I just had my first encounter with the US medical system outside of university. The place was part of the Beth Israel Medical Group, called Docs Physicians, and I went as a walk-in to the branch at 55 E 34th. They opened at 9am on saturday morning, so I arrived a bit early and had a coffee and bagel next door before I went in. There were two patients before me, and the wait was short (30 min) even though there was only one attending physician. I told the doctor that the last time I had suffered a prolonged cough from a cold, I had been prescribed an inhaler, and may have accelerated the diagnosis a bit that way. Docter was Keith Uleis, he ended up prescribing an antibiotic (Azithromycin), an inhaler (Albuterol) and a steroid (Prednisone) for inflammation control, after putting me on a nebulizer for 15 min to gauge the possible asthmatic effects. The charge for the consultation was $225, and filling my prescription next door at Pasteur Pharmacy cost $10 (steroid) + $40 (antibiotic) + $50 (inhaler) = $100.

I can't believe it took me this long to figure out that I needed to go to urgent care - am seriously wondering about the purpose of having a primary care physician, which I spent part of yesterday trying to pick out. The differences: one, familiarity with medical history;  two,  a more relationship-based, as opposed to transaction-based, incentive system; three, most importantly, scheduling flexibility.

Giving doctors uniform access to standardized medical records gets rid of the first point. As for the second, I dislike decision by anecdotal evidence, which is what the relationship model encourages. What I want is for my accessible doctor pool to be more effectively commoditized, by virture of some standard success record. Urgent care centers may provide that by acruing reputation as a collective and not on a doctor basis.

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.

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.