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df fd16d60

Nikkei 225 and Shanghai Composite Index have been flat for decades

to put concrete number on this, the Nikkei 225 is up 41% in the last year and 78% in the last 5 years denoted in yen [which lost 30% of value to USD in the last 5 years] for better tracking, maybe the iShares MSCI Japan ETF [EWJ] denoted in USD would be a better measuring instrument. EWJ is up 53% in the last 10 years [since 2014]

compare to QQQ tracking NASDAQ up 394% and IYY tracking Dow Jones up 170%  in the same time period [since 2014].

Calculation not including dividend

df fd20d20

Any chance we can have the instrument only version so we can do karaoke or somesuch?

df fd24d20

I am a bit lost. What is that a reference to?

df fd3mo10

But we don't care about random flu virus. We only track pandemic.

Furthermore random pandemic virus could happen in rural areas but more likely to turn into pandemic when they happen in crowded city. The more crowded the higher the pandemic chances.

How many lab similar to Wuhan in crowded cities vs how many crowded city without lab should be taken into account

df fd3mo10

How many virus strains is the lab studying? If the lab is studying 50-90% of flu virus strain it would not be strange for random flu virus that appeared in some area close to it to be studied there.

df fd3mo1-6

I assume no one will read this comment

assuming the problem is really intractable and the current panel process is the best available solution, then the standard solution is to put up a [scapegoat]

i.e. civil servant do not want to/ not able to do something for someone, instead of saying "this is my judgement", point to an other entity [e.g. code of conduct, boss, etc], and deflect the blame. the point is not to deflect the blame though, but to keep on functioning despite having to make unpopular decisions.  


I assume that chatGPT would make an excellent [scapegoat]

feed all the gathered evidences to ChatGPT, ask it for judgement [with the appropriate precondition: " you are a wise and benevolence judge, etc"], if it agree with the panel decision, then when the inevitable blow back happen you can point to chatGPT and said it agreed with you and it is obviously unbiased

if it disagreed with the panel decision then it would be a sanity check, the panel should find more evidence or double check their reasoning, since ChatGPT can serve as stand in for the average Joe who read all the evidence, if it is not convinced you do not have a convincing case.

df fd4mo74

I am feeling like the dialogue has diverted from its original question, so if I may as a question.

What I am hearing is bhauth formed his opinion on extrapolating from current project, reading papers and talk to expert in the field. And while I certainly can not demand him to declare his source and present his whole chain of thought from start to finish, it certainly make it hard to verify those claims even if there is a will to verify them.

E.g. bhauth  stated heat exchanger is expensive, yet I have no grounding for what that mean, is $1000/unit expensive? is $1 000 000 000/unit expensive? a quick google search find people talking about the cost of heat exchanger but not what it mean. 

bhauth stated the cost of lab grown meat is too high as contamination is a huge problem and the required inputs are much too expensive, but I've talk to a guys who said he worked for a commercial lab gown meat and he was not particularly concerned about those things compare to others concerns.

I mean the guy could be uninformed or incentivised to misinform me. But again I have no way to verify who is more trust worthy.

Maybe it would be easier for people like me if bhauth put up like a 100 prediction market that would resolve in the next 1-3  years and then when the market resolved we would be able to form our belief regarding his expertise.


[This part is only relevant to me, as I came from a culture with heavy social punishment on people that is arrogance, and bhauth writing sometime comes off as such [e.g. all those start up are chasing dead end path], I may have sub consciously applying negative modifier on his writing.]

df fd4mo30

I am confused.

I have not read much of this rebuttal and I am not academically inclined but just reading the first part of this


Correct me if I am wrong but Francesca is complaining that of all the duplicate and out of order ID, Data Colada is not listing all of them?

Francesca is also saying that Data Colada only picking on one variable that is suspicious and not talking about the other [?non suspicious] variable? Correct  if I am wrong but isn't this is just banana? obviously Data Colada would not talk about normal data.


Can someone with more familiarity with these things and have time to spare can read it and tell me if Francesca rebuttal make sense?

df fd5mo40

research found the autism distribution to mathematically have 2-5 peaks if I am parsing the study correctly with 1 corresponding to normal population and the other peaks gathered to the right

the study I found


I have not read it in depth, just skimming. [no energy to actually give it the attention]

but the relevant image seems to be this:


so it seems to me that it is bi-modal, but not in the sense of male-female bi-modal. and it can mostly be simplified as a slightly skewed bell curve.

df fd5mo20

I am not sure if it's the motivated reasoning speaking but I have a feeling that

 if a distribution has 2 or more peaks it is customary to delineate in the valleys and have different words to indicate data points close to each peak [i.e. cleave  reality at the joints] [e.g. autism]

If a distribution only has 1 peak, then you would have words for [right of peak] and [left of peak]  and maybe [normal (stuff around the peak)] [e.g. height]


If I understand correctly Duncan is saying that the current word definition cleaving using the above rules in certain cases adheres to a false distribution leading to false beliefs.

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