Collaboration adds simplicity in a complicated world. 

Simplifying through collaboration is the topic of a Ted talk by Yves Morieux, embedded below, in which he gives us 6 rules to simplify work. Watch the talk to get the context, but here are his 6 rules (with more explanation here)

1. Ensure people in the organisation full understand what others really do. 
2. Look for cooperation – reinforce managers as integrators, by removing layers so that managers are closer to the real work. 
3. Empower everybody to use their judgement and intelligence. 
4. Create tight feedback loops that expose people to the consequences of their actions. 
5. Increase reciprocity, by removing the buffers that make us self-sufficient.
6. Reward those who cooperate and blame or sanction those who don’t cooperate. 

Do these sound familiar? Rules 1,2,4,5 and 6 are all components of Knowledge Management, and Knowledge Management is needed to support Rule 3. Yves is not reinventing KM, but showing how a knowledge enabled, connected and collaborative organisation is like an adaptive nervous system rather than a rigid skeleton of roles and structures.

Yves explains that as these 6 rules are brought into play, organisations begin to be able to manage complexity not through adding more and more complex structures and requirements, but by allowing people to take ownership of issues and sort them out together, rather than passing them on to someone else.

Yves finishes his talk by explaining how CEOs can help support the 6 rules of complexity, and gives us this story, which should resonate with all KM practitioners

The CEO of The Lego Group, Jorgen Vig Knudstorp, says, blame is not for failure, it is for failing to help or ask for help. This changes everything. Suddenly it becomes in my interest to be transparent on my real weaknesses, because I know I will not be blamed if I fail, but if I fail to help or ask for help.

This is very similar to Elon Musk’s email to his staff. Both give the vision of an organisation empowered and obliged to seek knowledge from wherever it may be found. And this is the basis of Knowledge Management. 

There is a step in the maturation of communities of practice when their focus shifts from knowledge sharing to collaboration

Collaboration is an unnatural act in humans.

We are tribal animals, and all our instincts lead us to see life in terms of “us and them”. When we divide people into teams in our “Bird Island” experiment, for example, each isolated team naturally starts to compete against the others without any prompting from us.

The famous “Eagles and Rattlers” experiment showed how this competition, when strengthened, began to lead to destructive behaviour. We can see this behaviour in any society driven by competition for limited resources.

This is often the case in business. Divisions are in competition for budget and people, and as a result the familiar organisational silos emerge and strengthen. The Eagles/Rattlers experiment demonstrated that traditional forms of team-building – social events, movies etc – did not break these silos. Something different is needed.

Communities of practice can begin to break these silos. Initially Communities operate as a self-help mechanism, where people in one silo raise a question which people in other silos can answer. Through principles of reciprocity and “what’s in it for me”, knowledge begins to flow through the communities.

However there is a radical step in Community behaviour, when they start to focus, not on value to the individual, but the value they collectively can generate. In our Community Maturity model, this is the step from 3 to step 4 on many of the key variables, and is where communities move from being a mechanism for knowledge-sharing to a mechanism for collaboration.

Let me explain why this step is so important, by going back to the “Eagles and Rattlers” experiment.

In this experiment, the groups of boys in an American summer camp were divided into hostile tribes by giving them competitive tasks. However the experimenters were able to turn this around completely, and to develop a massively collaborative culture, simply by giving them collective challenges that each group could not solve on their own.

Simple step, massively powerful outcome.  The way to break silos is to give challenges no silo can achieve on their own.

The same thinking can be seen in the “T-shaped Manager” approach. Give managers collective targets, and they cease competing internally.

Communities of practice can take this step almost as an evolutionary process. I remember a Community meeting many years ago, when someone stood up – eyes shining – and said “guys, just think what we would accomplish if we all worked together on this. I bet we could cut costs by 50%!”

This is where a CoP starts to think less about one individual or team solving the problems on another, and more about pooling everyone’s knowledge to make a step-change in collective performance.  That’s when a sense of true collaboration begins within the Community – something that previously might have felt unnatural.

That’s when you can hear the sound of silo walls collapsing

The Polymath Project is a collaboration among mathematicians to solve important and difficult mathematical problems by coordinating many mathematicians to communicate with each other. The project uses a blog, to manage conversation, and a wiki to build the solution. Mathematicians of all seniorities take part, the result is truly collaborative, and several papers have been published under the pseudonym D.H.J. Polymath.

A recent paper discussing the solution of the 8th problem to be solved by the Polymath community contains an interesting couple of paragraphs by an American Undergraduate maths student, Andrew Gibson. Andrew and his classmates are not yet experienced enough to contribute to the project, but they gained valuable knowledge and insight through lurking and observing.

As Andrew explains

“Shortly after Zhang announced his result and you (Tao, the coordinator of the Polymath community) proposed the project, my classmates and I began a small, weekly seminar with a professor devoted to studying some of the theory involved (analytic number theory, sieve methods, etc.), albeit on a much more elementary level that was within our reach.

Of course, the majority of the actual proof is still mostly over our heads but at least I feel as if I’ve gained a bird’s-eye-view of the strategy and, probably more importantly, how it fits into the larger field. (For instance, before any of this, I could never have explained the Bombieri/Vinogradov theorem or the Hardy-Littlewood prime tuple conjecture.) So for us the project was a great excuse to enter a new subject and has been immensely educational. 

More than that though – reading the posts and following the ‘leader-board’ (blog) felt a lot like an academic spectator sport. It was surreal, a bit like watching a piece of history as it occurred. It made the mathematics feel much more alive and social, rather than just coming from a textbook. I don’t think us undergrads often get the chance to peak behind closed doors and watch professional mathematicians “in the wild” like this so, from a career standpoint, it was illuminating. I get the sense that this is the sort of activity I can look forward to in grad school and as a post-doc doing research (…hopefully). I also suspect that many other students from many other schools have had similar experiences but, like me, chose to stay quiet, as we had nothing to contribute. So, thank you all for organising this project and making it publically available online”.

I love the bit about  “academic spectator sport” and “watching professional mathematicians in the wild”.

This is the benefit of a community deliberately “collaborating out loud” to an audience of more novide members – they get to experience the way the experts think and the way they collaborate on solutions. It’s an intense learning experience for the lurker.

Collaboration is not always helpsful, and there are cases where it actually reduces your chance of success.

The ideas in this blog post are from a very interesting paper by Martine Haas and Morten Hansen, who look at success data from bid teams to find out when collaboration actually helps performance.

They looked at a series of bid teams, assessed how much they accessed documents from previous bids (which they called “codified knowledge”), and how much they received advice from experienced colleagues outside the team (“personal knowledge”). They then looked at bid success rates, to give an objective measure of the VALUE of the knowledge to the team.

Now, we might assume that the more Knowledge a team accesses, the better their performance?

Unfortunately it is not as simple as that.

Results of the study

The graphs shown here are the authors’ conclusions about how much in knowledge helps to improve bid performance, in varying circumstances. In each graph, the vertical axes represents increasing bid success probability, the horizontal axis represents increasing amount of knowledge used, the black line is “codified knowledge” (reuse of documents) and the purple line represents “personal knowledge”. If the lines rise from left to right, then increased knowledge is linked to increased chances of success. If they fall from left to right, then increased knowledge is linked to reduced success. Read the paper to understand the evidence behind these.

The top left graph (2i) represents a team which is inexperienced (and so has a high need to learn), working in a situation where they do not need to differentiate the bid significantly, so can deliver a fairly standard proposal. In this case, the more knowledge they use, the more documents they copy and the more experts they refer to, the better their chances of success. Here collaboration is helpful.

The top right graph (2ii) represents a team which is inexperienced (and so has a high need to learn), but are working in a situation where they really need to differentiate the bid. Here it is a great idea to get input and knowledge from experienced colleagues, but the re-use of documents from previous bids is actually harmful to the chances of success. So here the right collaboration is helpful.

The bottom right graph (2iii) represents a team which is experienced (and so has a low need to learn), and who are working in a situation where they really need to differentiate the bid. Again, it is a great idea to get input and knowledge from experienced colleagues, but the re-use of documents from previous bids is actually harmful to the chances of success. Again the right collaboration is helpful.

The final graph at bottom left (2iv) represents a team which is experienced (and so has a low need to learn), and who are working in a routine situation where bid differentiation is not needed. In this case, they pretty much know what they are doing, and re-using any knowledge does more harm than good. Collaboration is harmful.

So what’s the conclusion?

The conclusion is that collaboration, the re-use of documents or seeking input from others is not always going to help you, and in some cases it can hinder.

In most cases (3 out of the 4), the more input you get from colleagues the better, but also in  most cases (3 out of the 4), recycling documents from other teams will not help you perform better, and may even harm your chances of success.

So know your context, and choose a collaborative method that will actually help, not hinder. If you are experienced, and dealing with routine work, collaboratiopn may be a distraction you don’t need.