Vicious cycle; Fuzzy cognitive maps; Process network synthesis; Industrial accidents; Circular economy
Index Keywords Index Keywords
Document Type Other
Open Access Open Access
Source Science Citation Index Expanded (SCI-EXPANDED)
EID WOS:000648839800001
WoS Category Green & Sustainable Science & Technology; Engineering, Environmental; Environmental Sciences
Research Area Science & Technology - Other Topics; Engineering; Environmental Sciences & Ecology
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