I wish they had taught me this in school.
Why does this statement almost always come up when discussing problem-solving frameworks? We encounter problems every day. And as the world becomes more technologically advanced, such problems become more complex.
In 2015 and 2020, the World Economic Forum named Complex Problem Solving the single most valuable skill for thriving in the Fourth Industrial Revolution.
I can’t recall ever once being taught how to draw a simple mindmap, a systems diagram, game theory, or bounded rationality. Yet, these are some of the most valuable tools in my arsenal as a strategist. And these are only a few of the tools that we need to learn to stay relevant.
Why is this?
The majority of school curriculums focus on natural sciences, i.e., how things are. The natural sciences provide methods for investigating and understanding naturally occurring things such as ourselves, plants, animals, the world, and the universe. Physics, math, biology, geology, chemistry, and astronomy all fall under the natural sciences.
“In view of the key role of design in professional activity, it is ironic that in this century the natural sciences have almost driven the sciences of the artificial from the school curricula.” - Herbert A. Simon
Problem-solving requires focus on how things ought to be.
Design is a key function of problem-solving. Within the realm of design are two critical environmental aspects. The “inner environment” and the “outer environment.” The “inner environment” represents the set of possible alternative solutions.
The “outer environment” represents the boundaries that are either known or unknown. We can consider these boundaries to be the design constraints that must be adhered to.
The designer's goal is to optimize the problem by identifying the solution that best optimizes the expected payoff value while adhering to the boundaries.
Good designers have experience across multiple disciplines.
Particularly in the fields of data science, machine learning, systems architecture, and software development. These skillsets are in high demand. Organizations willing to pay a competitive rate for such talents are well-positioned to capture professionals who have developed the necessary skillsets to be considered good problem solvers.
Public schools may lack the resources to compete for such talent.
According to Salary.com, the average salary for a public school teacher in the US is $59,530. On the other hand, the average salary for US-based systems engineers, data scientists, and software engineers are $71,829, $133,833, and $116,730 (in order). Given these numbers, I’m unconvinced the masses would go through the trouble to gain the skills necessary to effectively teach the subject and then take a pay cut to do so.
So what’s the answer?
Given this problem, we have a few options immediately available. We can pay teachers more, dedicate funds to training related to problem-solving skills, or encourage those with design skills to enter the education system. How might we go about exploring any of these options?
Thanks for reading!
All the best,
Roy Naquin
P.S. Did someone forward you this article? Want more?
0 comments