There are limits to what we know, and a lot of the time we are trying to figure out ways in which to make outcomes insensitive to our ignorance. Risk avoidance in the daily sense, to me, is ignorance avoidance, the seeking of profitable action crafted to avoid areas of ignorance. My ignorance being special to me, by paying attention to it I make maximal use of my Richardian comparative advantage with respect to others.

I don’t understand what others do or do not know very well, so I use the shortcut of estimating comparative advantage by virtue of my absolute level of certainty in the subject matter. Sometimes, however, I come across subjects which nobody really knows anything about. If others also maximize their comparative advantage by sticking to opportunities of high absolute certainty, then opportunities of low absolute certainty and high relative certainty would be one type of under-exploited opportunity I know to expect to find.

This, I feel, is the risk-tolerance of the entrepreneur. It is quite distinct from financial risk as we treated it two posts ago, which in fact is a rather strange animal because it is simultaneously uncertainty about the outcome and certainty about probabilities, an unlikely state in the real world.

I am guessing that the mind machinery for dealing with possibilities and future counterfactuals is more like Monte Carlo than anything else. We think of detailed alternate future paths and use those to weigh the value of an option – sounds like the algorithm a particular go-playing AI uses. What makes for a good Monte Carlo sampling strategy? How does one become a better Monte Carlo sampler? Answers to those would make for big advances in thinking.

Chiao, I read this and your previous post on financial risk with great interest. Connecting the two posts, you could continue to say that risk takers are being compensated for more than only undiversifiable risk (ala CAPM), but are in fact being compensated for assymmetric ignorance. But of course, assymmetric ignorance probably implies inefficient markets, which probably is assumed by CAPM.

But even more of interest to me is another continuation of this discussion: I have been thinking, like you, about the decision process involved in seeking competitive advantage.

Often, in the course of seeking competitive advantage, we are faced with a seemingly simple situation: we must choose between two possibilities with outcomes sufficiently characterized by normally distributed random variables X and Y. I find usually, I can estimate both expected value and variance of these variables sufficiently for our purposes.

Let us say, for example, X has both a greater expected value and a greater variance than Y. Because of the variance, although X has higher expectation, perhaps Y should be chosen.

What confuses me is how to account for this variance. In another words, what is the risk discount? I do find the quadratic model perhaps insufficient for the practical needs of an entrepreneur.

Your thoughts?

@Carl

Assuming a normal distribution and a general utility function is to a very large extent the same as assuming a quadratic utility function and a general distribution. (You can sort of see this from how adding any two quadratic functions gets you another quadratic function and multiplying any two gaussians gets you another gaussian.)

I don’t think an entrepreneur should care about these crude strategic pictures. What they should really care about most, I think, are tactical issues, the thousand papercuts that grind at startups. For example, I think the variance in employees and co-founders completely overrides anything else (i.e. how they respond to seemingly impossible tasks). Shortly after you shatter all tactical issues, you’d be in the position to hire a proper CEO I think. :)