In reading about innovation, we have many marvelous examples where successful firms incorporated technology by changing their business model.  Rather than digitizing paper, they considered the intersection of organizational imperatives and technology and considered how the business may be done differently.

Some firms (and Agencies!) went a bit far.  I am still haunted by the story of the Senior Executive at the Department of Defense who saw his secretary replaced by a computer.  The professional assistant who knew why papers were filed a certain way, who knew the history for those files, and who maintained an informal network among other assistants that provided the best intelligence operation in existence.  Shunted aside (Or kicked upstairs so she could print out some SES’s email) because some Executives now had new, mystical, magical machines that would connect the human to all the information he could ever need.  This is an example of transforming the office ad absurdum.

Nevertheless, careful transformation can mean everything.  Consider the advent of online stock trades.  In a famous case study, Clayton Christensen (“Disrupting Class”) demonstrates that while Merrill Lynch was slow to provide online trading, it did so using the same brokers who were comfortable in the old models, and did so within existing departments.  By contrast, Charles Schwab immediately “created a separate business unit to conduct online trading and made a masterful transition to the computer-centric investment management world – ultimately phasing out its original broker-based business unit…the new unit operated at much higher trading volumes and significantly lower costs than those characterizing the traditional business.” [p.78]  Merrill Lynch used technology to improve their core business – successfully for a time – but failed to transform and adapt.  Or survive, as it turns out.

What does this have to do with the U.S. Intelligence Community?  Eight months ago, I noticed this little video on YouTube.

Note how this idea presents an approach for using technology (in this case a simple wiki, in other examples a “Facebook” for the IC) to transform how intelligence is produced.  Yes, Intellipedia has transformed analyst behavior, and to some degree appears to be chipping at the information-hoarding, publish-or-perish model – but do we want to be Merrill Lynch or Charles Schwab?  Do we want to make the analysts better, or consider transforming how we process and produce intelligence products as a result of this new magic? The true magic of these technologies is the unprecedented opportunity to leverage network effects for faster, better intelligence products in an age that demands them.

What is the strategy inside the Intelligence Community to harness the new magic?  I hope we are learning from successful innovations. One must be careful with magic, after all.

Keep trying to connect the dots, and you'll remain blind to the future

Originally appeared in Inside Knowledge Magazine 10 Sep 2008, Vol 12, Issue 1.

On 12 September, 2001, I received an e-mail from the CEO of my company (a federal contracting firm located just outside Washington DC). As F-16s continued their combat air patrols over my neighbourhood, I read, paraphrasing: ‘John, yesterday [9-11] was a failure of knowledge management. In the years to come, this will be the critical area for improvement’.

We soon heard about failures to ‘connect the dots’ regarding behaviours among flight school students, an arrest in the Midwest not shared across the FBI, and so on.

Seven years forward and US national security is changing. ‘Need to share’ is the buzzword, hoping to replace ‘need to know’. The director of National Intelligence releases a vision calling for sharing intelligence with law enforcement. The Department of Defense releases its first Information Sharing Strategy. The implication, never explicit: if only we get the right knowledge to the right person at the right time, we can know the future and learn which dots pose a threat.

When then-National Security Adviser Condoleeza Rice stated “I don’t think anybody could have predicted… that they would try to use an airplane as a missile”, she was wrong. Someone in government had actually considered that scenario. There are thousands of scenarios considered daily across the national security system – some will always be seen in hindsight as predictive. While technically incorrect, Dr. Rice pointed up an underlying truth. There are thousands of scenarios considered daily and we do not know which scenario, which threat, which dot deserves our attention before the fact. And if we keep assuming there is a golden thread that, if pulled, will unravel the future – we never will.

Systems scientists, organisational theorists and business leaders are beginning to work in a world where control can be an illusion and adaptation preferred. We are starting to focus on nurturing networks and relationships;a recognition that certain systems are, by their very nature, non-linear, and they change their behaviors based on their starting points and the random events that might ensue, leading to emergent new behaviors that cannot be predicted.

Anticipation replaces prediction. While linear models are generally developed to predict the future; complexity helps us anticipate developing patterns of behavior.

In reforming the US national security system, it is vital that we question assumptions regarding the predictability of our world and instead understand that we connect not to find the haystack needle, but in order to better understand and discern patterns in the noise. The subtitle of the interim report from the Project on National Security Reform is a good beginning: ‘Ensuring Security in an Unpredictable World’. How we apply KM and complexity principles to national security reform will shape our ability to secure the nation’s future.

Words mean things.  One of the more obnoxious statements of the obvious, and yet I find myself saying it more often these days.  The more I delve into understanding complexity theory, network science, and struggle to understand cognition and neuroscience, the more frustrated I get when people use terms in ways appear at odds with the literature.

As I was preparing this blog to address the use of ‘complex’ versus ‘complicated,’ I found that I am certainly not alone in trying to retain some clarity of language.  Paul Jansen, in particular, has a great blog post on exactly this topic.  Nevertheless, I owe the nice people who followed this exchange on Twitter this week a brief explanation of my frumiosity.

This week, caught up in the holiday mood – I found myself engaging this week in an exchange with a gentleman, Roger Sessions, who has developed a method for IT architecture designed to ‘reduce complexity.’  His paper features references to “attacking complexity” and includes a method for measuring it.  He introduces the “standard complexity unit,” based on something he refers to as “Glass’s Law,” which posits that for every 25% increase of complexity in a problem space, there is a 100% increase in the complexity of the solution space. This reflects work from a 1979 paper by Scott Woodfield, who first posed this idea.  The idea is that increasing the complexity of problems tackled by software engineers does not increase the complexity of the solution in a linear sense, but on an exponential scale.  It is this problem that Sessions seeks to take on with his approach.

Now the notion of reduced complexity is attractive, if you understand complexity as a system that has developed so many connections as to become unmanageable. This is a common usage for ‘complex,’ which seems to translate to “something too hard to understand or manage or control or cost.”  The notion of ‘wicked problems‘ applies here as well.  The greater the connections you find among things, the greater are your odds of decision paralysis and “failure.”  Solution?  Easy, make things simple.  The danger, for me, comes in simplifying management behaviors in ways that deny the nature of the systems we are attempting to manage.  If you believe complex is nothing more than the ‘opposite of simple,’ you are missing some of the most promising areas of applied research in a half century.

When I engaged the gentleman on his use of the term complexity, I received what I believed was an odd response.  For someone who uses the word in titling his books and lectures, he did not appear terribly connected to the word itself.  He even invited me to suggest a different term for what he was trying to achieve. The closest I could come to his definition for complexity (admittedly, without buying his book) is an ‘exponential growth in system states with regards to information technology systems.’  To me, he is trying to help people with an architectural approach that makes overly-complicated IT systems more manageable.

For his part, Roger was comfortable with my discomfort, because in his world “complex” merely means the opposite of “simple.”  Several of us during this Twitter-fuffle suggested the use of “complicated,” which suggests a system that has known but prolific connections.  Cause and effect in complicated systems are related and knowable, but analysis by an expert will likely be needed to connect them when something goes wrong.  My example here is the ‘check engine light’ on my car – while I am at a loss to understand the cause, an expert with tools can ascertain it quickly.  Modern car engines are extremely complicated.

They are not, however, complex.  My car engine is unlikely to evolve new features anytime soon.  There is a reason medical doctors have different training regimes than auto mechanics.  The latter deal with complicated systems, the former with complex ones.

Complexity is a specific term.  Complexity, as described in the literature, is a science that seeks to explain how emergent order (often called ‘hidden order’ or ’self-organization’) is observed in systems or (most) networks. For what it’s worth, I believe those seeking to develop IT architectures could benefit from a deep understanding of complexity, as their users are sloppy humans in messy and evolving sub- and extra-organizational work networks.  Methods for complex systems management show some promise in ‘attacking’ the unmanageable IT systems that Mr. Sessions is tackling here.  It may be that observing and nourishing self-organization among human-based networks, rather than embedding and enforcing an existing or desired organization within them, will help architects develop more manageable and relevant IT systems.

As a blog post, however, this has gone on long enough.  I just wanted to explain my bristling at a usage of the term ‘complex’ in a way that conflicts with the literature. At one point, Roger reminded me that he is trying to tackle an extremely serious problem.  I respect that, of course, and was doing the same.  Given the great work that is ongoing around complexity and complex adaptive systems, we owe some respect to giants upon whose shoulders we seek to stand.

Hat tip to Fred Zimny on finding this gem. I embed this video here because I wanted to also give some initial thoughts on what I’ve learned watching this.  You may be tempted to skip the video once you see it will take an hour out of your life.  This would be a mistake, but just in case I thought I would share some of my notes.

At first, I hesitated when I saw the title “digital age,” because I presumed I would be hearing more about the “digital learner,” and how kids are just so different today.  I don’t find there is much science to support this notion, and believe strongly that ‘generational’ characterizations are lazy, deny our shared humanity, and empower us to ignorance.  I’m looking at you, Myers-Briggs.

Much to my delight, John Seely Brown instead here touches on a core problem that I’ve had a hard time describing.  Specifically, and this comes during the Q&A: “there is no norm, no prototype, no typical example, in a power law distribution.  And the human mind is unprepared to reason about things that don’t have examples.”  We are trained to believe in Gaussian (normal) distributions, whereas much of our world is made up of power law distributions.

What? Brown gives an example:  what if architects had to account for humans who didn’t adhere to a normal distribution for height, but rather a power law distribution?  There would be millions of us around 1 foot tall, and a few poor folks 1 thousand feet tall.  How would you design that building?  Fortunately for architects, humans generally follow a normal law distribution for height.  Unfortunately for the rest of us, much of the world does not.

Translation:  we are surrounded by ‘black swans.’  The more we rely on the established wisdom about how the world works, the less prepared we are to succeed in a world that is in flux.  The good news is that our digital age, properly embraced, can help us adapt our notions of learning.  Our first inclination with new technology is to use it to evidence existing practices.  ”Digitizing paper,” if you will.  Moving beyond this will be key to embracing what Brown calls the “new disposition” for a digital age learner.

Other nuggets:

* The biggest obstacle to innovation is wisdom.

* Singapore is reinventing their education system based on a single maxim:  ”teach less, learn more.”

* Marking on a curve creates incentives that fight against social learning.  And all learning is social.

* Nothing clarifies ideas better than explaining them to others.

* Learning through creating, playing provides the foundations for constantly mastering a world in flux.  If your world is static, learn through teaching. If it is in flux, learn by tinkering.

Enjoy!

If you ask my friends and new colleagues, you will find I am known as the slightly addicted Internet guy.  iPhone?  Why, certainly.  MySpace?  Cancelled that one, haven’t you?  Facebook? Got my wife hooked on it.  LinkedIn?  Pro.  Twitter?  Are you serious?  @jbordeaux has had brief brushes with stars from Brea Grant to Danny DeVito.  (The former thanked me for an insomnia suggestion, and I’m fairly certain I made the latter laugh once with a ribald remark.)

Occasionally, I get a serious if exasperated question:  Why?  Why are so many people chattering away with strangers and long-lost childhood friends?  Why on Earth would people send text messages to, well, the Earth?  Why are people sharing private information in this online expansion of a phenomenon as old as time – social networks?  In the right mood, I tell them my story.  It’s time I shared it here.

Right as the holiday season hit in 2008, I learned I would be laid off after New Year’s.  As a result, I don’t recall much of last year’s holidays.  My family was around me, but I was absent and overwhelmed.  Every day was spent looking for work, but not in the usual way.  Instead, I used the time to develop and share some ideas; including formalizing this blog, taking it from a blog I called DrFuzzy to a something more business-like.  I opened a consultancy and announced my availability for both contracts and job offers.

Trusting in the theory, I engaged in simple conversations without agenda.  Searching for new colleagues; I made new friends, from Harvard professors to Silicon Valley entrepreneurs.  My long-suffering Bride trusted me and supported me every minute, but I could see the questions deep in her eyes.  I was not blanketing the capital city with my c.v., I was chatting on Twitter and blogging.  Not about my situation or needs, but about my ideas.  I even attended a “Tweetup,” my announcement of which prompted one minor media luminary to send me a private message, “What the F is a tweetup?”  I had coffee meetings with fascinating people – with no agenda other than “we should talk.” The conversations arose from shared ideas, and the lack of an agenda let us wander through fields of inquiry, often ending with nothing more than additional names and the promise of more coffee. I joined online sites that share itineraries, to learn when these new friends may be nearby.  (This led, in part, to several treasured in-person conversations with giants in my field.)

Still, I had a job interview almost every week.  And I landed contracts.  With one exception, each of these came directly from social media and colleagues/friends I had never “met” in real life.  In the end, I avoided bankruptcy, or any real disruption to my family.  I crafted an unpleasant Plan B, plotted the date that it would kick in, and threw myself into this experiment.  By the time that date arrived, I was well on my way out of my personal recession.

Using online social media tools, I stitched together a loose network of future colleagues and relationships to be tended.  Rather than broadcasting my increasingly urgent need for income, I trusted the network effect would work in time.

And it did.

Today I find myself engaged in meaningful and rewarding work to redesign a failed education system; working alongside leading professionals in innovation, public policy, and social change.

A year ago, I could not predict where I would be today.  Such is the nature of complexity and networks.  The theory suggested I should place myself in conversations, expand my connections into new networks, and a vocation would emerge.  (While I embrace the notion, I hope I never again have to conduct such experiments with my family’s financial health.)  I saw the traditional reaction to job loss as creating one-to-one intense conversations trying to match my talents to a company’s need.  Instead, I took this path.  Which amounted to no path at all, certainly not one any could predict.  To paraphrase Mr. Frost, that has made all the difference.

I want to thank all who I’ve met in the past twelve months, and commit to further conversation.  I have an obligation now to continue in the spirit of my late friend Melissie Rumizen, a ’super-connector’ soul whose greatest passion was creating friendships.  I am extremely fortunate, and have much to be thankful for this holiday season.  Thank you.

Nothing that is worth knowing can be taught. — Oscar Wilde

Let’s imagine a conversation at the close of the 19th century.  You and a team of designers are considering elements of the internal combustion engine that will, if successful, trigger a revolution in personal transportation and change the course of history.  In a conversation with team members, you are presented with a series of challenging questions regarding the use of a sparkplug.

“How do we know that’s the right design?  Where has this worked before?”

You are flummoxed because there is precious little evidence that you are on the right path.  You understand the principles of fuel and ignition, but you cannot demonstrate how the automobile will transform social structures and economies.  You are engaged in the new, and must resort to principles within known science rather than case studies.  You cannot predict how your creation will emerge and co-evolve in a new world, but in order to begin, you first establish some predictive rationale that lets you begin on a road that has the highest probability of success.

We who believe in systemic transformation for education are confronted with this challenge.  We cannot point to complete system exemplars, because the system we are encouraging does not yet exist. We instead develop principles of design that respect known science to the degree possible.

Let us take one of those principles, problematically titled “personalized learning.”  How do we know this is important?  Why the emphasis on learning, rather than instruction?  And why should the learning experience be tailored to the individual?    The first consideration when pondering how to help children learn should be to explore how they learn.  Fortunately, advances in neuroscience help us reconsider our approach to young minds, and answer some fundamental questions:  Are we born with a vessel into which knowledge is poured?  Or do we create our own mind?

Reviewing the science, we find that all learning is personalized.  Neuroscience, cognitive science, sociology, psychology, and philosophy agree – we create representations of our world based on individual experience.  No amount of instructional method can ensure an “accurate” uptake of information.  This is because you are designed to predict events in a complex world.  You do this by developing a consistent sense of the world around you, the memory of input patterns experienced from birth.  The infant brain is incredibly plastic, meaning it can change and rewire itself based on the type of inputs flowing into it.

When patterns appear familiar, you recall previous similar patterns and form a sense of the future based on them.  An intelligent human develops the ability to predict events in their environment, so that they may adapt themselves or elements of that environment to suit their interests and goals.

As the world is not a predictable machine, this means we do not develop complicated decision trees and Spock-like logic methods.  Instead, we explore, experiment, fail and learn about our world in physical and temporal context.  These learnings are shaped by individual experience, and are inherently intimate.  Our brains constantly create new structures with every new experience or piece of information – these structures are more specific to our individual humanity than our fingerprints or iris patterns.

You are designed to work with incomplete information.  The way you understand your world is through a combination of real inputs and memory.  You resolve ambiguity by continually filling in logical gaps based on learned patterns over time.  In conversation, not every word you hear is understandable out of context, rather, you predict the meaning of phonemes you hear based on the conversation itself.  This same principle applies when reading handwritten words – by themselves perhaps ambiguous, we resolve this by interpreting the context and resolving the meaning based on learned patterns. How does this work?

“Memories are stored in a form that captures the essence of relationships, not the details of the moment.  When you see, feel, or hear something, the cortex takes the detailed, highly specific input and converts it to an invariant form.  It is the invariant form that is stored in memory, and it is the invariant form of each new input pattern that it gets compared to.  Memory storage, memory recall, and memory recognition occur at the level of invariant forms.” (Hawkins, p.82)

You resolve ambiguous input data based on how you believe the world works. This is due to our memory structures, which provide for “invariant form memory,” a memory of input patterns allow for partial patterns to recall whole ones.  This is what occurs when you see a friend in the mall – catching just a glimpse is enough for you to ‘recognize’ her.  This is termed ‘invariance.’  If you see someone at a bus stop partially obscured by a sign, you ‘assume’ the rest of her based on previous patterns that assume whole humans.  This ‘filling in’ of details occurs at the most detailed sensory input, where the blind spot we all have near the center of our eye is accommodated by previous cognitive patterns.  At the top of the cognitive hierarchy, where higher order pattern matching occurs, you experience the same ‘filling in’ for missing details.

This is true from the simplest form – we don’t notice the blind spot in every human eye, but rather complete the image based on surrounding context – to the most complex, including how we make decisions.  One author, discussing the reality of intuitive or ‘recognitial’ decision-making, notes: “The basic aspect of recognitional decision making is that people with experience can size up the situation and judge it as familiar or typical.  Usually this assessment happens so quickly and automatically that we are not aware of it.” (Klein, p.89)

As a student is not passively absorbing what is provided, but rather continuously storing patterns and comparing them against a unique collection of invariant form memories – we see the student is already in control of the learning experience.  This is not new age fluffy thinking, this reflects the reality that embedded experience frames and shapes how we understand our world.

Preparing children to succeed involves acknowledging each child’s centrality to the learning experience.  We can choose to continue methods that are convenient to the adult, mass lectures or student ‘tracking,’ or we can provide a system that adapts to the individual minds in our care at every stage.  The science leaves us no option here – ‘personalized learning,’ by whatever name, is a central design principle for a transformed education system.

Sources

Deacon, T. W. (1997). The Symbolic Species: The Co-Evolution of Language and the Brain. New York, NY: W.W. Norton & Company.

Goffman, E. (1974). Frame Analysis:  An Essay on the Organization of Experience. Boston, MA: Northeastern University Press.

Hawkins, J., & Blakeslee, S. (2004). On Intelligence. New York, NY: Henry Holt and Company, LLC.

Klein, G. (1998). Sources of Power: How People Make Decisions. London, UK: The MIT Press.

Reprinted from a recent guest stint over at cognitive-edge.com

I’ve been titling the last few posts in terms of how context can shift over time.  This is not intended as a great reveal of some new management method, it just came to me as a recurring theme during this drive through Ireland.  How do we understand, or not, the great sites from ancient Ireland?  For some sites, such as the monastic cities of Clonmacnoise or Glendalough, the context is preserved through accidents of geography or the persistence of reverence – or because perhaps they are a mere 1400 years old, and not 6,000. Recovering context for historical sites is made difficult due – in some cases – to the layers of later civilizations, the absence of stories when sites lay undiscovered for generations, or when there are deliberate attempts to change the context – as in this example, the Rock of Cashel.

The Rock of Cashel was a seat of Irish kings for Munster, dating back to 342 A.D., and was for a time the home of the high king Brian Boru.  It was St. Patrick, however, who made the Rock of Cashel important in Irish history, and activities following his arrival all but erased evidence of the Rock’s use as a seat for pagan kings.  It was here Patrick baptized, in 432, King Aengus.  (One legend holds that Patrick accidentally placed his staff into the top of Aengus’ foot during the baptism, but the King said nothing  When Patrick asked him later why he didn’t cry out or otherwise indicate the injury, and the King replied:  “I thought it was part of the ceremony.”)

The cross of St. Patrick sat on the alleged spot where this baptism took place, and some believe the stone used as a support for the cross is the rock at which the pagan Kings at Cashel were crowned.  It is possible that this rock was repurposed to hold a Christian cross, honoring the evangelist Patrick.  (It barely resembles a cross due to erosion and weather.) The onset of the Christian era did not, however, repeal inter-clan rivalry among the Irish kings.

If you visit the Rock of Cashel, you will find Cormac’s Chapel, a small Romanesque building intended for the private use of King Cormac.  Even this structure was not left unchallenged.  Proving that inter-clan rivalry survived the onset of the Christian era in Ireland, the subsequent bishop was of a different clan, and apparently was not taken with the charming small Chapel.  A large Gothic cathedral was built directly across the front door of Cormac’s Chapel.

Finally, the Celtic cross is a good example of repurposed context.  The circle around the cross is interpreted by some as representing infinity, while its origin is undoubtedly the incorporation of something to charm the pagan, sun-worshipping Irish.  This reminds one of the repurposing of pagan holidays such as late December for use in Christian rituals, a deliberate changing of context in order to erase undesirable stories.