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Emergent Patterns

Identify and exploit Emergent Patterns

Emergent Patterns

Emergent Patterns - Self-reinforcing feedback Emergent Patterns - Clustering and connections Emergent Patterns - Priming-variable threshold Emergent Patterns - Preferential attachment
Find and interpret non-linear patterns to identify leverage points. We can create high impact and save much wasted effort and energy if we understand the emerging patterns and dynamics. A core group of four patterns occurs in many complex situations in organizations and business.

Interpreting our observations in complex and evolving situations is often a significant challenge. Especially because much of the critical cause and effect behavior in such situations is non-linear. The impact of a change is not proportional to time and/or effort. Sometimes, the impact may be limited for a considerable time and then, without warning, it may accelerate rapidly. Alternatively, a significant and growing initial impact may inexplicably stabilize or even decline. In such circumstances, learning based on simple historical observation of cause and effect is inadequate and misleading. In the example of delayed impact, learning based on historical cause and effect would incorrectly suggest in the early stages that the effort is ‘not working’ and that the design or plan should be abandoned or changed.

The question is: how can we tell the difference between a non-linear response, resulting in delayed impact, and a ‘failure’ response in which the cause will not achieve the desired impact irrespective of the time or effort? Similarly, if the preconditions exist for a non-linear response, it is possible to influence that response, either to promote it, if it is desirable, or to suppress it, if it is undesirable? In complex and emergent situations we need to have a sound appreciation of the types of non-linear patterns that may emerge and how to work with them.

Self-reinforcing Viral videos (or other forms of social media) are an example of self-reinforcing feedback (sometimes called the ‘snowball effect’ or ‘virtuous/vicious cycles’). If a video is sufficiently engaging, those who watch it will share it with their friends. There is a good chance the friends will also enjoy it and share it with their network of friends, and so on. Similarly, the more hits or ‘likes’ a video gets, the more it moves up popularity based ranking lists which will, in turn, stimulate more hits and ‘likes’ that then drive it further up the ranking lists. So we see at least two positive self-reinforcing feedback loops driving the success of viral videos. One based on awareness generated through social media and the other based on attention generated by rising up ranking lists. Because of the multiplier effect each time around the self-reinforcing loop, rapid increasing (non-linear) growth occurs.

Self-reinforcing feedback occurs when a result or impact causes something to happen to further increase the result or impact further. The increased result creates even more feedback thus increasing the result even further. If there is no intervention, this feedback creates rapidly accelerating change. Self-reinforcing feedback will drive accelerating change until the feedback structure is disrupted, a resource constraint limits further growth or some form of catastrophic failure occurs.

The saying “success breeds success” is a statement of self-reinforcing feedback in which the more success you have, the more resources you have to invest in more success, the more others will have the confidence to invest in you and your initiatives, and the more self-confidence you will have to attempt a more challenging initiative. The “success breeds success” example demonstrates two important aspects of self-reinforcing feedback in complex situations. First, often the reinforcing response is not guaranteed but is a high probability. In the “success breeds success” case, one or more subsequent initiatives could fail thus limiting the resources for and confidence in any future initiatives and interrupting the self-reinforcing feedback. Second, the feedback may operate in progressive regimes. For example, early “success” may be so small that it is barely differentiated from other events and therefore has little or no impact on the availability of discretionary resources, or the confidence of others in you, or your self-confidence. It may be that not until your success has reached a certain threshold that self-reinforcing feedback kicks-in.

Clustering The iPhone is a great example of ‘clustering and connections’. Apple created breakthrough innovation in the initial iPhone by bringing together separate technologies that had been independently developed and, to a large degree, refined elsewhere. These include multi-touch technology that it obtained through the acquisition of Fingerworks, Corning’s gorilla glass technology that Corning had developed years earlier for automotive applications but shelved because it was too expensive, expansion of the iPod music store concept to incorporate applications, modification of the OS X operating system from its personal computer business, and the mobile email concept from Blackberry. The separate functionality and value of each of these was greatly multiplied when they were clustered and connected in the iPhone. Similar clusters and connections can be found in the development of innovations such as the airplane and mass-produced automobiles.

Emerging clusters and connections need not be on such a grand scale as the examples in the previous paragraph. Clusters and connections can emerge when we attempt to create change in organizations. We might observe clusters of support or resistance starting to develop, and as the clusters grow the support or resistance may start to feed on itself. The challenge is to identify early the formation of these clusters and work with them to promote the growth of support or to address the causes of the resistance.

When we see clusters and connections start to appear in complex and emergent situations, it is often valuable to explore what is the driving force or influence that is bringing the elements together. By understanding such driving forces or influences we get a better appreciation of how the situation may develop or evolve. They are also an indication of how and where we will get the most leverage if we attempt to influence the situation.

Priming In some situations we see a lot of activity with little or no apparent results. We might be working hard to create a more cohesive and productive team with little success. Our efforts to implement an organisation-wide initiative to become more customer focused may not be showing any significant results. The lack of results may be even more surprising because we have been successful with similar initiatives in other circumstances. Why does this happen? Why are we unsuccessful despite all our efforts?

In these situations we may be looking at a ‘priming with variable threshold’ pattern. In this pattern, there is little or no response until a threshold achieved. The threshold level is not fixed, but is dependent on context and history. For example, if our organisation has a history of poorly conceived and implemented change initiatives, may of the employees will be justifiably skeptical and will need more evidence of the merit and value of the current initiative before jumping on board.

The ‘priming with variable threshold’ pattern occurs in many situation where there is an attempt to exert influence and persuade. The influence accumulates until a response is triggered. (The influencing effect may also degrade over time.) However, the impact of the influence (its leverage) is dependent on the credibility, reputation and trustworthiness of the influencer. The more credible and trustworthy the influencer, the higher the leverage of their influencing efforts. We see this pattern at work in many managerial situations. Credible and trustworthy managers seem to achieve results effortlessly while managers that are perceived to be less credible and less trustworthy seem to struggle to motivate and influence their team.

Pref attach Imagine that you are in an unfamiliar area of the city looking for a restaurant to go to. You are likely to be drawn to well attended restaurants on the assumption that the level of attendance is an indicator of quality and value. This in turn increases the attendance and so increases the likelihood that other casual restaurant goers will select the restaurant, and so on. A similar situation can be seen in the migration of the people to cities. The larger the city, the more likely that it will have facilities and services that align with the desires of the migrants. The additional population generates further wealth and demand for facilities and services which, in turn, will grow the quality and range of facilities and services available, and thereby be even more attractive to migrants. This pattern in known as ‘preferential attachment’.

Preferential attachment occurs when the probability of a new entrant to a situation attaching itself to given structure among a set of optional structures is dependent on the size of the structure.

Library of Examples

Self-reinforcing feedback
Clustering and connections
Priming – variable threshold
Preferential attachment

 

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