“Bias” tends to have negative connotations. It’s the “wrong” answer.
The problem here is a translation issue. It’s going from the world of One Answers (mathematics) to the world of Many Answers (life).
Weather is a fascinating demonstration. Nate Silver writes in the 2020 edition of The Signal and the Noise, “The further you get from the government’s original data and the more consumer facing the forecast, the worse this bias becomes.”
(John Gruber) “I staunchly believe that Fahrenheit is the better scale for weather because it’s based on the human condition. Who gives a crap about what the boiling point of water is, it’s the most ridiculous thing I’ve ever heard in my life.”
(Ben Thompson) “The other thing is that Celsius is not precise enough. In the car it adjusts it by point-five because a single degree of celsius is too much for the car. Fahrenheit is more finely grained in a positive way.”
This is why we have a wet bias. We design weather for people.
Silver again, “It’s deliberate and it has to do with economic incentives. People notice one kind of mistake, the failure to predict rain, more than another kind, false alarms. If it rains when it’s not supposed to they curse the weatherman for ruining their picnic. Whereas an unexpectedly sunny day is taken as a serendipitous bonus.”
One change in my thinking over the yeas has been to reframe ‘bias’ as ‘tendency’ and then consider what’s happening. Humans are only illogical in the game of optimization, which matters in the world of calculations rather than considerations.
Wet bias may be inaccurate but that doesn’t make it wrong.
There’s the Fermi Paradox, which wonders where the aliens are. There’s also the Fermi Problem, which considers piano tuners in Chicago. These two are related to each other and also to what we will call Fermi Knowledge.
For instance, I remember that when he returned the calculus book by [Ulisse] Dini I told him that he could keep it for another year or so in case he needed to refer to it again. I received this surprising reply: “Thank you, but that won’t be necessary because I’m certain to remember it. As a matter of fact, after a few years I’ll see the concepts in it even more clearly than now, and if I need a formula I’ll know how to derive it easily enough.”
That’s a deep understanding to aspire to. Reading about Fermi, or Feynman, it makes me wonder how much of insight is due to seeing the world through first principles and then verifying a new idea works.
Imagine a young Ben Folds. He’s walking to piano lessons. He loves the piano but not this particular teacher. It’s snowing. And windy.
There’s a bicycle track through the snow. It’s all Folds sees. It’s snowing and windy.
He sees the track and imagines what happened. The track changes direction, the story changes too. Folds writes:
“I want to laugh at how old-fashioned and easily entertained I must sound to a kid today, who has a lot more seductive electronic shit competing for their attention. But a story is a story, in any era. And the best ones, I’ve always thought, develop from mysteries you want to solve.”
There’s a dichotomy between deep work (Newport) and Against Waldenponding (Rao). We balance on this tightrope each day. Some days more on one end, other days at the other, and some we troop between the two.
Newport wants people to learn first principles, to study things which change slowly. Rao wants people to fit first principles into the world in interesting ways, to prototype, to gather rough consensus and run code.
Stories are a first principle idea to consider. We run on stories and one way to get better at telling them is through boredom. Folds again:
Marc Andreessen once noted that it’s important to learn the right lessons from our experiences. As the expression goes, you never step in the same river twice.
One lesson from Reed Hastings’ No Rules Rules book about Netflix is the idea of cadence. To survive in their system, Netflix must tack from explore to exploit at a faster pace than the local pool construction company.
In the book Hastings writes that the Netflix expense policy (‘Act in the best interest of Netflix’) probably costs 10% more than a more strict policy but that it allows the employees to make faster decisions in an industry where the cadence has to be quick.
Co-Author Erin Meyer points out up front that Netflix has succeeded at four inflection points: DVD by mail to streaming, streaming licensed content to original, licensings original content from external studios to internal, from USA only to global.
That’s a lot of change in a short amount of time.
Pool Co. by contrast will probably stay in the exploit region for a longtime, in the right geographic region forever.
The filter from Hastings is this: the internal cadence should reflect the system’s cadence.