Making Systems More Approachable — Part 2
In Part 1 of Making Systems More Approachable, we highlighted the shortcomings of traditional systems maps. Thank you for all the thoughtful responses — especially David Peter Stroh’s reminder that while systems maps aren’t perfect, they’re still incredibly useful as part of a larger sense-making process, especially when developed collaboratively by the stakeholders themselves.
We believe there has to be a better way to communicate systems though, so without further ado we’re proposing to split systems mapping into two distinct phases: research and synthesis.
Systems research (aka what we’ve been doing)
By “systems research” I’m referring to the traditional systems mapping process — the causal loop diagrams you’re used to with all the pluses and minuses. The goal of the research process is to identify, document, and discuss all of the relevant factors and relationships within the system from an unbiased view point.
The value of the resulting map will not (and should not) be immediately clear. The only thing the research map should convince anyone of is, yes, this problem is indeed overwhelmingly complicated — let’s think a bit more before we touch anything!
Systems synthesis (aka communicating systems)
While the research phase is driven mostly by observation, the synthesis phase is about inferring what’s truly happening based on what we’ve learned — and hopefully convincing others we’re right in the process.
The purpose of our synthesized maps should be immediately clear, offering opinionated conclusions derived from and supported by our research.
A synthesized map has a single purpose: convince me!
You’ll know you’re on the right path when you can synthesize arguments for multiple sides from the same research map. The research map supplies facts. Synthesized maps use those facts to draw and communicate conclusions.
Now that we’ve covered the differences between the two, let’s walk through the process of synthesizing a research map using this map of the factors influencing unwanted pregnancies by Andrew Frangos and Jenny Zhou:
How to synthesize a systems map
To do this right you’ll need to stop working from the premise that everything needs to exist on a single map. Maps are cheap! Experiment and throwaway. There could be dozens of stories worth exploring. Let’s go!
1. Take a stance
In a research map, there’s no such thing as good or bad, just is. Namaste.
Not so in a synthesized map!
Before you do anything, take a stance. Take a strong one while you’re at it. After all that unbiased research and sensitive stakeholder-handholding you’ve done it’ll be a huge relief.
What story are you going to tell?
Who are you trying to convince?
What is your goal? Are you trying to rally a community around a specific leverage point? Get board approval for your strategy?
Here are a few frames to consider:
- What dynamics surprised you the most?
- Why do you think previous efforts failed to make a difference?
- Where do you think is the best place to intervene?
- What unintended consequences are you most concerned about?
Notice the not-so-subtle emphasis on you. You did the research. You had the conversations. You are now the expert here. What are your conclusions?
For our example, we’ll explore the following stance: abstinence-only sex ed perpetuates the need for legalized abortion.
2. Map changes, not stocks
Throw out what you’ve heard about systems being made of stocks and flows. That’s an abstraction useful to computers, not humans.
We’re no longer modeling systems, we’re telling stories about systems to humans.
Instead of stock and flow we’re going to use a simple framework everyone understands: cause and effect.
In our synthesized maps, elements represent effects and connections assert causal relationships between one effect and another.
Elements imply “more of this” unless labeled otherwise. Connections imply a “leads to” relationship unless labeled otherwise.
You’re free to explicitly label every element and connection for clarity. But no funny business here! The traditional plus and minus notation will not be tolerated. Only real words humans use and understand.
These rules may sound rigid, but there’s power in constraints. Most notably:
- The connections between elements form natural sentences like “more unwanted pregnancies [leads to] more unwanted births”. Now anyone can read the map without previous guidance.
- By removing nuance you’ve made the map easier to critique. Now that I don’t have to interpret what you’re saying, it’s easier to accept or challenge your assertions (and ideally offer up supporting and counter evidence in the process).
Experienced system mappers may realize we’ve backed ourselves into a little corner here… what happens when you have competing dynamics pushing a factor in multiple directions (eg “more contraception use” and “less contraception use”)?
Well, first you should decide if one side significantly outweighs the other within the current frame. If so just focus on the primary dynamic:
If both sides are significant, use a neutral label for the element and clarify the relationship using connection labels:
Or just create separate elements for each side:
The goal here is to go with whatever conveys the story you’re telling the clearest without throwing out key information.
3. Let the loops tell the story
Instead of simply labeling the loops, we’re going to describe the essence of each loop in a single, clear sentence. These descriptive loop labels will allow readers to scan the map in a minute or two and have a solid grasp of the underlying dynamics.
But don’t stop there! Feel free to break loops up into multiple labels if needed, and don’t limit yourself to labeling loops alone. Label all key dynamics that aren’t immediately clear by looking at the elements and connections alone.
4. Be honest
Instead of hiding the fact that many of these causal assertions are best guesses, we’re going to be explicit about our confidence in each connection.
Don’t worry about being overly precise here. Just be honest. Simple values of sure/unsure are fine. Or just tag the connection as questionable.
Are you really sure this causal relationship exists? Do you have research to back it up?
These will frequently be the most important connections on the map since they’re often close-held beliefs and assumptions (level 2 leverage points for you Donella Meadows fans) that may not be grounded in reality.
And you’re done! You should now have a map that tells a story anyone can understand without further guidance. Ideally it tells that story so clearly that others can now politely point out where you are wrong and we can really start making progress.
You’ll likely ignore the original research map once you’ve reached your conclusions and work mostly from the synthesized maps. Which begs the question: If you’re just going to toss the research aside, do you really have to spend all that time making the research map in the first place?
Absolutely!
It’s a necessary step to gain an unbiased understanding of the bigger picture before jumping to conclusions. Plus the research map offers enormous value as an evaluation tool — an artifact that can be used to revisit and review the quality of past decisions and interventions.
Systems are hard. We need to stop making them harder. The next time someone tries to pitch their research map to you directly just say “whoa… where’s the synthesis?!?”
