wedrifid comments on Open Thread: July 2010 - Less Wrong

6 Post author: komponisto 01 July 2010 09:20PM

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Comment author: wedrifid 02 July 2010 03:02:31AM 2 points [-]

These strike me as good examples of applied rationality. I wonder what percentage of Less Wrong readers would succeed as startup founders?

I would not deviate too much from the prior (most would fail).

Comment author: lsparrish 02 July 2010 11:07:43PM 0 points [-]

Are you saying that LW readers suck at applied rationality, or are you disagreeing with the idea that applied rationality can help prevent startup failure?

Comment author: wedrifid 03 July 2010 04:11:34AM 0 points [-]

I would say that preventing startup failure requires a whole group of factors, not least of which is good fortune. It is hard for me to judge whether LW are more likely than other people who self select to start start ups to get it all right. I note, for example, that people starting a second startup do not tend to be all that much likely to be successful than on their first attempt!

Comment author: lsparrish 03 July 2010 06:27:19PM *  0 points [-]

Suppose we were to test it empirically and 9/10 startups fail on their first attempt. Then test again and 9/10 still fail on second attempt. That is not enough information to determine that a given person would fail 10 times in a row, because it could be that there is some number of failures <10 where you finally acquire enough skill to avoid failure on a more routine basis.

Given the fact that there's a whole world of information, strategies, and skills specific to founding startups, I would be surprised if an average member of a given group of startup founders fails x times out of y when x/y first attempts also fail.

So it would be relevant (especially if you are, say an angel investor) how low the percentage of failures can be brought to with multiple attempts by a given individual, and whether a given kind of education (such as reading Less Wrong sequences, or a quality such as self-selecting to read them) would predispose you to reducing that number of failures more rapidly and/or further in the long run.

Comment deleted 04 July 2010 02:28:16AM [-]
Comment author: lsparrish 04 July 2010 04:04:29PM *  0 points [-]

Here is the relevant quote from Paul Graham's Why Hiring is Obsolete:

Risk and reward are always proportionate. For example, stocks are riskier than bonds, and over time always have greater returns. So why does anyone invest in bonds? The catch is that phrase "over time." Stocks will generate greater returns over thirty years, but they might lose value from year to year. So what you should invest in depends on how soon you need the money. If you're young, you should take the riskiest investments you can find.

All this talk about investing may seem very theoretical. Most undergrads probably have more debts than assets. They may feel they have nothing to invest. But that's not true: they have their time to invest, and the same rule about risk applies there. Your early twenties are exactly the time to take insane career risks.

The reason risk is always proportionate to reward is that market forces make it so. People will pay extra for stability. So if you choose stability-- by buying bonds, or by going to work for a big company-- it's going to cost you.

Riskier career moves pay better on average, because there is less demand for them. Extreme choices like starting a startup are so frightening that most people won't even try. So you don't end up having as much competition as you might expect, considering the prizes at stake.

The math is brutal. While perhaps 9 out of 10 startups fail, the one that succeeds will pay the founders more than 10 times what they would have made in an ordinary job. [3] That's the sense in which startups pay better "on average."

Remember that. If you start a startup, you'll probably fail. Most startups fail. It's the nature of the business. But it's not necessarily a mistake to try something that has a 90% chance of failing, if you can afford the risk. Failing at 40, when you have a family to support, could be serious. But if you fail at 22, so what? If you try to start a startup right out of college and it tanks, you'll end up at 23 broke and a lot smarter. Which, if you think about it, is roughly what you hope to get from a graduate program.

He also goes on to say how managers at forward-thinking companies that he talked to such as Yahoo, Amazon, Google, etc. would prefer to hire a failed startup genius over someone who worked a steady job for the same period of time. Essentially, if you don't need financial stability in the near future, your time spent working diligently and passionately on your own ideas trying to make them fit the marketplace is more valuable than time spent on a steady payroll.

Comment author: gwern 04 July 2010 05:57:11PM *  3 points [-]

"For example, stocks are riskier than bonds, and over time always have greater returns."

In a LW vein, it's worth noting that selection and survivorship biases (as well as more general anthropic biases) means that the very existence of the equity risk premium is unclear even assuming that it ever existed.

(I note this because most people seem to take the premium for granted, but for long-term LW purposes, assuming the premium is dangerous. Cryonics' financial support is easier given the premium, for example, but if there is no premium and cryonics organizations invest as if there was and try to exploit it, that in itself becomes a not insignificant threat.)

Comment author: Douglas_Knight 04 July 2010 08:18:12PM *  -1 points [-]

The survivorship bias described by wikipedia is complete nonsense. Events that wipe out stock markets also wipe out bond markets and often wipe out banks. Usually when people talk about survivorship bias in this context, they mean that the people compiling the data are complete incompetents who only look at currently existing stocks.

If your interest is in the absolute return and not in the premium, then survivorship is a bias.

ETA: I think I was too harsh on the people that look at the wrong stocks. But too soft on wikipedia.