Book Review: The Wisdom of Crowds by James Surowiecki

It's quite annoying to find out that groups make better decisions than you can as an individual - but a very powerful thought. However, the groups need to be diverse, independent and decentralised (so that may exclude a few crowds I can think of...)

We are essentially cooperative beings and meld our behaviour around that of others. One of the examples in the book is how crowds flow in shopping centres, how we move on staircases etc. We do all this even when there are no explicit rules. Mind you, as someone who regularly travels to the US from the UK I know what it is like to accidently move against this flow when I forget to walk on the "right". So maybe there are more socialised rules and logic than the author makes explicit.

One theme in the book which did appeal to me was the notion of the difference making a difference. While it seems inefficient, having a diversity of ideas seems to allow meaningful differences of ideas, especially at the early stages of decision making - and this is important. Groups also need to be able to distinguish good ideas from bad ideas and having more to choose from helps.

There is some helpful work about distinguishing the difference between situations which are easily defined by a single answer, such as the weight of an ox, and those which are more co-ordination problems, such as traffic manageemnt. Co-ordination problems come when all individuals want to go their own way. Here the wisdom of the crowd sound a lot like complexity science and simple rules and I wasn't totally convinced of all of his arguments. However, they were interesting.

A thought provoking book which much more to say that the few bits I have mentioned here.

The book's website is here http://www.randomhouse.com/features/wisdomofcrowds/ where there is a lot of detail and audio to download.

Model 7: data, information, knowledge, wisdom

I thought I would stop at 7 models on the data, information, knowledge and wisdom theme - afterall, the brain is supposed to be able to hold a maximum of seven thoughts at any one time.  I've left my favourite to last. I particularly like the examples given at each stage. You can read more about this model on this blogsite.

Model 6: data, information, knowledge, wisdom

The Liebowitz (1999) model of DIKW is helpful if you are looking for something with details to give you ideas on what you might do in your own work. I can see how this can be used to evaluate large scale programs. The value aspects are useful reminders of the underlying purpose in moving to each stage, and therefore gives an idea of what might need to be planned for in a large scale program to achieve each step.

Model 5: data, information, knowledge, wisdom

So let's start 2011 by continuing the data-information-knowledge-wisdom series of posts with something creative from Topicscape's Mindmap directory:
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I like the zone of potential - zone of possibility continuum as well as half the "effort" being below the surface which can only really be explained by doing a drawing like this.

Model 3: data, information, knowledge, wisdom

One of the QI refrains is "increase the capability and capacity of employees". While this is a great concept, easy to declare and impossible not to support, for me it lacks any concrete applicability. What exactly is meant by this? There is another one of our data-information-knowledge-wisdom models which may help pin down what might be meant. Next time you hear somebody say the capacity/capability thing then whip this model out and ask them to explain their intentions and expectations along the data to wisdom curve.

 The challenge here is to produce learning experiences that enable someone to move up the curve. In my experience, much of healthcare improvement work is focused on developing data based skills - how to measure change. Some people get to the information stage where they learn to look for patterns, say by using SPC charts. Can they port this knowledge to other projects in a predictable way? Can they make intelligent choices? To what extent do the participants on a QI project become "wise"?

The above curve comes from Designing Knowledge Eco-Systems for Communities of Practice.  The web resources are excellent - especially if you are developing CoP's as part of your QI strategy.

Model 2: data, information, knowledge, wisdom

There is an excellent post about wisdom on one of my favourite sites - Big Dog and Little Dog's Performance Juxtaposition (yes, really!).  It's a place I recommend you spend some time checking out.

I like the way this model gets me thinking about how we learn - there are connections here to the Honey & Mumford Learning styles.  The fact that there is a continuum for context is also thought-provoking.  This model has left me wondering whether in many of our quality improvement projects we focus too much on the bottom left hand corner and assume the progression to wisdom will be automatic. What would happen if we thought more and designed more of the journey to wisdom into our improvement interventions (and by extension, into our evaluations of projects)?

Model 1: data, information, knowledge, wisdom

The old adage goes along the lines that knowledge can be defined as knowing a tomato is a fruit, and that wisdom is therefore knowing that you don't add a tomato to a fruit salad...  There are a number of models and frameworks that investigate the data-information-knowledge-wisdom continuum and in the this series of posts I cover a few of these.

For the theorist a good place to start is with an online paper A Primer:, Enterprise Wisdom Management and the Flow of Understanding by ScottCarpenter@CognitiveCybernetics.com

I like the way environment and context have come into play as important factors in understanding that knowledge and wisdom have a contextual perspective.