A ship without a compass is lost, no matter how seaworthy. The tools we use to navigate "change" may vary and evolve. The key challenge for anyone in life or business is to have a firm understanding of what their primary objectives/values are (they may change too). Too often, businesses let the 'urgency' of change management (theories and practices) undermine the 'criticality' of purpose.
You are compressing a lot in a very short question. I think that there should be a lot to discuss. So let me try an initial suggestion:
It is probably relevant to accept as an axiom that change is a process. We may think of a differential equation. Most simply, we have an initial condition that can be fixed as a starting point for the process that the differential equation describes (its trajectory or solution). Yet, even this simple view implies a change from the past (namely, the initial condition should be studied in more detail and not assumed away). If we worry about the effect of varying initial conditions, a good starting point is to think of delayed differential equations. But, we might ask, where is “volatility” in all of this? There is none: we are using deterministic differential equations: everything that the differential equations model is known with certainty! And so we can move to stochastic differential equation: volatility becomes variability of the probabilistic kind. Distributions are explicitly taken into account and so we are more consistent with “change” that now seems a natural aspect of the model and is dealt with as trend and diffusion.
We can now turn to “uncertainty” as such, moving away from the narrow confines of the probabilistic world. We need to start thinking of fuzziness, vagueness, possibilities, and so on. It pays to begin by taking smalls steps and follow Zadeh, Dempster and Shafer all the way to I. Balboa and many others. “Uncertainty” is not so simple to deal with and requires a scan across complicated literature, before proceeding.
The same can be said of “complexity”. We have computational complexity, physical complexity, information complexity, and so on. Again, combinatorial aspects are one example of computational complexity; fluid mechanics is another (e.g., Navier-Stokes equations). Entropy of information becomes very useful when dealing with the information content of flat (uniform) distributions; Gibbs distribution is also important to start with for both probabilistic and physical (thermodynamics) aspects. We may start with Nicolis and Prygogine for scanning the (mostly physical) domain of complexity, as well as Chatlin for a more theoretical discussion.
Finally: now to “ambiguity”. We go from language (using a dictionary) definitions to decision theory (see Ellsberg, Machina, Quiggin, Schmeidler, Marinacci, Ghirardato, Nau ,and many others) for more theoretical and practical discussions.
I hope that this brief amplification of your issue helps.
Focusing on technical and professional education, giving up luxuries, and working on the correct balance between income and spending, and it can be said that a good relationship with good family and friends has an important role, and paying attention to everything that contributes to enhancing your health.
Great comments. I am reminded that "the more something changes the more nothing changes."
I prefer to set the stage for VUCA formally so that I have a common understanding. from which I can go on, avoid ambiguity and its effects on reasoning about future change, and deal with the effect of uncertainty on predicting changes.
I agree with all of the comments and expressed feeling about changes and how to face them.