Applying the Butterfly Effect to Organizations
Sensitivity to Small Nudges
Virtually all the organizational change models in use are rooted in the Industrial Era. However, applications from the study of complexity science are starting to impact our understanding of management, organizational behavior, and the theory & practice of organizational change. From this body of new thinking driven by complexity science, the focus here is on the Butterfly Effect within the context of organizational change.
The Butterfly Effect describes the potential impact small changes can have on a larger system as the impact ripples through a system of interconnected, non-linear (complex) relationships. The “butterfly effect” comes from the sensitivity to initial conditions where a butterfly theoretically flaps its wings and weeks later the cumulative ripple results in a tornado. These impacts follow a distribution curve where most shocks to the system have little or no impact on the outcome while a few may result in significant impacts when building upon each other. Each cause-effect relationship has a low probability, but with some “possibility” of the impact being magnified and promulgated as it ripples along.
For some historical context — the origin of the Butterfly Effect can be traced back to Edward Lorenz’s work on weather prediction. To save some time when rerunning a weather simulation, he entered 0.506 instead of the full parameter of 0.506127 (Wikipedia, 2015). This single insignificant change of 0.025% resulted in a completely different outcome from an earlier prediction. Upon review, this small shift in data had outsized results due to nonlinear equations and feedback loops in the weather simulation.
Organizational application
This brings us to the organizational complexity we face every day where “complexity” is framed within complexity science and not the general use of the term. Within this framework, organizational culture emerges from numerous interpersonal interactions across the organization, each driven by a personal agenda framed within the context of a shared understanding of their personal and collective purpose (mission). Most of these daily interactions reinforce the norm, but sometimes a chance comment or action goes viral, cascading to produce a new cultural component. However, promulgation across the organization is a bit like playing a child’s game of telephone — each iteration has the potential for meaning modification as each individual interprets the message within their personal frame of reference. If the new message “fits” it may be accepted and even gain credibility. However, some messages can start to challenge the norm — or they may just die out as most outliers do.
A more specific application can be found within organizational change. First, all change starts with one person giving voice to a problem or new idea that (somehow) gains momentum as the idea spreads across the organization. While most ideas will die early, a few will grow in significance. The challenge is nurturing a culture of idea generation (e.g., Kaizen continuous change) with sufficient context to understand the current situation and evolving needs for the future. Second, most change initiatives cannot be controlled (managed), but must be influenced (led) toward a desired objective. Further, it is not possible to know in advance which influences will have the greatest impact, so it is essential to test ideas to see which gain the most traction knowing that most efforts will fail to break through the background noise. Another part of this effort is sensemaking to provide some context through which change communication will be filtered and diffused. Change initiatives then involve both frequency of small interventions and working to increase the likelihood of message promulgation.
Thirdly, organizational change involves many people undergoing individual change, each with a different personal agenda. A single effort is likely to dampen out when pushed into an organization versus trying multiple, aligned ideas, building on those that gain traction. In this way, change initiatives require clarity of the objective and tolerance for the many ways progress might be established and sustained. We can only proclaim success once sufficient feedback loops establish a new norm for message maintenance thereby establishing self-maintaining messaging. This flip from stability is a point of bifurcation, another principle coming from complexity science. And finally, stable organizations can be thrown into total disarray by introducing a relatively small change somewhere in the organization. Having some response (resistance) can be expected, but not sufficiently predicted to mitigate or build a counter-response into the change plan up-front. Those implementing change need to sense disruptive responses early while the impact is still minor and react quickly (in any number of ways) before resistant attitudes can diffuse too widely.
For an interesting illustration — complexity can be demonstrated through very simple relationships that produce either stability or disorder, depending on starting conditions. It is easy to illustrate this shift between stability and disorder using a simple non-linear equation with a single feed-forward information loop. The link below is to an Excel spreadsheet that graphs an equation over time, using an incrementally altered starting factor each time the macro button is pressed. The spreadsheet has two suggested starting points and increment settings to illustrate sensitivity to initial conditions. Running this macro repeatedly shows that a stable pattern is the “normal” state with apparent ‘randomness” periodically appearing. However, we know that the results are not truly “random” since the equation alone fully predicts the outcome. This illustration differentiates “random” from a “chaotic” state. Yet, the sudden appearance of what appears as “random” behavior is similar to organizations suddenly becoming chaotic in reaction to a minor change to the organization. The chaotic behavior cannot be predicted but understood in hindsight.
Reflection
What small impacts (nudges) can you take to disrupt the organization dynamics that reinforce a problem you are facing?
Which isolated or infrequent behaviors are already evident in your organization that you can give further support with a nudge?
