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From mystery to heuristics; how we develop knowledge

By Peter Opondo | May 12th 2021

Imagine for a moment that you have been dropped in the middle of Nairobi’s Central Business District. It is your first time in town and you are blindfolded. You can speak neither English nor Kiswahili, the most commonly used languages in the area. Ultimately, the blinders are removed and you are supposed to find your way out, probably back to the village. How would you go about it?

At this state, you will be confronted with infinite options. Do you ask around? Can you trust the people of this city to give you honest answers? Who can you talk to and who can’t you talk to? Can you just walk around and maybe discover for yourself?

All these questions form the first stage of knowledge development, which is all about exploring a mystery or a hunch.

Whichever option you choose, none is likely to deliver a perfect solution. You are likely to make several attempts with minimal success. Sometimes it might feel like drinking from a hosepipe, but by continually exploring the options you get a sense of what might work and what might not.

That is what happens to many entrepreneurs and innovators as they start their businesses; they proceed by making guesses–by doing stuff even with less than requisite information– and learning from the outcomes then adjusting accordingly.

The second stage of knowledge development is when we start developing general rules of thumb about what works and what doesn’t. These are called heuristics. Heuristics represent an incomplete yet more advanced understanding of what was previously a mystery.

Anticipation of demand

If you run a corner kiosk, over time you notice that you need to ramp up stocks to a certain level to meet evening demand as people go home from work. This anticipation of demand based on patterns becomes a heuristic that guides business decision-making. Not perfect, but it often works.

The final stage of knowledge development is the algorithm. When scientist Isaac Newton calculated that an item dropped from any height would fall at a constant rate of 32 feet per square second, he advanced what was a mystery (why are things falling down?) from gravity (heuristic) to an algorithm, a mathematical formula.

Algorithms are certified production processes with a guarantee that in the absence of intervention or complete anomaly, following a sequence of steps embedded in the process will produce specific results.

Guesswork is removed from the equation. Once you have an algorithm it all becomes about exploiting the formula for maximum productivity at peak efficiency. Production processes can be automated because specific inputs produce specific outputs. 

What we should note here is that this knowledge development funnel process is never meant to be complete as there are many cyclical iterations between and within each stage.

Businesses ultimately fail or are disrupted when, after developing a particular knowledge to an algorithm, they fail to appreciate the need to go back and explore other mysteries and hunches in order to develop algorithms for future growth. 

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