31247 Chap.10 Fuzzy Cognitive Maps and Network Analysis
Fuzzy Cognitive Maps and Network Analysis
Adding strength to causes
A fuzzy cognitive map is a causal map whose links carry weights. Its factors need not be measurable, so it can capture what stakeholders know without data, and it is used to explore how a change spreads through a system.
Mapping relationships
Social network analysis treats people or organisations as nodes and their relationships as edges.
Out-degree, in-degree and betweenness show who sends, who receives, and who acts as a bridge.
Building a map stakeholders trust
An FCM is built in three steps: list the factors, construct the map with the direction, sign and strength of each link, and then interpret the analysis with stakeholders. The last step matters most.
Stakeholders are asked whether the results match their understanding, and they may keep or revise what they believe about the structure.
Networks in a project
The Week 6 reading builds a contact network from public health records and finds that only a few people are highly influential.
In a business process project the same measures show who passes information to whom, and the bridges are often the best place to intervene.
What this chapter covers
- 01
Semi-quantitative modelling
- 02
Factors and weighted edges
- 03
Three steps to build an FCM
- 04
Interpreting an FCM with stakeholders
- 05
Nodes and edges in a network
- 06
Out-degree and in-degree
- 07
Betweenness and bridges
Worked example · free
Read the weights in a late-semester stress map
- 1Assessment load raises stress, and at 0.7 it is the strongest single influence on stress in the map.
- 1Sleep and peer support both lower stress, with sleep (0.6) judged stronger than peer support (0.4).
- 1Stress lowers study quality, with a weight of 0.5.
- 1So the map suggests that protecting sleep would do more for study quality than adding peer support, if the team’s weights are right.
Key terms
- Edge Weight
- The number on a link in a fuzzy cognitive map that shows how strong the influence is.
- Out-Degree
- How many links leave a node and point at other nodes.
- In-Degree
- How many links arrive at a node from elsewhere in the network.
Fuzzy Cognitive Maps and Network Analysis FAQ
What makes a fuzzy cognitive map semi-quantitative?
Its links carry numerical weights, but its factors do not have to be measurable or backed by data. That lets it combine qualitative knowledge with some quantitative information, which is how the Week 6 lecture describes semi-quantitative methods.
What does betweenness centrality show?
It shows which nodes act as bridges, lying on the shortest paths between other nodes. A person with high betweenness may not start or receive the most connections, but removing them can cut parts of the network off from each other.
Which tool is used for fuzzy cognitive maps?
The lecture and tutorial use Mental Modeler. The Week 6 tutorial asks you to build a map of how drought and rising energy prices affect a farm.
What do in-degree and out-degree measure?
Out-degree counts the edges leaving a node, such as how many people one person passed something to. In-degree counts the edges arriving at a node. In the contact-tracing reading, an out-degree of zero means a person was not a source, and an in-degree of zero means no source was identified for them.
How do stakeholders help interpret a fuzzy cognitive map?
They are asked whether the analysis matches their understanding of the causal effects, whether it makes sense, and whether they have an intuition about why a result happens. A surprising result is then either a flaw in the map or a genuine insight.
Do the factors in a fuzzy cognitive map need data?
No. A factor only needs to be something that can go up or down; it need not be measurable or supported by data. That is what lets the method capture what stakeholders know when evidence is scarce.
Assessment move
Pick five variables from your causal loop diagram and give each link a weight between −1 and 1, then ask a teammate to weight them independently and compare. For networks, sketch who passes information to whom in your project and compute out-degree, in-degree and betweenness by hand. Ask Sia to check one betweenness calculation with you before you trust the rest.
Keep both sets of weights in your log to show how the team reached agreement.
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