# Fuzzy-Based Multivariate Analysis for Input Modeling of Risk Assessment in Wind Farm Projects
## 2 Literature Review
### MCS for Risk Assessment and Input Modeling
Examples of cognitive biases include overconfidence (i.e., excessive confidence in one's own answers to questions), anchoring (i.e., the tendency to rely too heavily on one piece of information when making decisions), and availability bias (i.e., the tendency to overestimate the likelihood of events with greater availability in memory). Various debiasing methods, such as decomposition, multiple experts, and exploration of the extremes of a target variable, can be applied to reduce cognitive biases [28]. Motivational biases are defined as \"those in which judgments are influenced by the desirability or undesirability of events, consequences, outcomes, or choices\" [30]. One example of motivational bias is the underestimation of project costs to provide more competitive bids. A strategy for overcoming motivational biases, proposed by [PERSON] and [PERSON] [30], is the decomposition of a target variable into component variables and events (i.e., root causes). It is expected that the evaluation of a specific root cause by an expert will be more precise than the evaluation of a risk factor as a whole. Figure 2: Monte Carlo (MC) simulation experiment for risk assessment. As such, analysts are encouraged to adopt a decomposition strategy when eliciting subjective risk evaluations from experts. While recommendations for successfully inputting uniform, triangular, or PERT distributions into Monte Carlo risk assessment models have been proposed in literature, other forms of input modeling remain limited by a need for a large number of experts or by an inability to integrate their detailed knowledge [9; 10]. The fitting of expert opinion regarding the impact and probability of occurrence of risks into a probability distribution has been proposed by [PERSON] et al. [31]. However, their approach requires a large number of experts to be involved in the assessment, which is not feasible for novel projects, such as onshore wind farms, where the number of experts at a construction company is limited. Furthermore, their approach does not allow experts to express their detailed knowledge about a risk factor, potentially limiting the accuracy and representativeness of the risk assessment results. In 2003, [PERSON] et al. [24] proposed a methodology to enhance the input modeling of Monte Carlo risk assessment using belief networks to estimate the boundaries (i.e., minimum and maximum) of Beta-Pert distributions for activity durations. In their method, they assumed that, while experts can estimate the most-likely activity duration, the boundaries are more difficult to estimate using traditional methods [24]. The Bayesian belief network was, therefore, applied to integrate the risk factors affecting activity duration when calculating the optimistic and pessimistic boundaries of Beta-Pert. However, their model did not distinguish between the variability in activity duration and the risk impact. Furthermore, their model was built using multiple questionnaires, making it difficult for construction companies to implement this approach because of the number of experts and time required to complete the questionnaires.