Formula for predicting CHANGE

How can we predict is a specific change will in fact occur and to what extent? Here is an equation to help you understand various dynamics involved and foresee what is likely to happen.


Let’s brake this down

Change is positively / proportionately effected by the following factors:

  • Severity of Problem: How large the problem is / How severe its impact is / How much pain it brings
  • Clarity of Solution: How clear is the solution / How clear are the steps involved / How clear who is to play which role
  • Personal Motivation: How relevant and important is the problem to people involved in resolving it

Change is negatively / inversely effected by the following factors:

  • Complexity of Ecosystem: How complex is the environment needing change (e.g. due to bureaucracy, corporate structure, management layers, governance, etc.)
  • Time & Effort Required: Size of effort required to resolve the problem / Amount of time it takes to roll out a solution
  • Stagnation Constant: Refers to the negative impact of the solution being delayed eventually causing stagnation. Think of this number increasing over time. If resolving a problem in a specific way takes too long, people either accept it or find other ways to bypass it.

Let’s use some examples

First let’s use “Solving World Hunger” as an example to demonstrate how this plays out.

Example A: Solving World Hunger vs Malawi Hunger

Obviously Malawi is a smaller ecosystem and requires less effort to address hunger. Thus it would be easier to achieve more significant change that hunger on a global world scale.

Let’s use another example, perhaps something more personal like wanting to loose some weight or quitting smoking. Here is a way how we can understand and predict whether we will be successful in achieving change in these areas.

Example B1: Loosing 20 LB Weight & Quitting Smoking

Let’s go one step further and assign numbers to these variables (1 to 10 with 10 being the highest) to make them more meaningful. These are purely hypothetical to demonstrate certain points.

Example B2: Loosing 20 LB Weight & Quitting Smoking (with scores)

In this scenario we can quickly see that we are more likely to loose weight (Ratio is 3:2) and we are not likely to quit smoking (Ratio is 49:54 / less than 1). We can improve our odds of quitting smoking if we are able to change any of these factors (e.g. dropping time and effort involved by using smoking aid instead of trying to quit cold turkey).

Now let’s bring something more related to my professional background. How successful will be my effort to implement agile process? How successful will be my effort to replace my client’s old website with new responsive website. Again, I’ve assigned values based on a hypothetical scenario.

Example C: Implementing Agile Process & Replacing Client’s Current Website (with scores)

Looks like my company ecosystem is too complex (Ratio is 5:8 / less than 1). On the flip side looks like the replacing client’s website will be very successful (Ratio is 4:1). I like these odds!


Of course this formula uses approximations and cannot quantitatively predict size of change to expect. It does however allow you to approximate and predict the change by comparing factors involved.

Now try this on whatever you are facing right now. What ratio are you getting?

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