Well, I say... "move fast and break things" is now a scientifically validated methodology.
Move fast, break things and succeed is the valid methodology. Just move fast, break things and put them in production, not so much.
Scientists use big data to understand what separates winners from losers
11–20 of 22 posts
Re: Scientists use big data to understand what separates winners from losers
#12>> The average number of failures for those who failed at least once before success was 2.03 for NIH, 1.5 for startups and 3.90 for terrorist groups. Regarding counter-terrorism, that would suggest that rather than more surveillance we instead need better reporting of and responding to failed terrorist incidents/attacks.
This reminds me of (I think) Richard Feynman's comment about the Challenger, to the effect that when the o-rings were eroded by 1/3 or whatever, they called that a safety factor of 3, whereas they should have stopped and said "wait, it wasn't supposed to do that at all, something is seriously wrong".
I'm not sure if it was him or someone else that pointed out when you do a post-mortem on a disaster, you generally find a history of near-misses, but people don't take near-misses as seriously as they should.
Re: Scientists use big data to understand what separates winners from losers
#13Why was the original headline changed to the less descriptive subheadline of the article?
for those not yet aware of this title change tracker: https://hackernewstitles.netlify.com/
Re: Scientists use big data to understand what separates winners from losers
#14>> The average number of failures for those who failed at least once before success was 2.03 for NIH, 1.5 for startups and 3.90 for terrorist groups. Regarding counter-terrorism, that would suggest that rather than more surveillance we instead need better reporting of and responding to failed terrorist incidents/attacks.
This reminds me of (I think) Richard Feynman's comment about the Challenger, to the effect that when the o-rings were eroded by 1/3 or whatever, they called that a safety factor of 3, whereas they should have stopped and said "wait, it wasn't supposed to do that at all, something is seriously wrong". I'm not sure if it was him or someone else that pointed out when you do a post-mortem on a disaster, you generally fin…
You're right, it was part of his opinion filed with the Rogers Commission:
https://en.wikipedia.org/wiki/Rogers_Commission_Report#Role_...
Re: Scientists use big data to understand what separates winners from losers
#15>> The average number of failures for those who failed at least once before success was 2.03 for NIH, 1.5 for startups and 3.90 for terrorist groups. Regarding counter-terrorism, that would suggest that rather than more surveillance we instead need better reporting of and responding to failed terrorist incidents/attacks.
Thank goodness startup founders aren't as determined as terrorists.
Re: Scientists use big data to understand what separates winners from losers
#16>> The average number of failures for those who failed at least once before success was 2.03 for NIH, 1.5 for startups and 3.90 for terrorist groups. Regarding counter-terrorism, that would suggest that rather than more surveillance we instead need better reporting of and responding to failed terrorist incidents/attacks.
This reminds me of (I think) Richard Feynman's comment about the Challenger, to the effect that when the o-rings were eroded by 1/3 or whatever, they called that a safety factor of 3, whereas they should have stopped and said "wait, it wasn't supposed to do that at all, something is seriously wrong". I'm not sure if it was him or someone else that pointed out when you do a post-mortem on a disaster, you generally fin…
The book Apollo by Charles Murray and Catherine Bly Cox shows a similar concept. In the investigation after the fire in Apollo 1, they found many possible near misses. But when each of those items did not lead to a failure, they became accepted. They poor designs or installations were allowed to slide and the accumulation of trouble spots kept increasing.
Re: Scientists use big data to understand what separates winners from losers
#17Well, I say... "move fast and break things" is now a scientifically validated methodology.
[deleted]
Re: Scientists use big data to understand what separates winners from losers
#18Well, I say... "move fast and break things" is now a scientifically validated methodology.
Not so much. It's validating "fail fast".
Re: Scientists use big data to understand what separates winners from losers
#19>> The average number of failures for those who failed at least once before success was 2.03 for NIH, 1.5 for startups and 3.90 for terrorist groups. Regarding counter-terrorism, that would suggest that rather than more surveillance we instead need better reporting of and responding to failed terrorist incidents/attacks.
There was once a mathematician who got caught at the airport with a bomb. They took him in for questioning. When asked why he had a bomb in his suitcase he stated "the odds of there being a bomb on your airplane are less than one in ten million. So I figured what are the odds of there being two bombs on my flight?"
Do you see why this is similar? When an outcome (terrorism) becomes an input to its own predictor (past terrorism failures), the logic breaks.
Re: Scientists use big data to understand what separates winners from losers
#20>the faster you fail, the better your chances of success, and the more time between attempts, the more likely you are to fail again This is confounded by the fact that the closer one is to success the more frequent their attempts tend to be. The article doesn't indicate the research took this into consideration. e.g. Golfers takes shots more frequently the closer they are to a hole. But telling a golfer from the star…
It is also confounded by the element of ruin, the ability of a gambler to stay in the game based on their stake. It's reasonable to suppose that some of the lag in re-try times (particularly with regard to startups) is attributable to raising money, which will be much easier for someone who starts out wealthy to begin with, even if they're not directly investing their own funds.