Intended for healthcare professionals

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Education And Debate Complexity science

Coping with complexity: educating for capability

BMJ 2001; 323 doi: (Published 06 October 2001) Cite this as: BMJ 2001;323:799

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A scientific method of discovering 'simple rules' in a complex system

Recent articles about complexity theory applications are of great
interest to us as, like Plsek & Wilson, Fraser & Greenhalgh, we
believe complexity science has important benefits in the management of
organisations. Only in the last few years, however, have we had the
computing capacity and skills to build robust models of social
organisations. This enables us to simulate real events in real contexts
through a dynamic model, calibrated against real data.

However, not surprisingly, few social organisations use this kind of
modelling yet - it's very new science (a hard one, based on mathematical
and computational modelling), so there are few people with the skills and
experience to do it.

As these recent BMJ articles say, complexity science does have real
relevance for the NHS. But what's the nature of this relevance exactly?
The articles aren't 100% clear. Plsek & Wilson present the need to use
simple rules to run the system, yet give no real hint as to how to find
those simple rules, and how to differentiate between simple rules which
work and those which don't. This is exactly where mathematical and
computational modelling, firmly grounded in complexity theory, can provide
a precise, exact relevance through clear identification of what the simple
'rules' are that are creating seemingly intractable problems, and can make
valid, tested recommendations about what the new 'rules' are to mitigate,
or even occasionally eliminate the identified problem.

There are many successful examples (in many different fields) of
this: For example, the multi-agency Nasdaq wanted to know what would
happen when the SEC changed the rules of the game and 'decimalised the
markets'. The Bios Group, an consulting off-shoot of the reknowned Santa
Fe Institute, built a scientific model of the market and developed 6
precise predictions of what would change in the behaviour of the market as
a whole. Of these, 5 have come true.

The robustness of these intractable problems, called 'emergent
phenomena' in the literature, are a feature of all complex adaptive
systems. What Bios, the Santa Fe scientists and people like Brookings'
Axtell and Epstein do, is create a different, equally robust set of
outcomes, based on a tweaking or changing of the 'rules'.

But what rules should you be changing? Moreover, too, in the real
world, you might not even 'see' the problem, even though you're mightily
concerned about an aspect of the system (waiting lists, a week of a flu
crisis every year, for example!) Where does one start with some of the
hard problems the NHS is facing? Are managers indeed asking the correct
questions? No amount of brainstorming, ideas generation, hard work by
dedicated staff or collecting of data will tell you.

But models and simulations will let you design many new simple
policies, and test them over and over again within the model. Running lots
of 'what-if?' scenarios through a calibrated model allow you to identify
which 'rules' produce which outcomes; i.e. those 'rules' which you want
embedded in the system so x,y,z happen instead of a,b or c.

It's like this: before this kind of modelling were possible, who
would've thought you could reduce Southwest Airlines overnight cargo
transfers by 70%? (as described in the Harvard Business Review article
Swarm Intelligence last May) Once the model was built, (in this instance,
Bios scientists used agent based modelling), and explained the results,
someone could then come along and retrospectively kind of explain why the
simple rules put in worked. But there were equally simple rules there
beforehand which didn't work.

In conclusion, scientifically valid and replicable methods for
discovering 'simple rules' within a complex system do now exist. Their
application will back up what many NHS managers are talking about, and
provide them and others with scientifically robust examples for the

Competing interest: Eurobios UK is a consulting company applying
complexity science to business and organisational problems -

Competing interests: No competing interests

10 October 2001
Kate Cooper
Eurobios UK