In Search of a Collaboration Algorithm

[An algorithm is a sequence of computational steps that transform input into output.] *

For some time now I have been convinced that the future of sustainable social and economic enterprise will be in small entities that are better connected, rather than large entities who are rich in structures and infrastructure. Rightly or wrongly, I believe that the focus will be on quality and resilience of those connections which in a large part could be developed algorithmically. At this point this may seem a bit outré, however we are going to witness new forms of organisations (companies, governing bodies, social groups and communities) that, while in the present may seem radically different, in the very near future will be entirely natural; almost in the way that democracy is normal and the feudal system of the middle ages is not.

It is in this context that the collaboration culture makes sense: i.e. its pervasiveness. I have already hinted at this in one of my earlier blogs, a two-part post on collaborative cities. However a recent Forbes article about Amazon’s current focus on a predictive shopping strategy, or as they call it ‘anticipatory package shipping’, had me thinking more about the broader development of a collaboration algorithm and its omnipresence. This is all critical for a number of reasons that go beyond a focus on collaboration; i.e. the future of jobs, and the future of economic and social life as we know it.

Binary Background


For now I will stay focused on my topic; is a collaboration algorithm something that should be pursued more ambitiously? And, if so, what would it look like and could it further enhance collaboration and give it a competitive advantage? And could then an algorithm applied in a collaboration setting also enhance the already known advantages it brings to innovation?

The role of algorithms in some areas such as policing has been well documented and discussed. Policing programs such as Crush [Criminal Reduction Utilising Statistical History] have been both praised and criticised. However, praise for its outcomes in reducing crime have left many unimpressed as they saw science fiction like that shown in the film “Minority Report” arrive much too fast to allow us to adapt. Some have pointed to the risks associated with the obsession with algorithms as a tool that can predict human behaviour. As one critic pointed out many years ago; “In cases of uncertainty, humans will tend to anchor on the first substantial piece of information they get and any new information that contradicts this initial idea is given less attention than it merits. This is the theory of anchoring bias.”  While the debate about the use of algorithms in predicting human behaviour is not really a debate for many, it is clear that new technologies are certainly allowing it to be considered as a serious tool with massive potential.

One area that I feel could to some extent be enhanced is the way we recognise potential for collaboration and the way we can better drive multiple collaborations across a range of industries. Algorithms are a powerful tool when the level of input data is high. This is largely a problem at present as collaborations are not well documented. The documentation of collaboration is paramount and the old adage of ‘you can’t manage what you don’t measure’ certainly applies here.  But, for the sake of argument, let’s think about what collaboration would look like if we collected and shared data? One notion is that data collection through collaboration could assist in reaching innovative solutions that normally take a long time to become apparent. We could further explore this by examining how the existing method of collaborative filtering works. Collaborative filtering has been described as ‘the lifeblood of the social web’ because its ‘mechanism is used to filter large amounts of information by spreading the process of filtering among a large group of people’**. Amazon and iTunes are among the well-known users of this method which helps them in making recommendations to customers.

One does not have to be a creative specialist to imagine the many potential uses of algorithms in collaboration, provided that an enterprise has a good collaboration strategy, governance and culture in place. These factors are in fact the basis on which data collection would rest as the first step towards building an algorithm that would enhance the collaboration process. I can recall some examples in long term collaborative projects that I designed and managed where it would have been helpful if there was an algorithm at hand. Having said that, an algorithm would not replace the necessity for other features of collaboration such as relationship building, but it would alter it to be based on more strategic focus in the process of collaboration.

Another possibility for a collaboration algorithm would be the potential for it to significantly assist in managing any risk associated with collaboration. Adjusting to emerging or disruptive trends would be much easier if an algorithm was in regular usage among collaboration practitioners. It is important that we do not confuse data collection and sharing in collaboration with algorithms. A good algorithm that focuses on collaboration as a business strategy would ultimately depend on the overall business model. In my view this reveals one thing; are we not a bit stubborn in neglecting the inevitable changes that algorithms bring?

*Cormen, Leiserson, and Rivest



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