Keywords
Exploratory Modeling, FCM, Particpatory Modeling
Start Date
26-6-2018 9:00 AM
End Date
26-6-2018 10:20 AM
Abstract
This work combines qualitative text analysis, participatory modeling with Fuzzy Cognitive Maps (FCM) with Exploratory Modelling and Analysis (EMA). FCM is suitable for modeling in data-poor environments when the system under study is not well described in quantitative terms. A case in point is safety culture, which describes the values, routines, and work processes that allow an organization to prevent disasters by avoiding and quickly bouncing back from mistakes. The concept is particularly relevant in oil and gas industry, where initially small errors have potentially devastating environmental, social, and economic impacts. In this setting relevant quantitative data on safety culture is virtually non-existent: when nothing happens, nothing is reported and when an accident happens and is reported, the report provides no direct information about the culture. Accordingly, our project creates a system model of safety culture based on published (mainly qualitative) research and expert inputs. EMA, on the other hand, is used to address uncertainty about the model structure, such as a lack of knowledge on how to quantify causal links. Rather than synthesizing knowledge into one model, EMA constructs an ensemble of plausible models and explore their impacts. Our work integrates the two approaches and demonstrates the results with data on oil and gas safety. In this paper, we discuss the general approach and present data on the first step: creating an FCM model of safety culture based on qualitative analysis. We present the use of thematic analysis and t-coefficient.
Exploratory participatory modeling with FCM to overcome uncertainty: Improving safety culture in oil and gas operations
This work combines qualitative text analysis, participatory modeling with Fuzzy Cognitive Maps (FCM) with Exploratory Modelling and Analysis (EMA). FCM is suitable for modeling in data-poor environments when the system under study is not well described in quantitative terms. A case in point is safety culture, which describes the values, routines, and work processes that allow an organization to prevent disasters by avoiding and quickly bouncing back from mistakes. The concept is particularly relevant in oil and gas industry, where initially small errors have potentially devastating environmental, social, and economic impacts. In this setting relevant quantitative data on safety culture is virtually non-existent: when nothing happens, nothing is reported and when an accident happens and is reported, the report provides no direct information about the culture. Accordingly, our project creates a system model of safety culture based on published (mainly qualitative) research and expert inputs. EMA, on the other hand, is used to address uncertainty about the model structure, such as a lack of knowledge on how to quantify causal links. Rather than synthesizing knowledge into one model, EMA constructs an ensemble of plausible models and explore their impacts. Our work integrates the two approaches and demonstrates the results with data on oil and gas safety. In this paper, we discuss the general approach and present data on the first step: creating an FCM model of safety culture based on qualitative analysis. We present the use of thematic analysis and t-coefficient.
Stream and Session
C5 Participatory Modeling 2.0