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I became an expert™ on the omicron variant over the weekend.
Unfortunately everyone else beat me to it.
Too slow. Online expert card revoked.
To be honest, the only thing I figured out 'for sure' is that it's too early to be certain of anything.
It's all guesswork.
I'm still bullish on oil 👇
Although not as bullish as JPMorgan...
Anyway, the whole episode has me thinking again about where to draw the line when we're trying to figure stuff out.
See, I think the natural human condition is to be curious, to try and better understand the world.
We get sidetracked by work, political tribalism, and sometimes a general exhaustion, and it's easy to fall into lazy habits, and draw conclusions based on very little.
Consistently battering against the frustrating wall of our natural limits does that.
It's tempting to give up, and specialise instead.
Stay in your lane. Excel at one thing. Focus on one area of expertise.
Why is specialisation encouraged when there's such stigma around generalisation?
Jack of all trades, master of none?
Perhaps, but why does it have to be one or the other?
Why not both?
Generalisation is unavoidable and necessary. It's an excessively tainted word.
"Oh, don't make such sweeping generalisations Tim, some politicans are actually nice people"
That kind of thing.
Yet there's sooo much information to filter through that we have to take some risks and try to jump to conclusions.
Some generalisation is necessary. Otherwise, how else can we get closer to figuring things out?
Especially in complex subjects where there's no real hope for a tourist...
Take omicron. Even the experts don't agree so what chance have any of us got? 👇
It's OK to say "I don't know"
In fact, I really think that should be the default mode.
Generalisation is no bad thing.
As long as it's this kind 👇
Generalisation: the act or process of perceiving similarity or relation between different stimuli, as between words, colors, sounds, lights, concepts or feelings; the formation of a general notion.
Rather than this kind 👇
Generalisation: a proposition asserting something to be true either of all members of a certain class or of an indefinite part of that class.
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Generalisation is especially useful as a learning tool.
Generalisation is understood to be directly tied to the transfer of knowledge across multiple situations.
The knowledge to be transferred is often referred to as abstractions, because the learner abstracts a rule or pattern of characteristics from previous experiences with similar stimuli.
(Such as similarities between sentiment driven bubbles or positiong squeezes)
Generalisation can be supported and partly explained by the connectionism approach.
Just as artificial intelligences learn to distinguish between different categories by applying past learning to novel situations, humans and animals generalise previously learned properties and patterns onto new situations, thus connecting the novel experience to past experiences that are similar in one or more ways.
Generalisation is good as long as we're aware we're doing it and why.
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