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Reification of Entropy?

I’m beginning to suspect that the notion of entropy outside the physical domain is simply a mathematical construct and may have nothing really to do with “information” as the word is generally accepted (unless you change the numeraire and define information as entropy). I claim that we can work with it as a mathematical construct (like the notion of variance), but to try and assign a deeper meaning or a connection to information may be going too far. (This does not preclude me from claiming that the mathematical construct called entropy is increasing).

The size of a zipped-file containing the text of Eco’s Foucault’s Pendulum does not represent the information contained within that text. The notion of information is relative to the reader and is at least tangentially related to the concept of “meaning.”

Shannon’s entropy is a measure only of the probability of a specific message amongst all possible messages. It is not a function of the transmitter or the receiver, only the channel. But, for someone who speaks no Korean, the Korean translation of Foucault’s Pendulum is meaningless, even though the entropy is probably the same. So entropy does not represent meaning or information or the lack of meaning or uncertainty or any such thing except in a narrowly defined sense as described by Shannon.

Here is a quote from Shannon’s original paper:

The fundamental problem of communication is that of reproducing at one point either exactly or approximately a message selected at another point. Frequently the messages have meaning; that is, they refer to or are correlated according to some system with certain physical or conceptual entities. These semantic aspects of communication are irrelevant to the engineering problem. The significant aspect is that the actual message is one selected from a set of possible messages

I repeat for emphasis:“These semantic aspects of communication are irrelevant to the engineering problem.” When we talk about information, all we’re talking about is the semantic aspect, not the engineering aspect. When a new discovery is made and it enhances our understanding of the world it increases the information content in the universe, but it is not directly related Shannon’s entropy. It does increase the size of our libraries, but so do many other things that are not considered new forms of information, such as a book containing a purely random stream of numbers that just hasn’t been recorded before. Entropy offers us no way to distinguish between the two and hence may not be useful as a direct measure of information.

How does this relate to my earlier thesis about information, entropy, and kurtosis? I’m not sure yet, but I’m thinking about it. I think it only requires a change in terminology to remain consistent. That is, I can remove all references to “information” and work only with the math of entropy and still have the essay be meaningful.

Victor Brochard

Wiki Source a ces livres par Brochard:
Les Sceptique grecs, La Méthode expérimentale chez les Anciens, et De la croyance

C’est gratuit et en francais, bien sur.

Parabole Chinoise

Nassim Taleb’s notebook discusses the “Parabole Chinoise” [In french, google translate does an ok job].

Climate

I don’t know much about climate change and I don’t have a position on it, except to say that we should tread carefully. But I do find two points interesting:

First, climate scientists are in the unusual position of arguing that while short-term phenomena (weather) are highly chaotic (butterfly effect), long-term phenomena (climate) are relatively stable (or at least can be modeled). This is the opposite of most other environments when we discuss the future. It is usually acknowledged that bigger and longer-term projects have much higher uncertainty in their schedules compared to short-term projects. In the markets, even with the “Central Limit Theorem,” participants are careful about the really long-term future and will readily agree that short-term market behavior is better grasped than long-term behavior. I’ve never seen anybody actually address why weather-climate uncertainty is switched relative to other systems.

Second, there is talk about “runaway global warming.” The model states that melting polar ice will release even more greenhouse gases into the atmosphere which drive temperatures even higher, melting more ice… and the cycle repeats. This model is invoking a positive feedback loop that will result in very high temperatures. A small change causes a relatively large state change in the climate. But this is pretty close to the definition of a chaotic system which cannot be modeled. It seems to me that climate can either be modeled (non-chaotic) or have large state-changes resulting from small perturbations (chaotic), not both.

Serendipity in Thailand

More notes from my Travelogue:

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In the Suan Pakkad Palace Museum in Bangkok, there is a letter. The curator wouldn’t let me photograph it, but I was able to copy it out by hand. The letter talks about a major archeological find in Ban Chiang, Thailand. It is now a UNESCO World Heritage Site. It is a major site for understanding prehistoric settlements in Asia and an important source of clues for the cultural, technological, and social evolution of humans. The author of the letter discovered the site.

The letter, to his family:

August 12, 1966 …

Here is a short message about my feelings on Ban Chiang: In reflecting on the Ban Chiang find, I became more humble and spiritual, less scientific and confident of human dominance over fate.To use a Thai reference, some Theveda must have been responsible for the Ban Chiang find for I did not consciuosly seek out ancient Thai history. In the summer of 1966, I was looking for villages in the Isan in which to interview villagers for my senior thesis at Harvard College.A chance acquaintance sent me to Ban Chiang. I could have gone to any of hundreds of villages.Why did I end up in Ban Chiang? Luck.

Then, I had been in the village, walking around, for some time and never saw the pottery in the ground. But on this day I lost control and tripped right where there were many pots in the ground.When I fell forward, face down on the ground, there was a pot just under my nose. I had to see it. It was as if some force outside me wanted me and not somebody else to find the pots. So, as soon as I realized that they were beautiful and old, I became the recipient of a sense of responsibility that it was my fate, my duty, to tell the world about this find.The important event, and my task, had not been planned at all. Just so, many important things happen to people in life which are not planned.

Stephen B.Young

A very multi-faceted story. Ranging across countries, disciplines, and belief systems.

Voodoo and Inequality

I’ve always had a fascination for rainforests. In early 2007, I spent a couple of months at a research station in Amazonian Peru. While there I met a prominent ethno-biologist studying indigenous tribes in the area. We got to talking about the origins of Voodoo in the area. These notes first appeared in my Travelogue:

==

Voodoo. There are many indigenous tribes in the rainforest. Some of these remain uncontacted by western civilization, while others have been exposed heavily. I met a famous ethnobiologist and anthropologist at the research station and we had an interesting conversation about one of the tribes he had studied well: the Machiguenga.

The concept of voodoo did not exist in these tribes before contact with western civilization. Back then, the spiritual aspects of their life was intricately connected to the rainforest. In those days there were relatively stable and equal communities. If there was any inequality, it was connected to such things as how many more boars a hunter killed relative to his peers. The inequality was a couple of standard deviations from the mean at the most.

Once the tribe was exposed to the western world and associated material goods, inequality increased considerably and suddenly. The anthropologist gave the following example:

  1. Person A goes to town and buys himself a radio. This is a highly desirable object and his social status shoots up immediately in the community.
  2. Person A sees Person B giving him the evil ! eye for some reason.
  3. Person A happens to fall ill.

In the earlier days, the illness would have been explained away with the suggestion that the rainforest gods were angry or some such explanation. Now, however, with his increased social status, Person A invokes Voodoo and basically explains the illness by saying: “Person B was so envious of me and my new found radio, that he cast a voodoo spell on me.”

This belief is held very firmly. When the rainforest gods were invoked, a quick herbal remedy would be found to heal the illness. Now that Voodoo is considered the cause, no serious attempt is made to cure the illness. Instead, Person A casts a Voodoo spell on Person B and a tit-for-tat revenge cycle begins. All this even though Person A will readily admit that he hadn’t actually seen Person B do any real voodoo to begin with.

==

Note the shift from Mediocristan to Extremistan and the impact on the search for causality. We don’t want to make the same mistake and announce that one caused the other, but the shift is interesting nevertheless.

On Being a Fool

Nassim Taleb’s latest interview with The Sunday Times is very well done and talks about the underemphasized issue of behavior in the absence of Black Swans. I had been thinking about the same topic over the last few days. Here are my thoughts:

The part of TBS that discusses being a fool in the right places is equally important as the part that discusses being conservative in Extremistan domains. In that respect, the chapter titled “Half and Half, or How to Get Even With the Black Swan” is my favorite and contains a succinct and personal kernel of the whole book. I think even the folks who understand the Extremistan ideas did not take away all that there is in the book on the other aspect: behavior in Mediocristan.

Let us consider a system of thought where “there is no logical reason to prefer one action over another.” In Extremistan domains, where the unknown plays a large role, it is beneficial to lean towards this system of thought. For example, if you are asked to invest in N securities, it is best to either invest in all of them, or none (if that’s a choice), instead of tunneling and showing a preference for a few over others. In this domain it is useful to value all choices equally beforehand, so we should not prefer one action over another.

However, it is possible to take this too far and apply this system of thought to Mediocristan domains. This will clearly lead to inaction. In a transaction involving exchange of goods, money, or time, a person who treats all choices as equal gains nothing from entering the transaction. He will exchange Object X for Object Y, but if he values X and Y equally, he gains nothing from the transaction. En fait, he will incur a transaction fee and come out at a loss. This would imply he is better off doing nothing at all, if he truly values everything equally (including his time). For example, if he knows that an MBA is valued fairly (it probably is, given the competition) and he values money and the MBA education equally, he has no reasons to apply at all.

So Taleb rails against the application of Mediocristan tools in Extremistan domains, but he is careful to point out that Extremistan thinking should not be brought into Mediocristan domains and he stresses the opposite. Many miss this point, and he emphasizes:

Few understand the beauty in the story of Apelles; in fact, most people exercise their error avoidance by repressing the Apelles in them.

If one decides to be hyperconservative in Extremistan domains, it becomes necessary to become hyperaggressive in Mediocristan domains. Otherwise, a part of us, the part designed to take risks, will be amputated.

So it becomes important to cultivate tastes, likes, and dislikes in harmless things. There are perils to being too open minded and valuing everything equally. The process of living in Mediocristan is trading-off what one doesn’t mind losing in order to acquire what one desires. For someone who has no particular likes or dislikes, this becomes very difficult and can lead to inaction. The stronger the likes and dislikes, the easier it will be to make decisions and trade-offs. These trade-offs are acceptable because the worst-case-scenario is clipped, these decision cannot hurt us more than a certain amount. A little diversification will wash the negative consequences out.

Creating an emotional differential between harmless choices in Mediocristan is what drives us - it generates action. This can be cultivated. It is difficult to put this in terms of “Gaussian risks,” so something may be lost in translation when the difference between Mediocristan and Extremistan is introduced.

Both takeaways from the book are important. Applying the one without the other leaves you lacking something important.

Efficiency and Robustness

I took a few business courses at Stanford. They were pretty entertaining and my favorite was a course on Supply-Chain Management. It was a series of case-studies that all started or ended with: “Rob looked out of his office window overlooking the hills in Palo Alto and wondered how he was going to …”

Invariably, the case-studies had consultants who would come in and save the day. More often than not they would either move the supply-chain from an existing centralized system to a more distributed process or vice versa. In both cases there would be very rational sounding reasons for doing so, and indeed the case study would cite much increased growth and productivity at the host company after the consultants has done their thing.

I never quite understood how one would make the decision before-hand on whether to go with a centralized or distributed process. In any case, it was always presented as a binary decision - it was never framed as a tradeoff.

Because that’s what it really is. Anytime somebody moves the slider on the efficiency scale, the system will become more or less robust. The higher the efficiency, the less robust the system. A highly efficient centralized system is much more vulnerable to single-point-of-failure situations. A more distributed approach, while being more resistant to single-point failures, is less efficient. An efficient system will also fail very efficiently.

Unfortunately, competition pushes systems to move towards higher and higher efficiency, all the while ignoring the massive drop in robustness for each player. Robustness is a difficult quality to measure compared to efficiency, and is thus rarely included in cost/benefit analyses. Efficiency gains can be quoted in percentage terms, but robustness measures only rely on “messy” scenario analysis that are difficult to enumerate and ask people to imagine the unknown.

While efficiency has been reified, robustness remains an elusive measure that few take into account until a failure actually occurs.

Entropy, Negentropy, Information, and Uncertainty

I am making some more progress with the entropy angle. But there is enormous confusion in terminology and fundamental concepts when it comes to entropy.

In various journal papers, “entropy” has been taken to mean information, randomness, disorder, uncertainty, increased order. In others, it is “negentropy” that takes these meanings.

A note on the NIH website talks about “Information is not Entropy, Information is not Uncertainty,” and has a nice quote:

The story goes that Shannon didn’t know what to call his measure so he asked von Neumann, who said `You should call it entropy … [since] … no one knows what entropy really is, so in a debate you will always have the advantage’

More as I clarify my thinking and stick to one set of terminology and concepts.

Unrepeatable

One of the pillars of the scientific method is repeatability. It should be possible for an experiment performed in one lab under certain parameters to be repeated under exactly the same conditions in any other lab.

Part of this requires at least some parameters that are controlled and easy manipulated by the scientist. From wikipedia on Dependent and Independent variables:

Dependent variables and independent variables refer to values that change in relationship to each other. The dependent variables are those that are observed to change in response to the independent variables. The independent variables are those that are deliberately manipulated to invoke a change in the dependent variables.

So at least a few parameters need to be easily controlled and manipulated. This allows nice graphs where one axis represents the dependent variable and the other the independent variable.

The result is a plethora of studies where men and mice are put on treadmills and made to walk or jog at a constant pace. The resulting hormone response, weight change, etc. are studied against varying speed, distance, time and so on. These are easily repeatable and result in nice graphs.

The problem is that most natural systems behave as far from equilibrium systems with a lot of “novelty” and enormous variation in the systems. Predators describe levy-distributions in their energy expenditures and predatory ranges. These distributions have highly unstable means and variances (if they exist at all). A levy-distributed range looks like this:

levyflight.png

There are large jumps that are unique. The mean is finite but unstable and converges only with extremely large data sets. The variance doesn’t even exist. So a hypothesis along the lines of:

Levy-flights with infinite variance result in a specific response in other variables.

This can be tested by experimentation, but said experiments will be very difficult to replicate. The sample mean jumps all over the place. There really isn’t any meaningful parameter to be used as an independent variable. Nice graphs are out of the question. Asking people to vary their energy expenditure based on a levy-distribution on a treadmill will result in a lot of data, but no two experiments will be alike - that’s the whole point of the levy-distribution. Even two successive experiments at the same lab will be different (else they aren’t really following a levy-distribution).

I’m not saying the type of studies being done now are useless, I’m just pointing out that there’s a fundamental problem with certain types of hypothesis. This wouldn’t be a problem except that so many systems in nature exhibit this levy-distributed behavior. And there’s a huge hole in experimentation because the graphs don’t come out nice and the experiments aren’t repeatable.