Convergence Leadership Series #3
Complex Way of Seeing and Sensemaking
The third seminar on April 7 was dedicated to sensemaking. We talked about what is sensemaking, why is it important, and explored two methods for complex sensemaking: story telling and the debate.
As is our tradition, we started the seminar with a warm welcome to the whole human in our team members. This is our moment catcher check in question: over the past month, what were the moments you would like to remember? This is a gentle easing into the story telling part of the seminar.
Sensemaking: What and Why
Sensemaking is the process by which people give meaning to their collective experiences. It is the proccess people answer the question “what is going on”? Today I attended the NSF Leap to Large series of seminars, where coach Muffet McGraw shared the importance of open communications to make sense together, esp. during a losing streak. She said “because otherwise people are going to make up stories in their minds and blame each other.”
The truh of the matter is, if there is no space for making sense properly, everybody will make up stories themselves, make decisions based on those stories, and take actions accordingly. That is where it will become random arrows shooting at each other, without coherence or synergy. If organizations wonder about the amount of resistence in the action taking phase, maybe the answer lies in the upstream sense making stage (see image on the right). Think of the complex realities organizations have to wrestle with as the ‘elephant’, and individuals in organizations as the ‘blind men’ (see image on the left). The chance of gettting closer to reality is so much higher with making sense together.
Story telling
We focused on the question “why storytelling”? I believe too many communications workshops focus on the skills of story telling without fully opening participants’ mind and heart to the importance of storytelling. I also belileve we are alll natural born story tellers. Once we fullly grasp the importance of it, we can all come up with strategies to do it better.
Check in
Question 1: What comes to mind when you hear the word “storytelling”? Share both positive and negative.
Answers: Long, meaningful, cultural traditions; myths; my father-in-law; connecting to people’s emotions, empathy; non-Western ways of knowledge; “moral” of the story; buzzword, relationship-building; side track; lesson; inspirational, survival bias; learning; passing along information/culture; positive: walking in someone else’s shoes; negative: possible preachy-ness; our brains are organized to accept stories
Question 2: How do you relate storytelling to complexity both for individuals and for systems?
Answers: requires patience and respect; anchoring us to a specific case, example, or experience to get away from the amorphous qualities of circular economy; the elephant in the room is a great example! I like to think of complexity as a spider web — we pull one aspect and the system can shift; every one of us has a unique life story and connect with different aspects of storytelling…; indigenous people use storytelling to make sense of how the world works; seek first to understand, then be understood; a way of defining that complexity for all to understand; myths are like meta-stories; there are different perspectives and uniqueness about everything
How many ways can you explain gravity?
We can explain gravity with data sets, with formula, with definition; or with Wile Coyote’s gravity lesson.
Stories are effective and memorable because they touch on our emotions. Research shows that we decide with emotion and justify our decisions with logic. In addition, stories tap into the intelligence of both the mind and the body, and create an experience for listeners while imparting the knowledge. Experience is far more memorable than knowledge, as it defeats the forgetting curve.
Exercise
Prompt: Tell the story of how our team is contributing to the circular economy in a convergent way.
Answers:
Highlight challenges: eg. What even is consumption?
Creating a collective definition of CE that includes people and well-being (something we wouldn’t have done otherwise)
Taking the time to learn about other’s tools and consider how we can work together
Our nature paper was truly team-forming; the concept of convergence and divergence and the “pitch ideas”
Initial divergence is essential to being a dynamic team
The story of our whole team coming together to learn how convergence happens in the CE, but then starting the process of converging as a team ourselves.
And, but, therefore style of storytelling. How can we tell our story in a metaphoric way using the hero’s journey (Lord of the Rings specifically)
Our non-linear, roller coaster-like road to convergence
We are like the different threads coming together to form a solidified understanding of the working of CE. Each one of us comes with a different perspective and we can work together to create a better understanding
The Debate
With the ladder of inference, I invited team members to think about: where do our different disciplines and perspectives each start to come into place? And how does it play out at each rung of the ladder?
Some team members think it is where we add meaning or assumptions, while others think it starts at the very bottom — we cannot observe neutrally because what we see is informed by what we are expecting to see, what we think is surprising. Then in the next rung, selection bias creeps in, which could also enter us into the ‘reflexive loop’. This points led us to appreciate the neccessity of convergence research:
I’m imagining like a second head and a bridge between those rungs. So for example, say that this head belongs to an engineer and it’s thinking inside the box. Then suddenly this engineer collaborated with an anthropologist. And then there is a bridge between the beliefs and they share values and their values and how they see the world. And because of that, the cycle is now different. We are able to open our mind and see data differently because of this bridge that we are building. So there’s this vertical thing that each of us is doing. And then there’s this horizontal bridge that will be the collaboration between them.
When we expand our beliefs, we are able to go through the reflexive loop, come back, and see the data differently. That’s where I think a lot of research goes into peril, when we say we have to be ‘on the same page’. The same page, for me, means to make sure that we are looking at the same observable data and experiences where there could be some basis of common ground we can build upon. Then we also need to make the steps climbing up the ladder very clearly. It is not about “which one’s right and which one’s wrong”, but we need to be aware of where we’re at on the ladder. Also we need to be willing to examine those assumptions and reselect the data if it’s challenged.
We also had two concrete examples to back up these insights:
One of the things like that Cindy, Brie and Nancy and I (Melissa) are working on is this LCA and SLCA paper — and Joe and Vicas and Callie, basically a lot of people. We’re so used to putting the numbers through and saying, “here are the categories and these are the findings.” And then we have impact and workers or something like that. But then with Brie and Cindy (anthropologists), they’ll say, “well, what does that really mean? What is the true impact? What are the consequences if you shift production somewhere?” And so I think having this large team allows us to then almost have checks and what we’re actually saying and get used to doing it. And I see that as a benefit.
Even in a huge amount of data and observations, the method by which those data are collected in the observations all have built-in bias. We’re using instruments designed by people and we’re using measurements that are only carried out by people. Even AI in computational methods still retain a lot of that bias. I think that’s one of the challenges that’s interesting, because we often put a lot of weight on empirical data versus the sum of what all of us do, which is modeling data. But even empirical data has so much bias baked into it because you can never completely analyze every aspect of your system, you can never fully isolate all of the variables.
The exercise
For the debate, we invited Cindy to give us four perspectives on this question: Should we adopt a federal level bottle bill (which is actually part of the Break Free from Plastic Pollution Act)? This scenario is from her focus group, which carrys much potency.
Prompt: What are your thoughts on all the perspectives presented (a state level environmental regulator, a representative from a packaging trade group, a CE thought leader, a community leader)? What are your own thoughts from both your personal and professional experience?
Process: Two rounds of “throwing the arrow”: The statements from each side, without interruption.
Philosophy: Everybody is encouraged to express views from both sides to promote role fluidity, discipline fuzziness and psychological safety for minority views. At the end check if anything left unsaid from either side.
Takeaways
No black and white, important to see the nuance.
The reasons why we take action (or don’t) matter.
I haven’t thought about the perspective from the community leader, and the impact that EPR policies would have for communities who have relied on their waste collection organizations!
Trying to step into another person’s shoes (opposing view)
The Ladder of Inference is a constant but “in the background” happening. It can be helpful to bring it to the foreground and stop “assuming.”
The details to a bill like this are very important. Stipulations about how to recycle w/o including toxins and potentially taking money out of community hands could unintended consequences of this.
