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Modeling Risk Allocation in Privately Financed Infrastructure Projects Using Fuzzy Logic
Author(s) -
Jin XiaoHua,
Doloi Hemanta
Publication year - 2009
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/j.1467-8667.2009.00609.x
Subject(s) - transaction cost , fuzzy logic , process (computing) , resource allocation , risk analysis (engineering) , computer science , inference , fuzzy inference system , risk management , resource (disambiguation) , management science , knowledge management , operations research , process management , business , engineering , fuzzy control system , artificial intelligence , finance , computer network , adaptive neuro fuzzy inference system , operating system
Risk allocation (RA) plays a critical role in privately financed infrastructure projects. Project performance is contingent on whether the adopted RA strategy is efficient. However, no mechanism was specifically designed to facilitate the risk allocation decision‐making (RADM) process. Two theoretical frameworks based on the transaction cost economics (TCE) theory and on both the TCE and the resource‐based view (RBV) of organizational capability, respectively, were thus adopted in this article. As conventional modeling techniques are not suitable for modeling RADM processes, which involve ambiguous and qualitative information, fuzzy inference systems (FISs) were developed, illustrated, and evaluated to model these frameworks. An industry‐wide survey and rounds of expert consultation were conducted to collect data and generate fuzzy rules. It was found that both FISs are capable of reliably explaining the RADM process. In particular, the FIS based on both the TCE and the RBV theories performed more accurately and thus is more suitable for forecasting efficient risk allocation strategy.