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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
About the Author
Deb Lavoy has been studying the dynamics, culture and technology of collaborative teams and knowledge transfer for 12 years, while working in product marketing and strategy for companies as diverse as AOL and Adobe. She is currently Director of Product Marketing for Social Media at OpenText.
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