Collective Intelligence – Eugene Eric Kim

Doug Engelbart, Human Systems, Tribes, and Collective Wisdom

Published December 18, 2018 by Eugene Eric Kim

Sunday, December 9 was the 50th anniversary of Doug Engelbart’s The Mother of All Demos. There was a symposium in his honor at The Computer History Museum and much media and Twitter activity throughout.

Among the many things said and written that caught my eye that weekend was a Twitter exchange between Greg Lloyd and Mark Szpakowski. Greg tweeted a quote from this Los Angeles Review of Books article:

“At the very heart of Engelbart’s vision was a recognition of the fact that it is ultimately humans who have to evolve, who have to change, not technology.”

Mark responded:

And yet 99% of the Engelbart tribe work has been on the techie Tool System. http://www.dougengelbart.org/firsts/human-system.html … used to say “coming soon”; now it has disappeared. Time to join up with recent progress on Social Technologies for Complex Adaptive Anticipatory Human Systems?

I agree with Mark, with one caveat: It depends on how you define the “Engelbart tribe.” Let’s explore this caveat first.

Tribes and Movements

There are many folks specializing in process design (what Doug would have categorized as “Human Systems”) who consider Doug a mentor or, at worst, an inspiration. I’m one of them, although I didn’t start (exclusively) from this place when I started working with him in 2000.

Three others in this group have been direct mentors to me: Jeff Conklin, who spent a good amount of time with Doug, and Gail and Matt Taylor, who didn’t, but who knew of him and his work. David Sibbet, the graphic facilitation pioneer, came across Doug’s work in 1972 and worked some with Geoff Ball, who was on Doug’s SRI team doing research on facilitating groups with a shared display. Those four people alone make for an impressive, accomplished, world-changing group.

There are also many, many more folks doing important work in human systems who aren’t familiar with Doug’s work at all or who don’t identify with him for whatever reason. Doug himself thought that lots of what was happening in both open source software development communities and in the Agile Movement were highly relevant, although he had nothing to do with either. At the Symposium celebrating Doug, Christina Engelbart, Doug’s daughter and the keeper of his intellectual legacy, connected the Lean movement to her dad’s work and invited Brant Cooper, the author of The Lean Entrepreneur, to speak.

An effective movement is an inclusive one. What matters more: Seeing Doug’s vision through, or establishing tribal boundaries? If the former, then it’s important to acknowledge and embrace the work of those who may not have the same heroes or conceptual frames of reference.

I don’t think many of us who loved Doug and were inspired by his vision have been very good at this, and unfortunately, our tribalism has extended to technologists too. After the Symposium, I had drinks with my friend, James Cham, who is a long-time fan of Doug’s, but who wasn’t lucky enough to spend much time with him. James told me that Dylan Field (co-founder of Figma Design) was inspired by Doug and that he had hosted his own celebration of the Demo that same Sunday that 300 people attended. Amjad Masad (founder of Repl.it, a tool that Doug would have loved) gave a thoughtful toast about Doug’s work there.

I didn’t know either Dylan or Amjad, and I certainly didn’t know that they tracked Doug’s work and were inspired it. I’m fairly certain that the organizers of the official celebration didn’t either. That’s pretty remarkable, given how small of a place Silicon Valley is. Now that we know, I hope we can start making some fruitful connections.

Capabilities and Collective Wisdom

The movement of folks committed to Doug’s larger vision is much larger than the “official” tribe to which Mark referred in his tweet. But even if we take into account this larger group, I think Mark’s criticism still holds.

Doug sought to make the world collectively smarter. He believed the path to achieving this would be a co-evolutionary process involving both tool and human systems. In other words, new tools would give us new capabilities, assuming we learned how to master them. Those new capabilities would inspire us to create even better tools. Rinse, and repeat.

As my friend, Travis Kriplean, pointed out to me this morning, we can already test this hypothesis. Technology has already evolved exponentially. Have our collective capabilities — or even more importantly, our collective wisdom — evolved with it?

Let’s narrow the question. Our ability to capture, store, and share information has improved by leaps and bounds since Doug’s Demo in 1968. Has our collective memory increased as a result of that?

If you were pinning me down, I would guess, “no.” The mere existence of those tools don’t guarantee that we remember more. Furthermore, the tools have a nasty side effect of overwhelm. But, these tools certainly create the potential for us to remember more — we just have to figure out how.

Right now, my eight- and 14-year old nephews have access to this blog, where they can read many of my innermost thoughts, including stories I wrote about them when they were younger. Right now, they can explore my Flickr, Instagram, and YouTube accounts without even having to ask for permission. If they asked for permission, I would probably let them go through my Google Maps Timeline, which is automatically harvested from my cell phone’s location data and which contains a comprehensive journal of my every day travels over the past few years. They already have access to lots of information about me, including my efforts to distill little bits and pieces of my experience. Most of this is purely the result of technology, with a little bit coming from my occasional discipline of sharing thoughts here and there.

But does any of this help them become wiser? If not, is it because our technology has not evolved enough, or is it because our human practices have not evolved with the technology?

The best example I know of a human system that evolved with the technology are wikis in general and Wikipedia in particular. Not enough people realize that wikis and Wikipedias aren’t just tools. They are a wonderful marriage of human and tool systems that created fundamentally new collective capabilities, exactly the type of thing that Doug envisioned. They are also 20-year old examples. I think this speaks very much to Mark’s critique.

The Not-So-Mystifying Power of Groupthink and Habits

Published March 19, 2017 by Eugene Eric Kim

My friend, Greg, recently sent me this excellent and troubling Nate Silver article, “There Really Was A Liberal Media Bubble,” on the 2016 presidential election. Silver references James Surowiecki’s book, The Wisdom of Crowds and suggests that political journalists fail the first three of Surowiecki’s four conditions for wise crowds.

Diversity of opinion (fail)
Independence (fail)
Decentralization (fail)
Aggregation (succeed)

Many of Silver’s points hit very close to home, not just because I believe very strongly in the importance of a strong, independent media, but because improving our collective wisdom is my business, and many fields I work with — including my own — suffer from these exact same problems.

On diversity, for example, Silver points out that newsrooms are not only not diverse along race, gender, or political lines, but in how people think:

Although it’s harder to measure, I’d also argue that there’s a lack of diversity when it comes to skill sets and methods of thinking in political journalism. Publications such as Buzzfeed or (the now defunct) Gawker.com get a lot of shade from traditional journalists when they do things that challenge conventional journalistic paradigms. But a lot of traditional journalistic practices are done by rote or out of habit, such as routinely granting anonymity to staffers to discuss campaign strategy even when there isn’t much journalistic merit in it. Meanwhile, speaking from personal experience, I’ve found the reception of “data journalists” by traditional journalists to be unfriendly, although there have been exceptions.

On independence, Silver describes how the way journalism is practiced — particularly in this social media age — ends up acting as a massive echo chamber:

Crowds can be wise when people do a lot of thinking for themselves before coming together to exchange their views. But since at least the days of “The Boys on the Bus,” political journalism has suffered from a pack mentality. Events such as conventions and debates literally gather thousands of journalists together in the same room; attend one of these events, and you can almost smell the conventional wisdom being manufactured in real time. (Consider how a consensus formed that Romney won the first debate in 2012 when it had barely even started, for instance.) Social media — Twitter in particular — can amplify these information cascades, with a single tweet receiving hundreds of thousands of impressions and shaping the way entire issues are framed. As a result, it can be largely arbitrary which storylines gain traction and which ones don’t. What seems like a multiplicity of perspectives might just be one or two, duplicated many times over.

Of the three conditions where political journalism falls short, Silver thinks that independence may be the best starting point for improvement:

In some ways the best hope for a short-term fix might come from an attitudinal adjustment: Journalists should recalibrate themselves to be more skeptical of the consensus of their peers. That’s because a position that seems to have deep backing from the evidence may really just be a reflection from the echo chamber. You should be looking toward how much evidence there is for a particular position as opposed to how many people hold that position: Having 20 independent pieces of evidence that mostly point in the same direction might indeed reflect a powerful consensus, while having 20 like-minded people citing the same warmed-over evidence is much less powerful. Obviously this can be taken too far and in most fields, it’s foolish (and annoying) to constantly doubt the market or consensus view. But in a case like politics where the conventional wisdom can congeal so quickly — and yet has so often been wrong — a certain amount of contrarianism can go a long way.

Maybe he’s right. All I know is that “attitudinal adjustments” — shifting mindsets — is really hard. I was reminded of this by this article about Paul DePodesta and the ongoing challenge to get professional sports teams to take data seriously.

Basis of what DePodesta and Browns are attempting not new. Majority of NFL teams begrudgingly use analytics without fully embracing concept. Besides scouting and drafting, teams employ analytics to weigh trades, allot practice time, call plays (example: evolving mindset regarding fourth downs) and manage clock. What will differentiate DePodesta and Cleveland is extent to which Browns use data science to influence decision-making. DePodesta would like decisions to be informed by 60 percent data, 40 percent scouting. Present-day NFL is more 70 percent scouting and 30 percent data. DePodesta won’t just ponder scouts’ performance but question their very existence. Will likewise flip burden of proof on all football processes, models and systems. Objective data regarding, say, a player’s size and his performance metrics — example: Defensive ends must have arm length of at least 33 inches — will dictate decision-making. Football staff will then have to produce overwhelming subjective argument to overrule or disprove analytics. “It’s usually the other way around,” states member of AFC team’s analytics staff.

On the one hand, it’s incredible that this is still an issue in professional sports, 14 years after Moneyball was first published and several championships were won by analytics-driven franchises (including two “cursed” franchises, the Boston Red Sox and the Chicago Cubs, both led by data nerd Theo Epstein).

On the other hand, it’s a vivid reminder of how hard habits and groupthink are to break, even in a field where the incentives to be smarter than everyone else come in the form of hundreds of millions of dollars. If it’s this hard to shift mindsets in professional sports, I don’t even want to imagine how long it might take in journalism. It’s definitely helping me recalibrate my perspective about the mindsets I’m trying to shift in my own field.

On Doing Things Well

Published September 6, 2012 by Eugene Eric Kim

My business partner, Kristin Cobble, is a Peter Senge disciple, and we’ve been having good conversations over the past few weeks about learning organizations. In the course of these discussions, I was reminded of a huge pet peeve of mine. I hate it when people say things like:

“We’re not collaborating.”

“That’s not a network.”

“We’re not a learning organization.”

when what they actually mean is:

“We’re not collaborating well.”

“That’s not an effective network.”

“We’re not an effective learning organization.”

I’m not just being pedantic. Not only does the qualifier matters, it’s the question that most of us actually care about.

Let’s take learning organizations as an example. Senge defines learning organizations as organizations that are continually expanding their capacity to create their desired futures. (This is almost identical to Doug Engelbart’s definition of organizations that are collectively intelligent.)

By this definition, any organization that is profitable is a learning organization, because more money generally increases the capacity of most groups to create their desired futures. Of course, this is not what most people actually mean when they talk about learning organizations. It’s not because it’s wrong. It’s because what we really care about is what makes organizations effective at learning. Profitably can indicate effectiveness, but it is not the defining factor.

Treating things like learning and collaboration as a continuum rather than as a binary acknowledges what you are already doing and supports you in building on that, rather than assuming (usually incorrectly) that you’re not doing it in the first place. It also gets you out of the mindset of trying to follow someone else’s predefined template. In other words, it puts the emphasis on the verb (to learn, to collaborate) rather than the noun.

Senge’s attributes for learning organizations are great, and I refer to them all the time. But when people ask me about what it means to be a learning organization, I turn the questions back to them:

What does it mean for you, individually, to learn?
What’s an example of something you’ve learned well? What enabled you to learn that effectively?
What’s an example of something your organization has learned? What enabled it to learn? How could you create conditions that would enable your organization to learn more effectively?

Simply taking the time to explore these kinds of questions together is the first step toward making any group effective at learning.

Doctors on Planes

Published October 10, 2010 by Eugene Eric Kim

On the plane ride home from Miami last night, the flight attendant got on the PA and asked if any of the passengers were medical professionals. Someone had fallen ill and needed help.

Two people rang their bells immediately, and they went to assist the passenger.

This is a wonderful, real-life example of leveraging the collective intelligence of the group. If you take a large, random group of people, you are likely to have an incredible diversity of skills and knowledge in that group. You don’t have to worry about seeding the group with “experts.” They’re already there; you just have to have a space and a process that allows the expertise to emerge.

I wonder how often this happens on planes? I fly 4-8 times a year, and I remember this happening only twice on previous flights, including this time. Both times, there were several doctors on board.