Comfort Zones and Local Optima Problem
We’re all challenged to get out of our comfort zones, set ambitious goals and engage in activities where we’ll be stretched, have opportunities to learn and develop ourselves.
Getting on this path is difficult, since it requires discipline and effort. This effort has many dimensions: dealing with uncertainty, fear of failure, taking a risk, starting over, loosing superiority, learning something difficult, and so on and so forth.
While contemplating on this claim and trying to understand why it is considered to be a universal truth, I realized that we can approach it as an optimization problem.
The problem of local optima
Let’s start with optimization problem definition from Wikipedia:
In mathematics, computer science and economics, an optimization problem is the problem of finding the best solution from all feasible solutions
Makes sense, right? What we really want is to find the best version of ourselves among all possible versions.
I deliberately put emphasis on “ all”, because this what the problem of local optima is about.
A local optimum of an optimization problem is a solution that is optimal within a neighboring set of candidate solutions.
Searching for optimal solutions includes approaches, such as “local search” or “hill climbing” methods, in which we start from an initial configuration and repeatedly move to an improving neighboring configuration. Eventually we get to the point where local search is stuck, as no improving neighbors are available.
Another example of this problem could be found in genetic algorithms.
Genetic algorithm (GA) is a search-based optimization technique based on the principles of Genetics and Natural Selection. Genetic algorithms are based on the ideas of natural selection and genetics. They simulate the process of natural selection: those species who can adapt to changes in their environment are able to survive and reproduce and go to next generation.
As with other optimization algorithms, generic algorithms suffer from local optima problem as well.
Getting Stuck in Comfort Zones
It is obviously easiest to stay in our comfort zones, but the analogy with the local optima problem shows us that this way we’re not likely to go far nor we will develop ourselves much further. Staying in our comfort zones for too long probably means that we won’t be able to keep stretching ourselves beyond the “neighboring set of candidate solutions”. It’s very hard to expand yourself when no pivot in your world of ideas, concepts, and perspectives is possible and that’s exactly what happens.
The Way Out
So how do we solve it?
We solve it in the same way, “restarts” solve it in optimization algorithms, the same way “mutations” solve it in generic algorithms, the same way teleport operations solve it in Google PageRank to get out from “dead” pages with no out-links.
We introduce uncertainty.
We introduce randomness by setting stretch goals, acquiring new skills and establishing new connections.
However counterintuitive it might sound, we must go to the unknown to better know ourselves.
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Originally published at https://www.linkedin.com.
