Typically, we think of adapting as adjusting to a change in one's environment:
|The season changes from fall to winter
|You wear layers to stay warm
|Your boss leaves and a new one takes his place
|You get to know the new boss
|You drive in an unfamiliar town
|You drive slower
In this formulation, the process of adapting is simply an adjustment of a current method. We have some process or method to handle changes which result from the accumulation of our knowledge and experience. By this definition, we often see ourselves as adaptable based on the speed of adjustment - effectively how fast we can optimize. The faster we identify the difference, modify our behavior, and tune our methods to tailor our response accordingly, the more adaptable we are.
But what happens when the change is very large?
|A cold wave hits and the temperature drops to -30°F
|Get to know boss
|The company gets bought out and you're out of a job
|You drive in a city for the first time
Suddenly, it's not clear that our current methods would be of any value. At -30°F, which is unbearably cold, no number of layers is going to stop you from freezing. Similarly, no "managing up" of any sort is going to help you if you're suddenly out of a job. And consider the surburban driver who drives in a city for the first time. It's a rude shock to realize that city driving is not just faster, but drastically different with pedestrians, cabs, bikes, one-way streets, sudden stopping, and narrow lane changes. Driving slower does not help - in fact, it almost always encourages other drivers to honk, cut in front, and generally make you more prone to error.
When the change is too large, it becomes different.
A large change shakes up the current method so that it's not useful. As a result, there is no modification or tuning for you to do. Instead, you'll need to build a new method for the problem at hand. When we view the situation from this angle, our previous confidence in being highly adaptable persons is rather shaken. If we can only react to small changes but not to large ones, are we even adaptable?
Adjustment vs. Transformation
Different states of adaptation are driven by the size of the change that occurs.
A small change is a change that exists within the same search space of our current method. In other words, a small change is manageable by the general process in which we typically solve that problem. There is some tweak you can make in your process to handle the change.
An adaptation to a small change is an adjustment. Adjustments are built by way of modifing, tailoring, and tuning an existing method. They are optimizations.
A large change, on the other hand, is one that happens outside the search space of our current method. A large change is not manageable by the same general process because the problem is outside of its domain. Tweaking the current process we have at hand to solve the problem is going to be ineffective.
An adaptation to a large change is a transformation. Transformations do not have a strict existing method to build on top of like adjustments do.
While it may pull from different sources, a transformation is ultimately constructed from the ground up. It happens by reorganizing current understanding and rebuilding the method for the space.
In a sense, the current method evolves into a different one. They may share some lineage, but the new one is fundamentally different from the old one.
Adjustment adaptation states are sticky. They are take significantly less energy than transformation ones. The majority of the time we opt for adjustment adaptations regardless of change size because it's easier.
State changes from adjustment to transformation are inefficient. We often start first by trying different adjustments in order to gain the realization that any adjustment would be ineffective. Until such a point is reached, we typically don't try for transformation adaptation because of the extreme energy differential. No one wants to think from first principles all the time because it's exhausting and slow.
Realizing that the change size is different - and therefore requires a new process - is the crucial step to initiating a state change.
Until we identify this, there is little reason to opt for the hard way over the easy way. The inefficiency of this state change is different for everyone because change size is ultimately personal. What is a small change to one person can be a large change to another. How we define each of our methods and the relevant search spaces is a process that is unique to each individual.
Optimizing Isn't Creating
We are attuned to optimizing. Most of what any system does is to make things easier. Typically this is done by teaching or instilling some ability of adjustment adaptation. And by dint of going through society, we are all products of some system. Learning and challenge exist, but they are specifically scoped to be incremental because it is more efficient to learn that way. So when a large change is presented, we often treat it how we are trained - we try to adjust and optimize. But optimizing doesn't work.
We read studies that say "happy people" have 7.1 hours of sleep, 16 minutes of commute time, a minimum salary of $95K, and write 5 minutes of morning pages. Many people who hit these numbers still find their life lacking. So what's next? Read more self-help books, listen to more podcasts, travel to different countries, and enroll in graduate school? Unless these actions are done with the explicit purpose of seeking to re-evaluate your own life and world view, they don't work. Optimizing does nothing for the ultimate problem of obtaining happiness, because happiness is a transformation problem.
You can't optimize your way to something new.
Transformation adaptations require you to create something new. You have to evaluate the space and construct a new approach that works. Knowledge and experience can act as guide posts, but they aren't directions. There is no easy way out. If there was then it wouldn't be a transformation problem to begin with. Don't spend your time searching for fire when you need to build one yourself.