You’ve been promised collective intelligence, but there’s more. Complexity is both the problem and -- if properly understood -- the solution.


Two Forces Compelling a Business Shift

So there are these two things going on. The first you’ve definitely heard of -- it’s the great reawakening of the white-collar and consumer world as their value and participation and voice are released from the anonymity of the command and control corporate model thanks to nifty new social technologies.

The second is about the exponentially increasing complexity of the world. Everything that touches anything sets off another thing and so on. Social is accelerating complexity and vice versa. The very best of us and even our technology are daunted by the challenge of understanding issues and taking action in such an environment.

This is why the future has become ever more unpredictable, and engaging in planning is generally a highly optimistic pursuit. [There’s a third -- all this reawakening stuff has nudged us to look hard at some things that had been left unexamined for too long, like leadership, collaboration and certain exploitative forms of capitalism, but that’s a different discussion.]

The Big Shift & The Path to Social Business Enterprise 2.0

In his classic work, "The Structure of Scientific Revolutions," Thomas Kuhn describes a period of “crisis” that precedes a scientific revolution. The crisis is a period where a field of math or science becomes dramatically more complicated, while yielding diminishing, incremental returns.

If John Seely-Brown and John Hagel are right, and the average Return on Assets has dropped by 75% since 1965, then we may be seeing an analogous crisis in business that leaves us ripe for a business revolution (they call it the Big Shift).

Business is changing to a new model -- Enterprise 2.0 -- both because people are demanding it AND because a centralized command and control model that uses process and efficiencies of scale to achieve superhuman feats has limits to what it can do.

A new model of applying networks of sensors and capabilities (people) onto complex problems to achieve uniquely human feats, can solve problems that hierarchies cannot. Calling it “social” business is missing half of the point. Business isn’t going “social” because it wants to hold hands and sing Kumbaya.

[Note - I’m officially, if temporarily, moving back to Enterprise 2.0. I dislike the term Social Business, and I’m taking my ball and moving on, until such time as someone finally coins a term worth using.]

Complexity Beyond Reductionism

Our gut instincts tell us, based on 400 years of Newtonian reductionism, that if only we work hard enough, with enough intelligence and discipline, nothing is beyond our ability to deconstruct it into its component parts. Everything can be understood by examining how those pieces fit together. Our notion of business and process design (among many other things) depend on this idea, but it is -- if not exactly wrong -- limited.

The reason strategy is hard, the reason R&D is hard, the reason marketing, support, sales, innovation, operations, design and gun control are hard is because they are multifaceted challenges that involve many interrelated, unpredictable, often external forces.

When you wade into address these challenges, adding and changing dynamics, they become even more complex.There is no definitive right or wrong answer, there is only better and worse.

These types of problems are often referred to as “wicked.” Wicked problems defy systematic, top-down solutions. Our Command and Control organizations have done many things well, but we are now entering an era dominated by the kind of problem they don’t do well.

There are well defined mechanistic processes going on within, but they are just a part of a story. The rest of the story is complex. The rest of the story needs a mesh of minds -- a complex system -- to address.

But complexity makes people nervous. Its not part of our basic education. By definition, complex problems are hard or even impossible to understand. BUT is understanding really necessary to progress? Surprisingly, no.

1. Knowledge-less-ness: We can find solutions to problems that we do not (and can not) “understand”

There is a field of math/computer science known as Computability Theory (I hovered here as an undergrad). It’s the study of how to solve logical and mathematical puzzles, how to classify these puzzles into different types, based on their difficulty, and how to rank solutions by how efficient they are. Solutions usually involve clever methods for changing a hard problem to look like an easier problem.

There is a class of problems, however, known as NP-Complete. (You do not care what that means for the purposes of this discussion, but Wikipedia has a decent definition if you really must.) Let’s just say that these problems are the ones where you can’t find a nice tidy algorithm for efficiently solving the problem.

One of the canonical examples of NP-Complete problems is known as the Traveling Salesman problem. [You can tell I took a math course in college because I can say “canonical example.”] The traveling salesman problem goes like this. A salesman has to visit each of a list of cities exactly once. What is the shortest path he can take through them?

It turns out that it is impossible to simplify this problem using traditional analytic methods, and the only way to find out the optimal answer is to try every possible combination of cities, check how long it takes, and pick the shortest. This is no big deal if the number of cities is 5 or 25, but the effort required to solve the problem grows exponentially as the number of cities grow. We have no clever solution.

So I’m eyeball deep in this stuff, and I read an article -- a little “Computer Recreations” column in Scientific American by A K Dewdney, describing Flibs. It was a mind-altering, possibly life-altering article.

Flibs solve the traveling salesman problem. Easily.

A Flib is a random list of the cities and their distances. What you do with them is cool. You generate a bunch of flibs. And you can check how long the distance is if you visited the cities in the order listed in the Flib. And then you can take the best 10 or 25 percent of the Flibs and exchange their front and back ends -- like a gene might do -- the first 10 cities of Flib 1, and the last 20 cities from Flib 2, etc. And you score this new generation. Or you can create some random “point” mutations, or other schemes for mixing up the good performers, score them again and voila: within a very few generations, you have a very good answer. Is it optimal? It's very close, and takes a tiny fraction of the effort to get there.

The Flibs solution did not require a clever trick based on insight into the intrinsic properties of the problem (graphs). It solved the problem in spite of the fact that it was impossible.

Why does this matter? Genetic Algorithms are an example of how networks of agents can solve impossible problems.

Most business and social problems are “impossible” -- they were always difficult but the pace, competition, expectations and interconnectedness of things has changed the game. But that does not mean that we cannot face them -- if we get over the fact that we can’t “know” the answer, but we can discover it.

It suggests that our newly networked workforce may be able to tackle some impossible problems. We know they can -- we see it, but we can’t really describe it, and we’re struggling to understand how to promote it.

2. The Bar problem: We can develop stupid, but correct, solutions to impossible problems

I spent some time in the 90’s actually getting paid to experiment with agents and complex adaptive systems. We did clever little custom apps for FedEx, AT&T and the government.

Agents, in this sense, are just little objects with a little bit of state information and some rules they can follow. They are the simplest possible things. [If you’re worried you don’t "get" them, its only because you aren’t used to things being so simple. It's a common problem in math -- we seek meaning where there is none, and think we’re stupid because we don’t get it. Or maybe that’s just me. Moving on...] So agents are little objects, consisting of a few numbers and a few rules of the “if agent 5 is blue, turn red” sort.

Around this time -- the mid-90’s -- the “Bar Problem” emerged as a classic example of how complex adaptive systems can solve hard problems. It goes like this. If less than 60% of the population goes to the bar, then it's boring, and everyone would have been better off staying home. If more than 60% of the population goes to the bar, it's too crowded and they would have been better off staying home.

Each night, everyone in “town” must decide if they are going to the bar. They need to decide at the same time and with no knowledge of anyone else’s decision. So how do you decide to go to the bar or not?

You can simulate this problem very easily. It's fun, if you’re into that kind of thing. It goes like this. Create 100 (or 200 or 3,586) agents, and give them rules to follow for deciding whether or not they are going to the bar that night. The “rules” are completely random. Like if your age -- the day of the week is even, then go. Or if you went yesterday, don’t go today. Or if the bar had the right number of people six days ago, go.

If you have enough rules to provide some variety -- so not too many of the agents are using the same one -- within a couple of “weeks” you will always have the right number of people (agents) in the bar. Agents, using completely random criteria, will figure out the right answer.

It's a bizarre but true type of equilibrium, wrought by “agents” following completely random rules. That is rules that are not intentionally built to represent any knowledge.

Why does this matter? It suggests that anything will work -- anything that you execute with learning built in. It's why Amazon and Google work -- they have amassed decades of experimental “do something, measure, and clinically iterate toward ever better” results. They’ve learned many things, but they’ve learned by asking questions, and measuring results. Apple has also learned, but they’ve learned by setting sights on a perfect vision and heading only there.

These both work. So if you “know” go forward. If you seek, also go forward. Action is its own reward.

Social and Complexity and Business: A New Ideal

Our 20th century ideal organization is the well-oiled machine. Can we make the 21st century ideal organization a complex adaptive system?

This idea has been implied in lots of writing in this space, but I want to make sure it’s crystal clear. People are agents. Organizations are (should be) complex adaptive systems. Of course, the pathetically simple agents that we “Flibbed” and “Barred” can’t hold a candle to the magnificence of a human network. But we may be able to think of human networks as a kind of a complexity calculator. In 2013 we will begin to learn how to wield this “wicked” weapon against complexity.

Complex business means we should be able to access not only collective intelligence, but emergent intelligence -- solutions that emerge from complex systems, without residing in any part in any individual’s head. Emergent outcomes -- the ones promised by social collaboration, social marketing and social in general -- are not just a hope and a prayer, but real. Trusting in them is not foolish but wise. Our human networks, thoughtfully connected, with some smart methodologies will help us to apply complexity to complexity and make progress against now-intractable problems.

Enterprise 2.0 is not about social, it is about thinking very differently about what is hard. About what is impossible. About what IS possible. About your role in it, and about how a human chorus of intellect can help.

Enterprise 2.0 will measure outcomes dispassionately (with equipoise) as a way to ask questions without assigning blame. It will focus on learning as innovation, and disentangle accountability, blame and outcomes. It will depend on the connected circulation of insight and information of a network, often knowledge-less solutions, and the deepest respect for what people can and will bring to the table, given the chance.

Our mission in the next handful of years is to seek to understand these issues better, so that we can build organizations that can look complexity in the eye and not blink.

The best is yet to come. 

Editor's Note: Hungry for more of Deb's thoughts? Read Steve Jobs Did NOT Predict the Future. He Invented It