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TB871: Cognitive mapping, causal-loop diagramming, and a refreshing use of SODA

Note: this is a post reflecting on one of the modules of my MSc in Systems Thinking in Practice. You can see all of the related posts in this category


In a previous post I gave an overview and introduction to Strategic Options Development and Analysis (SODA). This approach uses cognitive maps, as distinct from causal-loop diagrams. In this post, I’m going to outline the differences between them.

Differences between causal-loop diagrams and cognitive maps

Causal-loop diagrams model the causal interconnections within a situation. This kind of diagramming tool for illustrating how different elements of a system influence each other. The primary questions it addresses are “what is happening?” and “how does it happen?” You can see an example of this kind of diagram in this post about my situation of interest.

Cognitive maps focus on capturing how individuals or groups think about an issue. While causal-loop diagrams are, of course, subjective they are presented in an objective way. This is not the case with cognitive mapping, which clearly represent thoughts and perspectives of the people involved, laying them out in a structured way.

Cognitive mapping is less constrained in the use of text than causal-loop diagrams, with the aim (as much as possible) to capture the actual words of participants. These kinds of maps aim to answer “what do we think is happening?” and “how might we make this happen?” Cognitive maps capture subjective perspectives, often facilitated by an external person who helps articulate the thoughts of others. They are used to explore possible scenarios rather than model actual situations.

Creating Cognitive Maps with SODA

The SODA approach offers a structured method for constructing cognitive maps. My system of interest is “a system to promote lifelong learning” in a library context. Here’s how we might approach that, remembering that the ellipses (three dots) connect the construct with its opposite:

  • Identify a key decision: begin by identifying a critical decision, such as Enhancing lifelong learning opportunities.
  • Seed construct: develop a seed construct like “increase access to lifelong learning resources … maintain current resource levels.”
  • Hierarchy of planning: organise the constructs into three levels: goals (ideals), strategic directions (objectives), and potential options (tasks), placing the seed construct towards the top of the strategic options.
  • Develop constructs: generate related constructs such as:
    • “expand digital resource collections … maintain existing collections”
    • “offer more educational programmes … keep current programme offerings”
    • “improve user support services … maintain current support levels”
  • Link constructs: draw connections between constructs using arrows to show relationships — e.g. “expand digital resource collections” might link to “increase access to lifelong learning resources” with a positive arrow, while “limited funding … secure additional funding” might link negatively.
  • Iterative refinement: refine the map continuously by:
    • adding new constructs and links as the understanding of the problem evolves
    • grouping related constructs
    • removing less useful ones to maintain clarity

SODA map of my system of interest

I’ve had a go at creating a SODA map below. Bearing in mind that the following is the first of these I’ve ever created, I think it’s turned out OK:

Cognitive map to show 'System to improve lifelong learning' in a library context using the SODA approach

Further thoughts

I can imagine this being useful in a user research setting, as it allows you to take a central concept and then ‘ladder down’ to potential options and then ‘ladder up’ towards broader goals. Allowing the user research participant to see the map could help them explore the conversation more visually, going back to concepts and see dependencies.

I’ve seen this kind of thing in action in a different context when working with Bryan Mathers when we’ve done thinkathons together. He would draw out conversations, for example using a Defining the Cast activity and we’d all be able to refer to things that were said earlier in our time together.

TB871: Mapping my situation of interest

Note: this is a post reflecting on one of the modules of my MSc in Systems Thinking in Practice. You can see all of the related posts in this category


Activity 2.1 asks us to look at a situation of interest and identify:

  • the problem being fixed
  • the action taken to fix it
  • the immediate effect
  • the longer-term effect.

I’m still a beginner with causal loop diagrams, but I’ve tried to map some of the issues in my situation of interest using kumu:

A causal loop diagram focused on 'Number of library visitors' with positive and negative causal arrows between things such as 'Quality of provision' and 'Demographic changes'

If one number goes up or something increases, and the thing linked to it goes up or increases as a result, then it is said to ‘add to’ or be in the ‘same direction’. In the above diagram these are indicated by blue lines. Meanwhile, if the opposite happens then it ‘subtracts from’ or is in the ‘opposite direction’. This is indicated by red lines.

For example, if more e-books are available, then (one could argue that) physical book borrowing goes down. So that arrow is red. And if the quality of provision goes up, then one might argue, in the absence of data, that the number of library visitors would go up. The slightly confusing thing, for me at least, is that the plus and negative symbols refer to the causal relationship rather than whether the impact is positive or negative. This can be shown by thinking of the quality of provision going down, for example, leading to the number of library visitors being reduced.

From my diagram, it’s evident that:

  • The aim is to improve the number of people visiting the library
  • Actions taken to fix this include holding community events
  • The immediate effect is an increase in the number of people visiting the library from various demographic groups
  • The longer-term effect is fewer people visiting the library for quiet study due to the amount of noise and the availability of the study space

EDIT: I’m adding in the diagram below from Section 2.2.2 of the module because it’s helpful:

Two boxes: 'Case 1' and 'Case 2'. Both point to separate examples of A->B but with Case 1 having a + next to it and Case 2 having a - next to it.

The boxes next to each example explain that in Case 1 *either* a larger amount of A leads to a larger amount of B, *or* a smaller amount of A leads to a smaller amount of B.

With Case 2, it's the other way around. So, *either* a larger amount of A leads to a smaller amount of B, *or* a smaller amount of A leads to a larger amount of B.

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