Over the past few years we have collected data from a global mining company, engineering companies, high-tech services and local government and we have made some conclusions about a class of network called a ‘small world’. This phrase has entered popular culture and I am sure you have had several small world experience when a new work colleague from another town turns out be a married to a friend from school (or something like that).

As we have said before on this blog, this ‘small world’ experience has also been called ‘six degrees of separation’ and it has been puzzled social scientists for decades. Many years ago, a US sociologist by the name of Stanley Milgram popularized the six-degrees idea by getting people to send a letter to someone they had never met in another US city. The senders had to do this by sending the letter to people they knew who might know the final receiver. The amazing finding was that the average number of steps was a little over 6- hence the six degrees of separation.

The Milgram experiment has been repeated internationally with similar findings and the Kevin Bacon game works on the same principle. The fact that small worlds ‘work’ is a bit harder to explain. We don’t have that many contacts and we mostly prefer to be with people who are culturally or professionally similar to ourselves. This would suggest that the world is organized into distinct clusters of people and yet we have this idea that we are six network steps from anyone on the planet. Surely both clustering and few steps through the network can’t work together? Or can it….

One of the really interesting breakthroughs in network science over the past 10 years is an understanding of why small worlds work – and this has some pretty big implications for managing innovation (which I’ll explain later). Most of what follows here is taken from Six Degrees: The Science of a Connected Age by Duncan Watts. This is a really interesting account of networks and the six degrees problem and it’s very readable.

Watts introduces small world networks as being in between highly structured networks with organized clusters and networks with completely random links

The ‘regular’ sample network is highly structured with everyone connected to their immediate neighbors but count how many steps it takes to get from one side of the network to the other. On the other hand, the random network has few steps between anyone in the network but has no organization to it. The small world trick is that a few random connections across clusters in the network creates shortcuts and turns an organized (silo) structure into a small world. It turns out that this structure is very common in many social networks and this is a real map of a small world network of project engineers within a large multinational business. I have indicated a person who acts as a ‘connector’ by putting a red circle around them.

Collaboration Network of Engineers

Now, I suspect that many of you are thinking about the possibility of having organization structures together with an efficient way of transferring ideas and expertise. The small world network shows us that this is possible and there is a lot of good evidence that it can support innovation. I won’t go into great detail (and you can read a more technical review from Tim and me here) but my favorite evidence comes from the Broadway Musical industry. Using collaboration networks of score-writers, choreographers and librettists, this study showed that block-buster musicals were more likely to occur in the small-world environment.

So far, so good but can your world become too small and too connected? Another really interesting finding from the Broadway study was that too much small worldedness (e.g more clustered and less connected overall) correlated with decreased musical success. This is interesting and we think that there are two things going on here.

A highly connected cluster of people is like a goldfish bowl. Everyone knows everybody else and knows the same thing. Like a goldfish, talking around the network just makes us see the same things and hearing the same information. There is a very strong chance that group think and rigidity will set in when this network structure occurs. Another way of looking at it is that there is nowhere that different ideas can emerge.

The brokers in ‘hyper’ small worlds can get overloaded. When the clusters get too big and overall connectivity relies on a handfull of people they get overloaded or even use their position to selectively filter information for their own benefit. One study that Tim and I have done shows and engineering collaboration network that looks like a small world but the role of the connectors in that instance was to filter rather than pass on information. This small world network resulted in decreased collaboration performance on the project.

Small worlds can help innovation but look out for signs that groups are getting too inwardly focussed on their own work and watch for the overloading of connectors.

Thanks to Sam Macaulay for help with the network data.