FCM-based Scenario Development

The expert panels were used to develop the FCM-based scenarios for wind

energy. Members of the FCM EP were asked to provide their causal maps and

highlight the factors that may affect deployment of wind energy on a large scale

in Pakistan. Miles and Keenan recommend that the researcher should provide

background material to the participants of a scenario workshop so that they have

similar background information [211]. Therefore, introductory information was

provided to the experts so that they could understand the context of the research

along with instructions to develop causal maps. The agenda and the handout

documents of the FCM scenario workshop are attached as Appendix A to this

dissertation. The handout highlights the purpose of the workshop, focus

questions, detailed instructions for the construction of causal maps, and

examples of FCMs for the experts. The agenda and the handout documents

were provided to the participants four weeks prior to the workshop.

Individual FCMs were obtained from the FCM EP members prior to the

scenario workshop. The experts were asked to look into the social, technological,

economic, environmental, and political aspects while identifying the concepts

individual maps were combined into an integrated FCM by the researcher. In the

workshop, the purpose of the research, the basic principles of scenario planning,

and FCM-based scenarios were also explained to the members of the expert

panel. During the workshop, the participants reviewed and critiqued the

integrated FCM and highlighted the input vectors for creating FCM-based

scenarios.

For the development of the FCM, the following steps recommended in the

literature were followed [152, 201, 334]:

1. Identify and define the important factors:

a. Write down issues on Post-its; and

b. Cluster these issues as a map and discuss their importance.

2. Define the causal link between these factors:

a. Identify factors which are linked together;

b. Determine that the relationships is positive or negative; and

c. Define relative strength of the relationships by assigning causal

weights using a 5-point Likert-type scale, with values that range

from 1, representing a very weak causal link, to 5, representing a

very strong causal link.

3. Review and discuss the combined / integrated FCM:

a. The moderator individually obtains the FCMs from every expert

prior to the workshop and combines those into an integrated FCM.

a. Paste red dots on the most uncertain factors (5 red dots are

provided to every expert).

5. Identify plausible input vectors consisting of the most important factors

from the integrated FCM:

a. The moderator will provide a tabular worksheet highlighting the

critical scenario drivers at the top of the each column and indicating

the number of conceivable development variations of each scenario

driver; and

b. Combine the development variations into plausible strands (input

vectors) using markers of different colors.

Combining of Multiple FCMs

Multiple FCMs can be combined together to produce a joint effect and capture

the opinions of multiple experts together in one map for further analysis [163].

The combined FCM is considered more useful than an individual FCM because

the information is obtained from a multiplicity of sources [324]. After combining

the FCMs, the experts are asked again to review the integrated FCM and

highlight the most uncertain factors/concepts.

An example of combing multiple FCMs is shown in Figure 10, developed for a

pilot study project [11]. The central objective of this integrated FCM is to

investigate the factors that will cause the large scale deployment of wind energy

FCMs from seven experts and taking the average of the causal weights. The

concepts highlighted by a continuous boundary line are identified by all of the

experts (from concept 1 to concept 14 and concept 16), whereas the remaining

concepts highlighted by a dotted line are identified by two experts.

Taber and Siegel proposed a method for combining multiple FCMs. This

method computes the expert credibility weights based on the Hamming distance

between the inferences vectors obtained from the FCMs of various experts [324,

325]. The integrated FCM shown in Figure 10 is composed of 20 concepts,

where 15 concepts are identified by all of the experts and two experts identified

five additional concepts highlighted by a dotted line. It was found that the

credibility weight of the two experts who identified additional concepts is reduced

because they differed from the majority. The credibility weight of the five experts,

who proposed the same concepts, is 0.90. Whereas, the credibility weight of the

two experts who identified additional important concepts in their FCMs is 0.75.

Thus, this method estimates a lesser expert credibility weight for those experts

who differ and disagree from the majority. For scenario planning, it is critical to

collect diverse input from multiple experts and identify the weak signals that have

the potential to play a vital role in the future. Therefore, this approach is not a

suitable approach for combining FCMs for scenario planning.

In the other method, multiple FCMs are combined by taking the average of

the causal weights. This is another commonly employed technique and it has

152]. Both approaches presented in the literature for combining multiple FCMs

are explained in Appendix B.

Figure 10: Integrated causal map/FCM for deployment of wind energy

After forming the integrated FCM, the experts were asked to identify the most

uncertain and important scenario drivers (concepts), because it is important to

ranking the scenario drivers, the morphological analysis was used to develop the

input vectors.

FCM Simulation

The input vectors are used for conducting the simulation and generating the

FCM-based scenarios. Although, a few important scenario drivers are used to

form the input vectors; but in the FCM simulation all of the scenario drivers

(concepts) in the FCM model are considered for generating scenarios. It happens

because when a concept changes its state, it affects all concepts that are

causally dependent on it, and this process depends on the direction and strength

of the causal link [152]. The newly activated concepts may further influence other

concepts which they causally affect and this activation spreads in a non-linear

fashion in the FCM model until the system attains a stable state [152]. Due to the

meta-rules, it is also possible that in some cases several input vectors may lead

to the same final system state [156]. The FCM simulations can be used to

experiment with different input vectors and compare their outcome [156].

Therefore, it helps to deal with a complex situation and holistically evaluate all

concepts of interest. Moreover, despite using the input vectors consisting of the

critical scenario drivers, all drivers/concepts and their causal links in the FCM

model are considered during the development of FCM-based scenarios.

The FCM simulation is performed until the output vector is stabilized. It is

squashing function is applied after every multiplication as a threshold function to

the output vector. A simple binary squashing function is used which squeezes the

result of multiplication in the interval of (0, 1). For n number of concepts, the input

vector is 1 by n, the FCM adjacency matrix is n by n, and the output vector is 1

by n [324]. The new output vector is again multiplied with the FCM adjacency matrix and this process is repeated until the multiplication results in equilibrium

[152, 296, 324]. As a result, the system is settled down and stabilized, and then

new matrix multiplications will result in the same output state vector. Implications

of the FCM model are analyzed by clamping different concepts and the vector

and adjacency matrix multiplication procedure, to assess the effects of these

perturbations on the state of a model [2]. Thus, the FCM simulation process

provides a holistic overview and investigates the internal dynamics of the model.

FCM Validation

The literature recommends that every step should be validated through the

experts as an ongoing activity [156, 317]. Participation of the experts help to

address the validity and acceptability aspects of the model [133]. It is important to

accurately translate experts’ feedback in the FCM model [156] and weak

facilitation may lead to poor quality of the model [109]. Therefore, it is critical to

strictly follow the FCM modeling guidelines through a high quality process [155,

156]. The steps taken to ensure validity of the FCM model developed for this