#7: Algorithm-ish

Finding algorithms

Daniel Kahneman, the Nobel Prize-winning psychologist and author of Thinking, Fast and Slow, advises using an algorithm whenever possible. Although algorithms can also be biased, they don't suffer from the inconsistency and noise that human decision-making can introduce. The absence of noise, Kahneman says, gives them an advantage.¹

A great example is in book I’m almost finished reading, In "Talking to Strangers," Malcolm Gladwell.

Gladwell discusses research showing that an algorithm outperforms judges in deciding who should be released on bail. The algorithm, analyzing historical data, more accurately predicted who would reoffend or fail to appear in court. Unlike human judges, the algorithm's decisions were consistent and free from biases and noise, significantly reducing crime rates if implemented.

Judges, like most of us, think that by seeing the person and getting a feel for them, is important to judging what should be done. But that inclination is what creates more mistakes.

But then, here is the question: if we should reach for an algorithm to make decisions, where do we get these algorithms?

Here is the insight.

Though an algorithm seems like it only meaningfully exists in the world of math and computers, we can think a little less strictly about them.

A step-by-step process reduces noise and can be considered an algorithm.²

As a result, this allows us to recognize numerous opportunities to find useful algorithms from insights and guidance in books ranging from self-help to strategy.

References

Gladwell, Malcolm. Talking to Strangers. Penguin Books, 2020.

1. Daniel Kahneman and Olivier Sibony on Noise, Algorithms and Bias

2. In each step, a person can introduce noise, unlike a computation algorithm run by a computer.