Complexity, drivers and modulators in life and the law

This video is fascinating for a host of reasons. In particular, it illustrates a concept that is critical in my continuing series of posts about the legal ecosystem.

Patterns

The video shows an aggregation of anchovies — a shoal of fish obeying simple rules, but creating a constantly changing unpredictable pattern in the sea (just as a murmuration of starlings does in the air). What I find especially interesting is the way the fish react to humans in their midst.

Studies of fish show that they observe very simple rules when they shoal (as starlings do when they flock):

1. Move in the same direction as your neighbour;
2. Remain close to your neighbours;
3. Avoid collisions with your neighbours.

Armed with these rules, it is possible to simulate the behaviour of shoals. It is even possible to predict large-scale changes in behaviour (such as migration). What is impossible is predicting the precise pattern traced by the shoal itself as it moves through the water. There are simply too many variables to allow this — for all intents and purposes each pattern is unique for the fleeting moment that it exists. This can be demonstrated mathematically.

For any number of items (N), the number of links (L) between pairs of items can be expressed thus:

Likewise, the number of patterns (P) that can be generated by connection items can be calculated thus:

A simple table shows how the numbers of patterns grows exponentially as items are added:

Dots	Links	Patterns
N=4	L=6	P=64
N=10	L=45	P=35 trillion
N=12	L=66	P=73.8 quintillion

The number of possible patterns generated by hundreds of fish is inconceivable.

Drivers and modulators

We often refer to actions driving change in life and work. The word ‘driver’ suggests a direct and predictable link between cause and effect: if I do this, the outcome will be that — every time. Drivers of this type do exist, even in some quite complicated systems. When I turn the steering wheel in my car, I need the result to be predictable. It usually is, unless the system is broken or some other factor (ice or gravel, perhaps) has been introduced without my knowledge.

As the number of components in a system increases and the connections between them are loosened, the behaviour of the system become less predictable. However, when we see the outcome it often looks inevitable; we are persuaded by hindsight that it should have been predictable.

This can be seen in the video when people swim towards the anchovies. Sometimes the shoal just moves away from the swimmer. Sometimes it parts and rejoins beyond the swimmer. Sometimes it forms a ring around the swimmer. None of these outcomes was predictable, but they all appear to follow the same rules — the fish maintain a constant distance from each other and follow the course of their neighbours, but they keep a greater distance from the alien body (whilst not fleeing from it altogether).

Similar things happen in organisations. Patterns of behaviour might look fairly constant and predictable, but can be disturbed by significant events (a change of leadership, for example, or some external pressure). The consequence of that disturbance is unpredictable before it happens, but may look obvious afterwards — hindsight makes us think that it was inevitable.

It is at this point that the ‘driver’ fallacy comes into being. If we see a number of events that appear to form an inevitable sequence of events, it is natural to think that repeating the initial cause will drive the same outcome. If the outcome looks good, then we are likely to take the same initial action expecting it to result in the same outcome. This is the thought process that underpins so-called ‘best practice’ and books like Good to Great. These hold out a promise that success will follow emulation of others.

Dave Snowden has suggested that it is more appropriate to think of change in complex systems being effected by modulators rather than drivers.

Imagine that you have a round flat table and around that table are a series of electro-magnets. They can vary in strength and also polarity. Some you control, some are controlled by people you know and some appear to change at random. In the middle of the table are a lot of iron filings. Now as long as the magnets don’t change, the iron filings will form a complex stable pattern. However as the magnets fluctuate in strength the pattern changes. if some of them change polarity then change is sudden and drastic before a new stability emerges. At the same time some of the iron filings get magnetised in turn as they pass through electric currents, making the situation even more complex. I may not even be aware of some modulators until they suddenly come into play and their impact is seen.

The magnets in this case modulate the system. They interact with each other and with the system as a whole, they make it inherently unpredictable. Understanding what modulators are in play will help us understand emergent behaviour of the system, but not to predict its future state. Attributing cause to a limited number of dominant modulators (that is what I think people mean by drivers) is a mistake as the level of interaction is too much. I can build models to simulate the behaviour of the system, however simulation does not lead to prediction.

Modulating change in law

A common type of ‘driver error’ arises when two systems appear to have the same objectives and basic structure. If one of the systems appears to have a good way of achieving a particular outcome, it is natural to consider transposing it into the other system. This sometimes occurs in legal and political systems, when adoption of different approaches to similar problems from foreign jurisdictions might be proposed. It also happens in law firms and other organisations that are apparently similar in scope and purpose. Otto Kahn-Freund skewered the notion of transplanting between legal cultures in his 1973 Chorley Lecture, “On the Uses and Misuses of Comparative Law”. His view was that there is a continuum of actions ranging from the organic (rooted in unique cultural, social and political soil) to the mechanical. The closer a particular process is to the mechanical end of the continuum the more likely it is that a transplant will be successful.

I prefer the Cynefin framework to Kahn-Freund’s continuum. It is better rooted in theory, as well as being more subtle — the linearity of a continuum is an immanent flaw. I intend to explore that further in a future blog post.

Whatever explanatory tool one uses, it should be clear that some practices translate better between organisations than others. At the moment, there are a few practical changes that are fairly universally recommended to law firms as panaceas to help them ride out changes in the legal market. These include legal project management, process mapping, fixed-fees, and so on. Some of these will work for some firms, but how can we know at the outset which and why? The answer is that we cannot do so reliably. Instead, it is important to test things out. There should be clarity about how the experiment can be evaluated — what does success or failure look like? — and there should be a safe fall-back position in the case of failure. Anything else is wishful thinking.

An experiment might be fairly small-scale, but it can also be quite audacious, as shown in this video explaining what happened when wolves were reintroduced to Yellowstone National Park.

The sequence of events described here — leading eventually to changes in the physical environment — is unique. It is not possible to say that reintroducing wolves in other places would have the same effect. A number of other factors also played their part:

• The patterns of grazing behaviour of deer
• The topography of the park itself
• The availability of other species (beaver, coyotes, bears, birds, etc)
• The time taken for plant species to regenerate
• and so on…

So, for a law firm, introducing new ways of working or doing business might be a really good idea. The success of such changes depends on a host of components responding in particular ways. Beneficial outcomes are neither inevitable nor predictable.

Better outcomes arise from a process like this:

• An understanding of why particular changes might work (and knowing what ‘working’ means for your firm);
• Testing the change;
• Evaluating if it is working or not (by reference to the first step);
• If it works, continue;
• If it doesn’t, revert to the previous safe state.