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SYSTEMS CONCEPTS IN ACTION

STANFORD UNIVERSITY PRESS

Contents

Acknowledgments..............................................................viiIntroduction.................................................................1About Systems, Thinking Systemically, and Being Systemic.....................16Chapter 1 Causal Loop Diagrams...............................................31Chapter 2 System Dynamics....................................................45Chapter 3 Social Network Analysis............................................60Chapter 4 Outcome Mapping....................................................75Chapter 5 Process Monitoring of Impacts......................................92Chapter 6 Strategic Assumption Surfacing and Testing.........................108Chapter 7 Strategic Area Assessment..........................................123Chapter 8 The CDE Model......................................................136Chapter 9 Assumption-Based Planning..........................................153Chapter 10 Cynefin...........................................................163Chapter 11 Solution Focus....................................................184Chapter 12 Viable System Model...............................................199Chapter 13 Cultural Historical Activity Theory...............................217Chapter 14 Soft Systems Methodology..........................................241Chapter 15 Dialectical Methods of Inquiry....................................262Chapter 16 Scenario Technique................................................273Chapter 17 Systemic Questioning..............................................284Chapter 18 Circular Dialogues................................................292Chapter 19 Critical Systems Heuristics.......................................303Index........................................................................321

Chapter One

What are the key variables in the situation that interests us?

How do they link to each other?

How do they affect each other? Does each variable have a reinforcing or dampening effect on the variables to which it is linked?

WHAT ARE CAUSAL LOOP DIAGRAMS?

Causal loop diagrams (CLDs) provide a language for articulating our understanding of dynamic, interconnected situations. They can be considered sentences that are constructed by linking together key variables and indicating the causal relationship between them. By connecting several CLDs, a coherent story can be told about a particular situation or issue.

CLDs visualize variables and their relationships over time. They permit us not only to analyze current states and relational patterns but also to make assumptions about the dynamic behavior. They allow us to look beyond individual events and to reach a higher—one might say more systemic—level of understanding, by mapping the structure that is responsible for producing recurring patterns of events over time.

CLDs are based on the concept of "feedback," which was originally developed in the 1940s as part of the emerging science of cybernetics. Feedback is the transmission or return of information, and a feedback loop is a closed sequence of causes and effects: variable X is affecting Y and Y in turn affecting X. Thus, we cannot study the link between X and Y independently of the link between Y and X. Only by studying the whole feedback loop will we be able to develop a meaningful understanding of the behavior patterns at work in such a system.

Feedback loops are the building blocks of CLDs and appear in two types:

Reinforcing or positive feedback (see Figure 1.1) refers to a situation where all the variables respond to each other in the same direction: when A goes up, B goes up as well, which leads to a further increase of A. When A goes down, B goes down as well, which leads to a further decrease of A. Change in one direction is compounded with even more change (sometimes referred to as "cumulative causation"), which can produce both growth and decline. A savings account is an example of positive feedback, as the amount of savings and the interest earned are linked in a reinforcing manner: if savings increase, they will lead to higher interest earnings, which again are added to the savings, and so forth. Contrarily, if savings decrease, the interest earned will also go down, which again affects accumulated savings.

Negative or balancing feedback (see Figure 1.2) occurs when at least one variable in the system responds to change in another variable in the opposite direction: when A goes up, B goes down. If the relationship between B and A is positive (i.e., if B goes down, then A goes down too), then change in both variables is attenuated. (See Figure 1.2 in the case application.) An everyday example of negative feedback is a temperature control system. When the temperature in the room rises, the heating is lowered and the temperature begins to fall. As the temperature drops, the heating is increased and the temperature rises again. Provided the limits are close to each other, a steady room temperature is maintained.

Feedback can be used for analyzing the dynamics of a given situation, because it can be associated with specific patterns of behavior:

- Reinforcing feedback leads to exponential growth (or decline) and escalation, whereas balancing feedback results in stabilization.

- Reinforcing feedback by itself is unstable, amplifies small effects, and tends to get out of control. But it is usually counteracted by a balancing feedback of some sort (e.g., spending money, which slows down the growth of a savings account). This might result in s-shaped growth, where initial exponential growth is followed by correctional behavior.

- Delay between variables is the cause of oscillatory behavior, where a variable fluctuates around some level and makes predictions rather difficult. If delays are involved, such oscillations can appear in combination with all the other patterns mentioned above.

The behavior of a system over time is generated through the interaction of these feedback loops and delays. CLDs can also be developed, then, through observing patterns of behavior and identifying the systems structures that are known to cause the pattern. But even if this underlying structure can be mapped quite easily, the resulting behavior is usually far from simple; it is oft en nonlinear, counterintuitive, and hard to predict. CLDs, then, cannot predict patterns of behavior, nor are they reliable as means of explaining behavioral patterns. To this end, CLDs should be transformed into system dynamic diagrams that allow simulation (see Chapter 2).

DETAILED DESCRIPTION OF THE METHOD AND HOW IT FUNCTIONS

Identifying Relevant Elements

Since CLDs are not an end in themselves but facilitate better understanding, first of all, a situation of interest must be identified, be it an issue, a problem, or an event. This should be as specific as possible because it makes the following choices easier. Next, the time horizon that is considered appropriate should be defined; this should be at least long enough to see the dynamics that are relevant for the situation play out.

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