On to becoming Steak Florentine, the Mercato, Firenze, Italy

I don’t much like to write solely about personal empathetic development.  The reason is simple — I’m a systems guy, and writing about how one person can move up the Spiral seems to be counterproductive.  There are lots of self-help books out there.  On top of that, I also personally feel that one of the largest problems with SD is that it easily lends itself as a tool for hierarchicalization — my v-Meme’s better than your v-Meme — with higher being necessarily ‘better.’  You go to work on yourself, you evolve, maybe — but the larger structure just doesn’t change.

That said, we NEED more evolved people.  And yes — it’s been a universal problem forever.  Various cultures and religions have been working on this for thousands of years, using different aspects of empathy.  My favorite example has to be Tibetan Buddhism, which places all its money on an enlightened, Global Holistic v-Meme leader (the Dalai Lama), magical thinking, and mirroring behavior.  Realizing that there’s no way the resources exist to pop everyone out of the magical v-Meme, where so many poor Asians reside, they formed a system where everyone looks up (and copies) the head honcho, whom a select elite makes sure grows up to be one of the coolest dudes on the planet.  Add to that a pipeline through which many young men and women pass through (many young people become monks for a couple of years, then go back to more normal lives) that teach meditation and self-reflection — pretty unbelievable.

In that spirit, there’s nothing wrong with a little thinking, especially with the approaching New Year, on how the various v-Memes actually work, knowledge-structure-wise.  Most of us would like to improve our Performance-based behavior.  Performance-based behavior is the first v-Meme where real New School Design Thinking becomes emergent.  So it’s worth a little time pondering over the holidays.

Let’s start with a little deconstruction from our basic empathetic social/relational structure background, and see if we can’t reason through this together.

Here are some principles that govern all of the v-Memes:

1.  As we evolve, our temporal, spatial and energetic scales necessarily increase.
2. We increase our agency (capacity for independent action) and responsibility toward ourselves and others.
3. As we increase our agency, we increase our awareness of timescales, and our ability to affect them.
4. We transition more and more toward data-driven thinking.
5. As our empathy increases, we also increase our receptivity toward grounding our thoughts in larger and larger circles.

The transition from Legalistic/Absolutistic thinking to Performance-Based thinking is one of the most important of the transitions. When we make the transition, we are now opening ourselves up to independently generated, trust-based relationships — meaning that we will evaluate/perceive people not just on WHAT they are, but WHO they are.

The line that divides this portion of the Spiral is what I call the Trust Boundary, and starts a very important transition from primarily belief-based thinking to rational, data-driven analysis.  At this point, it’s important to remember the nested, emergent nature of the Spiral — we don’t just throw away all our lower modes of thinking — beliefs still matter — but we incorporate them into new modes.

For Performance-based v-Meme development, here are some good vectors.

1.  Develop authentic mastery of a given area.  Authentic mastery develops the empathetic relationship to self — if you want to have  independent, data-driven relationships with other folks, you first have to have one with yourself.  Yvon Chouinard, the founder of Patagonia, insists in his book that he never felt comfortable hiring someone who couldn’t do real work with their hands — an implicit endorsement of this authenticity principle.
2. Reflect on your level of mastery and expertise — use data and examples to accurately assess where you are on your journey toward expertise.  Looking at what others have done gives you metacognition — making you aware of what you don’t know, and how much further the journey will take you.  For example, I am a woodworker, and participate in Internet groups that have lots of other work displayed.  This lets me see how far I’ve come, as well as how far I have to go — and also gives me people whom I can ask for advice and consent while seeing the real results they’ve produced.
3. Be aware of your own impulsive thought — slow down your timescales and pause before making decisions.  One of the books I’ve discussed, Thinking Fast and Slow, by Daniel Kahneman, exhaustively catalogs the benefits of slow thinking.
4. Practice engaging in multi-solution thinking, preferably with a partner that you respect and trust.  Brainstorm multiple solutions to a given problem, and then list the reasons why or why not you think the solutions might be good (or not so good) ideas.  A fun book that shows this (especially if you like the Beatles) is Powers of Two by Joshua Schenk.  He details the paired interactions of some of the most creative people in the world.
5. If you’re given a problem, assemble multiple paths that could be followed to arrive at a solution.  Write down what you’re trying to optimize, and then judge those paths based on your criteria.  Think of this as being similar to finding your way across town during heavy construction.  There are many roads that you can travel — but which one you’re interested in is the one that suits your temperament.
6. Iterate, iterate, iterate!  — This word was a gift from a new friend, and is the key toward becoming a Performance-based thinker.  Iterating naturally puts different timescales in your process, and starts you on the path of decoupling your emotions from your process, and focusing on getting results.  Modify the path, and perhaps, modify the goal as new data becomes available.  Make fewer parts of your final state set in stone, and adopt a fluidity of mindset.
7. Ask someone (or work with someone) outside your normal group cohort for their opinion, and then actively work on incorporating that person’s ideas into a synthesis of your work and their ideas.  Nothing beats a diverse workforce, or a strong customer ethic, for growing this part of your brain and empathetic profile.
8. Understand your own path as a heuristic — a series of assembled steps that you control, that have inherent potential for good outcomes as well as bad.  Estimate the risk in each step, and in your overall path.
9. Understand that there will always be factors you can’t control — the other side of metacognition — while at the same time, work towards defining these and exploring them so they become more and more concrete.

That’s a start.  And maybe one more.  Practice saying ‘I don’t know’ if you really don’t know.  Change this from “I don’t know, and so therefore I must be stupid” to “I don’t know, and now that I know I don’t know, I’m going to find out!”  It’s the sign of real expertise.

Takeaways:  Here’s a Powerpoint Slide I use to describe Performance-based thinking and data structures.  Worth a read!

 

Liverpool, England, in front of the Hard Days Night Hotel.  Not surprisingly, Liverpool has a thing for the Beatles!

If you’ve been following this blog for a while, I know by looking at my statistics,  that you likely have not read all the posts.  After all, I am aware that while a lot of my readers are fond of me — I was the professor that helped get them ready for the work world, or a business associate with my Industrial Design Clinic, and we enjoyed educating students together — I also know that I am not an internationally recognized expert on organizational development, or empathy, or philosophy.  I haven’t cut it yet in the status-based lower v-Memes.  I’m not bothered by this —  those that know me personally know that I’m not much of an Authoritarian or Legalist.

But you’re probably thinking — “well, Chuck, that’s nice.  But why should I really care?  And how does this really matter?”

Here’s some insight.

Last week, I was in Liverpool, England, at Liverpool John Moores University (LJMU) at a conference for UAVs (Unmanned Aerial Vehicles — think quadcopter or fixed-wing drones) applied to environmental science and problems.  The breadth of application of UAVs to problems in the environmental arena was immense.  The keynote speaker, Tom Snitch, talked about his program, which involves using relatively low-cost drones to monitor elephant and rhino poaching in Africa   Others talked primarily about the use of LiDAR mounted on a UAV, which is basically using light like radar waves to map vegetation and landscape features.  Others used UAV-mounted regular cameras to create high-resolution photo mosaics of landscapes that are much more high resolution than available from satellite images.  And so on.

But in such a potentially synergistic, systemic world, those connections were few and far between.  The key element in all this was the structure of the combined UAV – sensor system.  In a room full of passionate, sophisticated people, the basic structure of a UAV system was ‘take a store-bought UAV and mount a camera on it.  Figure out how to trigger it and capture the location of the image and bring that back to the ground for post-processing.’  The design structure of any system that does that — regardless of the complexity of the design of either the UAV, or the measuring instrument (like the LiDAR unit) — is three fragmented, non-synergetic blocks in a row.  Learn how to fly the UAV, bring back the pictures, make your map.

It’s pretty obvious that this maps to the non-empathetic structure of researchers in the academy.  Three blocks put together, basically in what we call open loop feedback (read ‘no feedback at all’) .

What is interesting as well is to see how this social/relational structure will attempt to solve their problem.  How will they evolve?  In a fragmented social structure, the first (and likely subsequent) iterations will likely involve more pictures (read more fragmentation) and more detail.  More computer processing, with more sophisticated on-the-ground mapping algorithms for more complex assemblages of images.  Greater accuracy in the GPS units used.  Paying more money for UAVs with greater flight stability.  And so on.

Notice how NONE of these things engage in any meaningful feedback between the elements.  How could they?  How could the people engaged in the task develop any synergies at all, given the social structure of the typical academic enterprise?  Synergies with this given social structure are likely to come (not surprisingly) when the resolution of pictures taken on the ground get down to pebble size.  Fancier cameras.  More stable UAVs.

And that’s exactly what is happening.

What would be required for synergies?  The short answer is a different social/relational structure.  We might start with the old ‘multidisciplinary teams’ axiom.  Perhaps if we added someone who was an expert in flight control and dynamics, they could stabilize the UAV better.  Someone in cameras could invent a camera with greater resolution.  And so on.

What’s the takeaway? If we pursue a similar, fragmented non-empathetic structure, we can see that multidisciplinary teams approach doesn’t really add much to the synergies of the device.  At first blush, the different component providers don’t need to do much understanding of each other — knowledge can be passed in fragments, like ‘well we’d like finer resolution.’  And things would march down exactly the same path.  Perhaps a little faster, but likely much more expensive.  More people on the project definitely means more dollars.  Higher resolution equipment is going to climb up that marginal cost/performance curve that every product possesses.

What happens, however, if we pursue a different structure — where we now have a multidisciplinary team, with pairing between different components of the entire UAV system?  The mapper says to her partner, the camera designer ‘I want finer resolution.’  In an empathetic exchange, the camera designer would hopefully ask ‘why?’  The mapper would then explain that things aren’t going so well on the boundaries of images, and she figured that finer resolution was the answer.  The camera designer then might say ‘well, you can get finer resolution, but if you still can’t improve the auto-stabilization and orientation of the UAV, any more pixels are just going to get lost in the noise.’  So after understanding the problem with perhaps a little math, they make a decision to engage the flight control person.

The flight control person goes through an empathetic exchange with both the mapper and the camera person.  It turns out that the real problem with getting the pictures to overlap is that the UAV turns a little in the wind, and that makes the photos not line up on a nice, even grid.  So the real answer is to put two GPS units on the UAV, separated by a meaningful distance, so that the UAV can be flown with both a static coordinate, as well as an angular direction orientation.  Then mapping can commence so that you don’t have blurred pixels on the boundary, and so on.  The social structure, as well as the degree of empathetic connection, all has to change.  And in the world of empathetic connection, there’s going to have to be a whole lot more of it.

Or if nothing else, it gets discovered that we can’t yet orient the UAV at a given angle.  So we don’t waste money on more and more expensive cameras, or mapping software — because we really can’t do better than the fragmented system.  Either way, the performance of the system goes up.  Money is saved from not pursuing something not feasible (or too expensive), or mapping accuracy is improved.

And we can also see how trust is brought into the picture.  If one component expert doesn’t know the other component expert, how does one know whether they can believe them?  Only through an evolved working relationship can the mapper be sure if the flight control UAV expert is telling the truth — whether it be that you can orient a UAV, or you can’t.  Empathetic connection is the primary tool for assessing someone else’s metacognition — if they know what they know, as well as what they don’t know.

The non-empathetic, multidisciplinary effort yields results similar to the fragmented academic social structure.  Just as Conway would have predicted.  And the understanding of the level of empathetic connection leads the project manager on the same path as has been discussed in this blog.   😉

Takeaways:  Sophistication of individual knowledge doesn’t do you that much good if you can’t work at the boundaries (or even in the guts of these systems) with other experts to optimize and synergize shared results.  And empathetic connection between teammates is the pathway toward getting a better shared result, without having to go outside and pay a ton of money for experts who may or may not know what they’re talking about.  A little bit of empathetic relational development goes a long way.  Change the social structure if you want to change the performance.

Further reading: This piece on Tom Snitch’s work in South Africa regarding using drones for prevention of poaching elephants and rhinos shows, better than anything, that it is often social factors and trust that limit all our efforts.  It is indeed all about empathy.