Systems Thinking Method

Barry Richmond's 7 thinking skills in Systems Thinking

Barry Richmond's 7 thinking skills in Systems Thinking
There are several steps involved in applying systems thinking methodically.
The systems thinking method begins with specifying the problem situation
We proceed to build a conceptual then computational model to represent the structure we believe caused the situation, together with potential interventions.
We then test the hypothesis by running virtual experiments and iteratively modifying the model based on the test results.
Next we act to implement the changes to improve the situation.
This involves communicating the understanding gained from building and testing the model in order to produce effective collective action.
We then repeat the improvement or understanding cycle, moving to the next issue, problem situation or design stage
Thinking systemically is hard because it requires many different skills that require practice. We now define these skills and where they are used in the systems thinking method steps.
Dynamic thinking is framing a problem in terms of a pattern of behaviour over time.
System as Cause or Endogenous Thinking is seeing internal actors who manage the policies and "plumbing" of the system as responsible for a behaviour.

Forest thinking or Pattern oriented thinking is seeing beyond the details to the context of relationships in which they are embedded.
Operational or mechanistic thinking is understanding how a behaviour is actually generated.
Closed loop thinking is viewing causality as an ongoing process, not a one-time event, with effects feeding back to influence causes, and causes affecting each other.
Quantitative thinking is knowing how to quantify, though you can't always measure.
Scientific thinking is knowing how to define testable hypotheses.
We now contrast these systems thinking skills with traditional ways of thinking
Static thinking is focussing on particular events.
Dynamic thinking is framing a problem in terms of a pattern of behaviour over time.
Exogenous thinking is considering a system's behaviour as being driven by external forces
System as Cause or Endogenous Thinking is seeing internal actors who manage the policies and "plumbing" of the system as responsible for a behaviour.
Tree by tree thinking is focussing on the details in order to "Know".
Forest thinking or Pattern oriented thinking is seeing beyond the details to the context of relationships in which they are embedded.
Factors thinking is listing factors that influence or are correlated with a behaviour.
Operational or mechanistic thinking is understanding how a behaviour is actually generated.
Linear or straight line thinking is viewing causality as running one way, with each cause independent of other causes
Closed loop thinking is viewing causality as an ongoing process, not a one-time event, with effects feeding back to influence causes, and causes affecting each other.
Measurement thinking is focussing on achieving a high degree of numerical precision.
Quantitative thinking is knowing how to quantify, though you can't always measure.
Proving truth thinking is seeking to prove models true by validating them with historical data.
Scientific thinking is knowing how to define testable hypotheses.
We now provide some examples and practice tips for each systems thinking skill
Dynamic thinking is framing a problem in terms of a pattern of behaviour over time.
Dynamic thinking example: A marketing manager notices that sales rise after each sales promotion but then fall to a lower and lower level each time, till now at an all-time low. She knows this can be improved, but is preparing for things to get worse before getting better. Fixing the problem so it stays fixed could take time.
Dynamic thinking practice tip: 
Construct behaviour over time graphs.
Think of events as interesting points in a variable's overall trajectory over time.
System as Cause or Endogenous Thinking is seeing internal actors who manage the policies and "plumbing" of the system as responsible for a behaviour.
System as Cause Or Endogenous Thinking  example: A sales manager understands the value of the relationship between client operations managers and his account executives. He allocates specific time to this relationship. The combination of quality of relationship, price and innovation of services powerfully shapes the company's future.
System as Cause Thinking  practice tip:
Instead of blaming, ask "How could those within the system be responsible?"
Or "What could those within the system have done to make it more resilient to external shocks?" 
Forest thinking or Pattern oriented thinking is seeing beyond the details to the context of relationships in which they are embedded.
Forest (or relationship pattern) thinking  example: During the busy season, several subway trains break down. Rather than just fixing the trains and apologizing, a senior official takes a wider view of the relationship between the average age of carriages and the likelihood of breakdown. He then introduces a process to monitor the age of equipment and initiate maintenance and new investments to minimise breakdowns. 
Forest thinking practice tip:
Focus on similarities rather than differences.
Operational or mechanistic thinking is understanding how a behaviour is actually generated.
Generative mechanism or Operational thinking example: A senior executive of a rapidly growing IT company notices there is an unusually high ratio of human resources staff to company head count, compared with industry benchmarks.
Her simple generative mechanistic model reveals that demand for human resources services actually depends on the rate at which the company's head count is expanding, so her company's ratio is perfectly appropriate.

Generative mechanism practice tip:
Ask "What is the true nature of a process?" rather than
"What are all the factors that influence the process?"
Closed loop thinking is viewing causality as an ongoing process, not a one-time event, with effects feeding back to influence causes, and causes affecting each other.
Closed loop thinking  example: A human resources manager realises that downsizing reduces costs and therefore raises profits in the short term. However in the long run, the policy feeds back on itself in an undesirable way. Downsizing also decreases morale and therefore productivity, which raises costs, lowers profits and triggers yet another round of staff reductions.
Closed loop thinking practice tip:
Take a laundry list of dot points and try to understand how the items on it might influence each other.
Quantitative thinking is knowing how to quantify, though you can't always measure.
Quantitative thinking  example: By quantifying self-esteem researchers generate new insights into treatment interventions for alcoholics.


Quantitative thinking practice tip:
Ask what key "soft" variables have been left out of analyses, and ruminate about the implications of including them in your model.
Scientific thinking is knowing how to define testable hypotheses.
Scientific thinking  example: A model builder tests face validity by determining whether the model's structure matches the reality that the model is intended to represent. She tests robustness by subjecting the model to extreme conditions, and observing whether it breaks down in the same way the real system would break down under similar conditions.
Scientific thinking practice tip:
"Shock" a computer model by drastically changing the values of certain variables, to see how the model holds up.
Can you think of practical ways to move from traditional to systems thinking skills in your own area of application?

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