Modelling Change Management

For such a complex process, change management can be modeled mathematically using basic and remarkably simple assumptions, or rules.  The Nobel prize winner, Thomas Schelling as done so.

The sequence below shows segregation occurring where each red and blue dot is a “person” and they each want to live next to at least 2 neighboring dots of the same color out of their 4 total neighbors, the result of the simple and apparently moderate rule is total segregation after a period of time. What’s fascinating is that an apparently minor rule or constraint leads to this level of change.

Number of neighbors that need to be of the same color:

  • 1 neighbor – no segregation
  • 2 neighbors – complete segregation
  • 3 neighbors – complete segregation
  • 4 neighbor – complete segregation

Fine. So what does this mean for change management? The lesson is that very small changes in people’s behaviors and preferences can drive enormous differences in outcome.

You can see the animated sequence here (note: if it doesn’t work you need to have Quicktime installed.)

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See the full article from Atlantic Magazine here.

3 responses to “Modelling Change Management

  1. Simon,

    thanks for reminding us of this great work by Jonathan Rauch. Dynamic Systems like populations of people (or animals, or abstract entities) often exhibit complex behavior, including highly non-linear events like phase transitions (the “tipping” from one state or regime to another).

    Stephen Wolfram in his book New Kind of Science has reflected a lot about these phenomena using cellular automata as a good modeling paradigm.
    At a higher level one can discern classes of systems and their states which are different in their dynamics. There are static regimes where the entire system state is constant and either no change is happening or any induced change is quickly reduced back to the original state. There are periodic regimes where the entire system cycles through a limited set of states and keeps repeating this cycle forever. And finally there are chaotic regimes where there is no apparent order and small changes in one part of the system can quickly ripple through and change the entire system.
    One interesting aspect is the very limited ability to predict what such systems will do when at or near the chaotic regimes. Not because of limited or imprecise initial information. But because of their fundamental computational irreducibility. Simply put: To find out what the system will look like after N generations you have to run those N generations to see; there is no shortcut.

    Back to the change management context you brought this up in: “The lesson is that very small changes in people’s behaviors and preferences can drive enormous differences in outcome.” This is true, if the system these people operate in is near the boundary of a chaotic regime where such small changes can get amplified and ripple through the entire system. (The person protesting in Morocco by setting himself on fire last spring was such a seemingly small, local change that rippled through the Arab world in what was to become known as the Arab Spring.) However, if the system / company / bureaucracy etc. is in a stable or periodic state, even a lot of small changes will not make a difference and the system stays unchanged. I think progress would come from the ability to analyze systems at a macro-level and measure how close they are to dynamic / chaotic boundaries. Are you aware of such system analysis?

    Thomas.

  2. A good visualization of this concept of small changes possibly leading to big changes can be found in this 5min video: http://youtu.be/Qe5Enm96MFQ

    You may have heard of basins of attraction (to final states) and strange attractors. Nonlinear dynamic systems have an element of unpredictability, and the above video demonstrates this very nicely.

  3. Pavel Barseghyan

    Before managing changes in the systems of different nature, we must first describe the possible states in which the specific system can be. This can be done by using the equations of states. A classic example of such an equation is the equation of ideal gases in physics.
    This kind of equation can be obtained for an arbitrary project too, which serves as the basis for the derivation of the functional relationships between the parameters of the projects.
    Details you can find here: http://www.scribd.com/doc/113832236/Principles-of-Top-down-Analysis-of-Projects-Part-1-State-Equation-of-Projects-and-Project-Change-Analysis
    Pavel

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