Free will, Rationality and Intelligence
Free will, rationality and intelligence are inseparable in definition.
In FREE WILL-EVEN FOR ROBOTS, McCarthy gives a definition of free will which boils down to the ability to say "I can, but I won't". To be able to say "I can", the system has to have built a representation of the world, complete with counterfactuals representing the way the individual parts of the world link and react to each other. To be able to say "I won't", the system needs to have a preference, that is, in its actions it is using its understanding of its cans and cannots (the counterfactuals) to drive the world towards a state more preferable to itself.
Seen this way, free will is a design pattern / framework. It is any representation-building goal-seeking system. Rationality, then, is a statement about the quality of the representations - if a free will makes choices that most effectively seek its goals, it is considered rational. Note that you need to know both the actions and the goals to determine rationality. Whenever I see papers on irrational behavior, I look carefully to see what goals they assume.
The study of human rationality poses problems because we often don't know what people want, and it isn't certain that you get the right answer by asking them. However, even when the goals sought are not known, there is progress that can be made towards assessing the presence of rationality. For example, if I assume that a person is walking with the goal of going from point A to point B, I don't have to know what those points are to observe that any path with a U-turn is suboptimal. (I think this underlies the unwillingness to make U-turns even when they are optimal going forward, because they provide everyone around you with an undeniable proof of suboptimality.) I would be wrong to conclude this for a sight-seeing tourist though, or an oil tanker that gets diverted because it receives news that the price of crude is now higher elsewhere.
What of intelligence then? Well, counterfactuals are built by processing data from the senses / memory. I consider all quality difference attributable to the processing, and not the data, to be intelligence. This is often described in terms of speed - by locking a person up in a room, the time needed to reach the final conclusion cannot be due to new data, and must therefore be due to the processing, i.e. how fast or slow the person is. This is for cases where a final conclusion exists - where given enough time all people arrive at the same answer. In cases where the answers are persistently different, it is more difficult to examine intelligence by itself - I believe this is why the slow/fast terminology persists.
2 comments
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. :)