We’ve all heard the adage, “everything is connected to everything else.” However, in order to be able to analyze complex, dynamic systems phenomena, we have to imagine an artificial boundary between the system in question and everything else.
Consciously choosing a boundary enables us to focus on the factors and interrelationships that most directly influence the behavior we’re studying. Without the boundary, it’s easy to be overwhelmed or miss the story we are most interested in.
We can consider two types of boundaries: spatial and temporal.
- We can define the “space” the system covers by selecting the set of parts that we think are most significant. Identify parts, interconnections, and causal loops all help define the spatial boundaries of the system.
- To define the temporal aspect, we can create a “time horizon,” the length of time over which a system’s behavior unfolds. We can represent key behaviors or trends with a simple line graph, Setting time horizons, e.g., of 1 month, 1 year, or 10 years or more, counters the pressure to collapse time horizons and force our attention on snapshots or events, rather than patterns of behavior.
Try this: Imagine you are on a task force to understand why the student dropout rate is increasing in your school. How might you define the boundaries of the system that results in this situation? Consider that setting too narrow a boundary might miss key information and setting too wide a boundary might cloud the key elements of the story:
- What data would you gather to include in your analysis? What data might you leave “outside” the system. (For example, would you include data on administration’s and teachers’ economic, social, religious, or political backgrounds, similar student data, or personal experience of various players?