Causal Loop Diagram

Causal loop diagrams map the causal relationships between pairs of elements within a system and identify feedback loops. These loops can either be reinforcing (vicious cycle) or balancing (goal-seeking) and complex interactions between loops can lead to unintended consequences.

The arrows in the diagram describe the directions of effect. A causal link from one element (A) to another (B) is positive (+) when a change in A leads to change in B in the same direction; an increase in A leads to an increase to B and a decrease in A leads to a decrease in B. Conversely, a causal link from A to B is negative (-) when a change in A leads to a change in B in the opposite direction. Figure 1 describes dynamic factors affecting dispensing errors in a pharmacy setting. One reinforcing loop in red reveals that higher schedule pressure increases the number of dispensing errors, which leads to more rework to be done and schedule pressure eventually spirals up. On the other hand, a balancing loop on the left-hand side in blue reveals that schedule pressure can increase productivity, which then leads to less work to be done and decreased schedule pressure.

CLP-pharmacy
Figure 1 Causal Loop Diagram of Dispensing Errors

By understanding dynamic interactions between loops, causal loop diagrams allow us to have a more in-depth understanding of interdependencies underpinning a control structure and impact of changes.

Causal loop diagrams can never be comprehensive and are also never final, but always provisional. The diagrams evolve as analyst’s understanding improves and the purpose of modelling effort evolves.

Causal loop diagrams can be used as part of System Dynamics modelling and simulation approach, which additionally use stock flow diagrams to quantitatively model and simulate system’s dynamic behaviour. The simulation allows various scenarios to be tested.

Causal loop diagrams are conceptually simple, but not easy to apply and use without experience/support. Causal loop diagrams have been used to evaluate unintended effects of policies in various domains, e.g. public health, patient safety, mining, military accident and construction safety. Please email g.jun@lboro.ac.uk, if you want the review article below.

Reference

safetysciencepaper

SD to pharmacy

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Tools
  1. Loopy: A Simple Tool for Thinking in Systems
  2. Kumu: A Complex Mapping Tool

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