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.