Today I Read

Do Algorithms Beat Us at Complex Decision Making?

Short Answer: Yes, and in a lot of cases, better than experts.

Turns out biases get the better of us all, and sticking to simple hueristics gives good results because of consistant input/output and the focus on what's important

Take aways:

The surprising success of equal-weighting schemes has an important practical implication: it is possible to develop useful algorithms without prior statistical research. Simple equally weight formulas based on existing statistics or on common sense are often very good predictors of significant outcomes

If you are serious about hiring the best possible person for the job, this is what you should do. First, select a few traits that are prerequisites for success in this position (technical proficiency, engaging personality, reliability, and so on). Don't overdo it — six dimensions is a good number. The traits you choose should be as independent as possible from each other, and you should feel that you can assess them reliably by asking a few factual questions. Next, make a list of questions for each trait and think about how you will score it, say on a 1-5 scale. You should have an idea of what you will call “very weak” or “very strong.” These preparations should take you half an hour or so, a small investment that can make a significant difference in the quality of the people you hire.

  • Decision Making Noise: Given the same set of data twice, we make two different decisions. Caused by internal contradiction.

  • "It's possible to create useful algorithms without prior statistical research. Simple equally weight formulas based on existing statistics or on common sense are often very good predictors of significant outcomes."

  • "In a memorable example, Dawes showed that marital stability is well predicted by a formula: Frequency of lovemaking minus frequency of quarrels."

  • "the vast majority of human stock-pickers cannot outperform a simple S&P 500 index fund"

Spiraled into reading...

Simple Rules:

First, they are limited to a handful. Capping the number of rules makes them easy to remember and maintains a focus on what matters most.

Second, simple rules are tailored to the person or organization using them. College athletes and middle-aged dieters may both rely on simple rules to decide what to eat, but their rules will be very different.

Third, simple rules apply to a well-defined activity or decision, such as prioritizing injured soldiers for medical care. Rules that cover multiple activities or choices end up as vague platitudes, such as “Do your best” and “Focus on customers.”

Finally, simple rules provide clear guidance while conferring the latitude to exercise discretion.