The Roman Philosopher Lucius Anneaus Seneca (4 BCE-65 CE) was perhaps the first to note the universal trend that growth is slow but ruin is rapid. I call this tendency the "Seneca Effect."

Friday, May 21, 2021

The Rt Factor in the Pandemic: Is it Useful for Anything?


by Ugo Bardi, 

In these notes, I do not intend to replace the epidemiology specialists, my purpose is informative and tries to provide some data and some useful information to everyone in this situation, where the pandemic has become more a political issue than a scientific one. So, if we are to make informed decisions, we need to have the tools to understand what we are talking about, very difficult in the current cacophony of data and reasoning. Here, I have done my best to clear up the Rt factor issue using as an example a hypothetical epidemic, "bluite", which causes you to turn blue like the characters in the movie "Avatar". 

Note, this post was translated and adapted from my Italian blog Medio Evo Elettrico."  It still contains references to the Italian situation. But I think most of it is of general interest.


You surely noticed how in the discussions about the pandemic, the "R factor" is very popular. This factor, expressed as "Ro" or "Rt",  seems to give us useful information in a simple form, and we all know that politicians are always looking for simple solutions to complicated problems. And it is also based on the Rt factor that many governments decide on their restriction policies.

However, I bet that neither the politicians nor many of the tv-virologists who populate the media really understand what exactly this Rt factor is. In the real world, things are never simple and the R factor is not an exception to the rule. As Professor Antonello Maruotti  (1) noted the use of the Rt factor could result in a "persistent blindness on the part of political decision-makers." 

So what exactly is this Rt? How is it determined? How useful is it? And is it really a parameter on which it is worth basing all the restrictions policy that the government is doing? Let's try to understand how things stand.

A definition you can easily find all over the Web is that the Rt factor is “ the average number of people infected by an already infected person over a certain period of time."

There is a big problem, here. If we take the definition literally, it means that the epidemic can never go down. If there is at least one infected person, it will always infect someone else, and so the epidemic will grow forever. Clearly, the definition above is incomplete. We also need to take into account the people who recover (or die) in the time interval considered.

The matter is made more complex by the fact that in epidemiology there are two similar terms, one is called Ro and the other Rt. To give some idea of ​​the confusion, read the Wikipedia article on Ro, where you'll find that the definition of Ro "is not universally shared" and that "The inconsistency in the name and definition of the parameter Ro was potentially a cause of misunderstanding of its meaning." In short, a nice mess, to say the least. (this is from the Italian version of Wikipedia. The English one is better, but confusion reigns anyway)

Now, I understand that those who are specialists in a certain field tend to keep in the dark those who are not. But it seems to me that they are a little exaggerating, here. So, let's try to extricate ourselves from the various involved and misleading definitions, the best way to understand this story is to consider that a virus is a living creature so that biological laws and definitions apply. So, Rt in epidemiology is nothing else but the net reproduction rate parameter in biological populations.

This is an easily understood concept: take a population (say, rabbits). Consider the number of bunnies born for each generation: that's the "reproduction rate." Then, consider the number of rabbits that die in the same period of time because they are old, or they are eaten by foxes. Take the ratio of births to deaths and you have the net reproduction rate: if more rabbits are born than die, it must be Rt> 1. It is the opposite if Rt <1. A virus population is no different from a rabbit population in terms of growth or decline. Viruses multiply when someone infects someone else, but they die when someone is healed (or dies). 

How about Ro? It is simply the net reproduction rate at t=0, that is at the very beginning of the epidemic when there are no recovered and immunized individuals. 

These are the basic points. Then, it is always easier to understand something when it is expressed in terms of a concrete example, so let me propose an explanation based on a hypothetical epidemic that I call "bluite." There is some math in the following, but if you are willing to spend some time on that, you can develop a good "mental model" of how this Rt factor works.


The "bluite": a simplified epidemic example

Let's imagine a hypothetical infectious disease that is transmitted by contact, let's call it "bluite" because it makes you turn blue.
Incidentally, a disease that turns people blue really exists, it's called "argyria,the result of being exposed to silver salts. Some people ingest silver as an alternative therapy for certain diseases, not a good idea unless you want to find a job as an actor on the set of a science fiction movie. But let's not go into this, in any case, argyria is not infectious.

So, let's imagine that bluite arrived on Earth from the blue (indeed!). Let's also assume that bluite is a 100% benign disease. That is, it does not cause unpleasant symptoms and does not kill anyone. Hence, no one takes special precautions against it. Let's also assume that those who have been infected become immune forever, or at least for a long time. But their skin remains slightly grayish for some timeFinally, let's assume that bluite has a very short infection cycle: in one day it passes, and this applies to everyone. 

So, let's imagine that we counted, on a certain day, the number of blue-skinned people passing by on the street. Let's say that we counted 1000 people and that 10 of them had blue faces. If the sample is statistically significant, we can say that 1% of the population is infected. If we extrapolate to the whole population, suppose we are in Italy with 60 millions of inhabitants, it means that there are 600,000 people infected with bluite. This fraction is called "prevalence" in the jargon of epidemiology.

So far, so good, but that doesn't tell us anything about how the epidemic is evolving. For this, we need data measured as a function of time. Let's assume then that we do the same measurement again the next day. We find that there are now 20 blues, again out of 1000 people: this number of new infections in a certain period is called the "incidence." In this particular case, since the infection lasts one day and we make one measurement per day, the incidence is equal to the prevalence. 

Can we now measure the Rt factor? Sure. We said that Rt is the net reproduction rate of the population. So, over a one day interval, we have 20 newly infected people, but 10 people recovered in the meantime. It follows that Rt = 20/10 = 2. Easy, isn't it? (note that I chose the data in such a way as to have a nice round number as the result).

Easy, but you have to be careful when you extrapolate this procedure. At this point, you could say that if in one day the number of infected people have doubled, their number will continue to double every day. That is, 10, 20, 40, 80 ... etc. 

This is the mistake made by those who speak of the "exponential growth" of the epidemic; it is an acceptable approximation only in the very early stages of diffusion. Do some math, and you will see that if the number of cases of bluite were to double every day, in a week, there would be more people infected than the whole population. Slightly unlikely, to say the least.

The mistake here is to confuse the net reproduction rate (Rt) with the (simple) reproduction rate. They are not the same thing: the former is the growth rate of the population, the latter is the probability that a "blue" has to infect a "normal" when an encounter takes place. In general, we cannot directly measure the reproduction rate, we can only estimate it. Just to propose some numbers, let's assume that, on average, everyone in the population encounters 4 people every day at a close enough distance to infect them. Since there were 10 blues in the beginning, and 20 new ones came out, it would seem that the probability of infection at close range was 50% for each encounter. But is not so.

Not all people a blue encounters are "normal," that is susceptible to infection. We said that there were 10 blues in the population when the measurement was made and we may also assume that there were 10 grays (previously infected, now immune). It follows that only 98% of the population are susceptible ("normal") people. So the probability for a blue to infect someone is not 50%. It is 0.5 / 0.98 = 51%. It's a small difference, but it's the key to the whole story. 

To understand this point, first let's estimate the value of Ro, when the first blue alien from the planet Pandora landed and began infecting Earthlings. At that time, the whole population (100%) was susceptible to infection. Since we found that the simple reproduction rate is 0.51, it follows that Ro = 0.51x4 = 2.4. This was the initial value of the net reproduction rate when the epidemic had just begun.

But Ro has to do with the past, let's instead calculate how things are expected to develop in the future. The next day, the 20 infected people will each interact with 4 people, and a total of 80 people will be exposed to the virus. Not all of them will be susceptible, the number will be equal to 1000 (total number of people) - 20 (the blues of the day) - 20 (the grays of the previous days) divided by the total population. That is 960 people, or a fraction of 96%. It follows that the 20 infected people will generate 20 * 0.51 * 4 * .96 = 39 new infected individuals and not 40, as it would have been the case if the number of infected people had remained constant. At this point, Rt has shrunk to 39/20 = 1.96. You can see that Rt will shrink a little every day that goes by

From here, you can have fun doing a calculation with an excel sheet, but I did it for you. Here are the results, the red curve is a fitting with an asymmetric sigmoid curve:

 

Note how the curve of the daily infections (red) has the typical “bell shape" of epidemic curves (mathematically, it is the same as the "Hubbert Curve" in petroleum extraction). Note also that we didn't assume that the infection was cured or that there were precautionary measures in place: distances, face masks, nothing like that. Infections go to zero simply because fewer and fewer people remain susceptible. 

In this particular case, the number of people who contracted the infection stabilizes at around 74% of the total at the end of the epidemic cycle. The rest will never be infected. Do you see how “herd immunity” works? Over a quarter of the people in the population do not become infected, even though the virus was highly infectious at the beginning and no one took precautions of any kind. It is an intrinsic property of the spread of an epidemic.

Notice also how the curve for Rt always goes down, at least in this simplified case. You see that when the epidemic is at its peak, Rt is equal to one. Eventually, it stabilizes around 0.5. Depending on the various parameters, it can stabilize on different values, but always less than 1. 

 

Effect of restrictions on bluite

Now let's have a little fun using this model to see the effects of restrictions. The idea of things such as "social distancing" or face masks is that they reduce the likelihood that the virus will be transferred from one person to another. This is sometimes called "crushing the curve". 

First, let's plot again the results we obtained above without assuming any restrictions.

 

Now let's try to reduce the likelihood of the infection by 25% by some unspecified method. Here are the results

 


You see that the curve is indeed "crushed". But also note that the duration of the outbreak is longer and that the final value of Rt, contrary to what one might expect, increases slightly instead of decreasing. As for the total number of infected people, the restrictions have reduced it from 74% to about 58% of the population. If we assume that the effect of the restrictions is even greater, say to 50%, we can squeeze the curve even further and reduce cases to about 15% of the population. By further reducing the likelihood of infection, the epidemic just doesn't develop. Finally, note that this is the result of having imposed the restrictions from the start of the epidemic cycle and of maintaining them for the whole cycle.

Let's now try to see what happens if, as it is more likely, the restrictions start at some moment after the epidemic has already started and they are maintained for a limited time window. In the graph below, restrictions with a 25% reduction effect are assumed to have been put in place on the third day, and reopening occurs on the ninth day.


Notice that the contagion curve more or less retains the "bell shape," although it is now a bit skewed. Instead, the Rt factor shows fairly sharp discontinuities. Note also that the infection lasts longer. We have reduced the intensity of the outbreak in exchange for a longer duration. In these assumptions, the total number of cases is intermediate compared to the two previous examples: the number of infected people stands at 67%.

You can have fun by changing the parameters, but the results can be summarized by noting that using restrictions to bring the infection curve to zero is almost impossible. The effect of the restrictions is seen as a discontinuity in the Rt factor curve better than in the contagion curve. 

 

The real world

All this applies to a hypothetical epidemic that we have called bluite and to a simplified model. In the case of a real epidemic, the situation is more complex, but the results are not very different. The basic prediction of the model, that of the "bell" shape of the contagion curve, is confirmed by real-world data. In the figure, we see an example, a recent cholera epidemic in Kinshasa, Congo.

 


In this, as in many other real cases, we see a "bell-shaped" curve. Note how the number of cases never really goes to zero, contrary to what the model predicts. The pathogen becomes "endemic", ready to return to the scene when it finds favorable conditions to start over. 

What can we say about Rt in the real world? Here, the calculation is much more complex than for the hypothetical bluite. The infection does not have a fixed duration and it is also possible to get re-infected. Then there are the various uncertainties in determining the number of infected people, the delays with the availability of data, the effects of mutations, and more.  

The result is that calculating Rt for an ongoing epidemic is a complex matter that is left to specialists.  With these methods, the prediction that Rt should fall with time during each epidemic cycle is generally verified, but it is also true that many epidemics have multiple cycles, so the Rt factor can also reverse its trend and restart growing for a certain period.

Here are some recent data (for Italy) from Maurizio Rainisio's FB site (2). Here, you see an equivalent of Rt (which Rainisio calls the "Weekly Growth Rate"). The epidemic had two phases, probably due to seasonal factors, or perhaps also to the effect of the "variants" of the virus. Notice how the peak of the most recent phase corresponds to Rt = 1.

 

Here, it is very difficult to see an effect of the various red, orange, yellow, etc. zones (as they were created in Italy). For example, Rt showed a steep rise at the beginning of February 2021, while it started to decline around February 20. Is there a correlation with any specific action taken by the government that can be seen in the curve?  Maybe, but it is certainly weak.


 Conclusion: is Rt any good?

The usefulness of something always depends on the context. A submachine gun can be very useful in certain circumstances, but it's a bad idea if it's in the hands of a Taliban, especially if there's a tv shop nearby. This also applies to statistical models if they end up in the hands of people who don't understand them.

Thus, in the first place, the calculation of the Rt factor does not give you, and could never give you, any more information than what is already present in the curve of the trend of the epidemic. We saw that epidemic curves tend to have a "bell" shape so that it is possible to qualitatively understand whether the epidemic increases or decreases simply by the shape of the curve. The calculation of the Rt factor may be more sensitive to the trend, but it adds no more information. 

Then there is the problem that the value of Rt can tell us if the epidemic grows or declines, but nothing about the number of infected people. Clearly, there is a big difference if we have 100 infected people out of 1000 or if we only have 10, but the value of Rt could be the same. And this is not a detail: depending on the absolute value of the number of infections, hospitals may or may not risk becoming saturated. But the Rt factor, alone, tells us nothing on this point.

Above all, when the infected are few, the importance of the inevitable measurement errors and approximations changes (3). If you have 100 cases out of 1000, an error of a few units has little effect: whether they are 101 or 99, nothing changes. But if you have two cases on a certain day, while you had just one the day before, you would think that Rt is much larger than 1, and you should sound the alarm. In this case, the sensationalism of the media is a big problem. And so you could find yourself shutting down an entire country because of a statistical fluctuation.

But the biggest problem is precisely in the concept. As I said before, many people don't understand how an epidemic mechanism works and truly believe that an epidemic grows exponentially until everyone is infected. And, consequently, they are convinced that if we see that the contagion curve decreases, this is due solely and only to the restrictions. You find it explicitly written, sometimes: "the Rt factor measures the effect of the containment measures". But this is absolutely not the case!

Not that there is no way to slow down an ongoing epidemic! Vaccines, for example, force the achievement of immunity in individuals and cause herd immunity to be achieved more quickly. But if you see the epidemic waning or rising, you don't necessarily have to relate it to restrictions or vaccines alone. The epidemic has its own cycle, you can slow it down, but you have to take that into account.

Unfortunately, the debate has arrived at the conclusion that the only thing (aside from vaccines) that can stop the epidemic are restrictions. And the restrictions have a huge cost not only on the economy but also on the health of citizens. But until we think about it we will continue to insist on measures that may be exaggerated and not justified in comparison to the costs.

In essence, the problem is that many people, even among policymakers, cannot read a Cartesian graph and have no idea how an epidemic cycle works. So, they tend to rely on a single magic number, "Rt" for simplicity. But the situation does not lend itself to extreme simplifications and, as always, ignorance pays only negative dividends.

 

References

1. https://www.romatoday.it/attualita/coronavirus-professore-lumsa-sbagliate-decisioni-su-rt.html

2. https://www.facebook.com/La-Peste-111172767208456

3. http://www.radiocora.it/post?pst=39381&cat=news



Monday, May 17, 2021

Give a man a fish, and he will eat for a day. Teach a man how to fish, and you'll find that he already knew that better than you



The UN program "The Ocean Decade" is starting this year. It is supposed to be ten years of research, assessment, and development of what the world's oceans can provide to humankind and how that can be managed in a sustainable manner within the concept called "The Blue Economy". It is a good idea, in general, but from what I saw up to now, many of the participants in the program are still anchored to the view that the Oceans contain large, untapped resources that can be exploited within the model of "sustainable development," normally understood in terms of economic growth. 

That may be a remarkable misunderstanding. As we explain in our recent
book "The Empty Sea," the world's oceans do contain enormous resources, but it is also true that -- like all biological resources -- overexploitation is a misunderstood risk that always takes people by surprise. 

It is a mistake done over and over: when the yield of a fishery goes down, governmental agencies think it is a good idea to provide fishermen with more powerful boats and other technological tricks. It works, just until it doesn't. Then, it makes things worse. Overexploited fish stocks collapse, leaving fishermen with plenty of useless hardware and the sea reduced to a desert. 

Below, Paul Jorion tells a story that provides much food for thought in this field: the pretense of Western "experts" to know more than the local African fishermen and to help them by means of more powerful engines and better fishnets. And, as usual, the result was plenty of wasted money, possibly worse than that. The apparent inability of the Fishermen of Benin to produce as much fish as produced in nearby regions was not because they were bad fishermen. It was because of the lack of fish off the coast of Benin.

"Upwelling" is a concept discussed in some detail in our book, it is the oscillating phenomenon that characterizes the "El Nino/La Nina" cycles off the Peruvian coast. Upwelling brings nutrients to the surfaces and generates the growth of the fish stocks. The lack of upwelling has the opposite effect. The sea is a complex environment, you can see it as a giant holobiont that goes on in cycles, as living systems often do. You must understand these cycles, you can't fight them with technology. If you try, you'll destroy the very resources that make you survive. In this case, the fishermen of Benin had perfectly well understood how to deal with the lack of upwelling: you don't fish. 

Jorion doesn't say what happened with the program, but he hints that it was carried out and that it failed, badly -- as it had to. Will we ever be able to understand that growth is not always the solution for all problems?




AFRICA AND ME III. FISHERMEN NOT KNOWING HOW TO FISH
MAY 15, 2021 PAUL JORION 



By Paul Jorion

The FAO project in Benin aimed at developing fisheries in the country. It had been observed that, unlike neighboring countries, coastal fishing was languishing there. Benin was living at that time under a Marxist-Leninist regime and it was considered in high places at the United Nations that the time had come to intervene also in countries whose government was of this type.

Our project was sponsored by Denmark and Japan. Its objective was to discover the reasons for the weakness of fishing and to remedy them. As is often the case with development aid projects, the conclusion we would come to was pre-established: we could read it in the fact that Denmark had offered nets and Japan Yamaha outboard motors.

A preliminary survey on the situation in Benin had been carried out a few weeks before my arrival by a British anthropologist colleague: Jacob Black-Michaud, who had highlighted in a report of about thirty pages the mediocrity of the local fishery. This report established that, for some unknown reason, fishermen in Benin did not manage to fish with the same skill as observed in neighboring countries. The rationale for the United Nations to come to their aid lay there.

I had a real affection for Jacob Black-Michaud, whom I had previously had the opportunity to meet during an evening in Cambridge. His career had been similar to mine: from the university environment to anthropology applied to development projects in the field. But unlike me, who loved to deal with reality, he lived the transition from university life to that of a bush adventurer like a downfall. I would share his sentiment, but at a different time: when, fourteen years later, at the age of fifty-one, I was recruited for my first job in the United States: a programmer in a subprime loan company. I would have the opportunity to ask myself then, like him in Benin: "How did you get there: what happened to you?" 

It is always with real emotion that I think back to him and to our conversations about the deep meaning of our profession and the challenges of what is called "development aid". Black-Michaud had lived in Ceylon an experience that had transformed him on a personal level but also made him cynical on these subjects. I remember our last conversation: I didn't share his belief that anything we did was wasted (and my own experience would convince me that it was indeed not, despite the sheer size of some obstacles to come up against.) and I told him.

He wrote to me shortly before the Christmas holidays. Unfortunately, I was not surprised to learn a few weeks later that during a ski tour he had fallen to his death, having fallen from an overhang.

It was therefore necessary to find out why the performance of coastal fishing in Benin was so disappointing. In Houat, I went to a good school for the fishing profession, and also to a good school in Cambridge, in terms of mastering analytical tools. The first thing I did, with the help of a team of "statisticians" that the project had enabled me to recruit, was a census of the eight fisherman camps in Benin (including Beninese and Ghanaian people) who had been selected for our mission. project.

A census allows, among other things, to build an age pyramid. This is a very simple exercise in graphing the age composition of a population. After having counted the people of each sex of such or such age, this number is represented on a horizontal scale, the men on the left and the women on the right, by convention. The age groups are stacked along a vertical scale graduated according to age: children between zero and one year old are shown at the base, while the highest age group is shown. which belongs to the oldest person still alive.

For each age group, a line is drawn whose length is proportional to the number of people of that age. As with aging and accidents people die, the general shape tapers upwards. In traditional populations ravaged by very high infant mortality, the figure generally had the shape of a pyramid, the steps of which were made up of age groups. 

The pyramid is generally asymmetrical at the top: thicker on the female side for the reason that everyone knows that in all societies, women live longer than men and there are therefore more women than men at the top.

However, the age pyramids of my villages all had the same unexpected shape: asymmetrical, showing a very noticeable dip on the side of men in the age groups of fifteen to forty-five years. The interpretation was unequivocal: men in their prime were missing out. Where could they possibly be?

I went to see the women: "Where are the men I asked?" "In Liberia, Gabon, Congo!" They replied, adding:" Where there is fish. Not like here!" The men followed the fish, often leaving the women behind. Sometimes the women followed their men, in trucks, along the coast. I would discover that the Beninese had the reputation of being outstanding fishermen wherever they went fishing, returning periodically to the country, either seasonally or after stays that lasted several years. The men we saw in Benin, for example practicing the "beach seine" (this long pocket-shaped net that is spun off with the help of a boat after leaving one of its two ropes retained by a team on the beach, and which is then folded down after having brought the second rope back to the beach, the two teams then hauling the pocket by its two ends), were either those occasionally returning, those who came to see their families, or, and essentially, the disabled and the sick ones. I had involuntarily innovated, I had introduced a new style in development projects in West Africa: I had spoken to the people we said we wanted to help!

The explanation for the absence of fish in large quantities in Benin is the absence of "upwelling ", a thermal phenomenon: the upwelling of cold water from the depths near the coast, allowing an algal bloom. diatoms on which the larvae of mollusks and crustaceans feed. The upwelling allows the plankton (phytoplankton and zooplankton), basic food fish, to grow. The upwelling moves along the coast of West Africa but it rarely develops in the Gulf of Guinea, in the area stretching from Benin to the west of Cameroon. In this area, fish are rare.

It was neither laziness nor incompetence that explained the mediocrity of fishing in Benin but the thermodynamics of the oceans. I explained this to my colleagues. It turned out very badly: the remedies available to us were, as I have already said, of two kinds: Danish nets and Japanese engines. The only explanations considered for the poor fishing in Benin were inappropriate equipment and the incompetence of the fishermen. Unfortunately, the real explanation refused to fit into this pre-established mold.


Friday, May 14, 2021

A Concise History of the concept of "Hydrogen Economy"

Reposted from "The Hydrogen Skeptics" blog. 

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The concept of "hydrogen economy" has a distinct "1960s" feeling. It is the idea of maintaining the lifestyle of the post-war period, with suburban homes, green lawns around them, two cars in every garage, all that. The only difference would be that this world would be powered with clean hydrogen. It all started with the dream of cheap and abundant energy that nuclear plants were believed to be able to produce. The idea changed shape many times, but it always remained a dream, and probably will continue to remain a dream in the future.

 

by Ugo Bardi

Before discussing the history of the concept of "hydrogen economy" we should try to define it. As you should expect, there are several variations on the theme but, basically, it is not about a single technology but a combination of three. Hydrogen would be used for: 1) energy storage, 2) energy vectoring, and 3) fuel for vehicles. 

This "hydrogen triad" misses the fundamental point of how hydrogen should be created. Often, that's supposed to be done using electrolysis powered by renewable energy but, alternatively, from natural gas, a process that would be made "green" by carbon sequestration. There are other possibilities, but all have in common being multi-step processes with considerable efficiency losses. And the fact of never having been proven to be economically feasible on a large scale.

Indeed, the immediate problem with replacing fossil fuels is not vectoring or storage, surely not powering individual cars. It is the enormous investments needed to build up the primary production infrastructure that would be needed in terms of solar or wind plants (or nuclear), which don't seem to be materializing fast enough to generate a smooth transition. Surely, not growing fast enough to be compatible with a relatively inefficient infrastructure based on hydrogen. Nevertheless, the "hydrogen economy" seems to be rapidly becoming the center of the debate

Indeed, the Google Ngrams site shows two distinct peaks of interest for the concept, both grew rapidly and rapidly faded away. But it seems clear that a third cycle of interest is starting to appear, and that is confirmed by what we can read in the media.

So, why this focus on a technology that lacks the basic elements that would make it useful in the near term? As it is often the case, ideas do not arrive all of a sudden, out of the blue. If we want to understand what made hydrogen so popular nowadays, we need to examine how the idea developed over at least a couple of centuries of scientific developments.

That hydrogen could be used as fuel was known from the early 19th century. Already in 1804, the first internal combustion engine in history was powered by hydrogen. The first explicit mention of hydrogen as an energy storage medium goes back to John Haldane in 1923, where he even discussed the possibility of using "oxidation cells" that we call today "fuel cells," invented by William Grove in 1838.

But these ideas remained at the margins of the discussion for a long time: no one could find a practical use for a fuel, hydrogen, that was more expensive and more difficult to store and use than conventional fossil fuels. Things started to change with the development of nuclear energy in the 1950s, with its promise of a new era of abundance. But, in the beginning, hydrogen found no role in the nuclear dream. For instance, you wouldn't find any mention of hydrogen as an energy carrier in the "manifesto" of the atomic age: the 1957 TV documentary by Walt Disney, "Our Friend, the Atom.

In the book derived from the movie, there was an entire chapter dedicated to how nuclear energy was going to power homes, ships, submarines, and even planes. But nothing was said about the need for fuels for road transportation. The atomic car was just briefly mentioned as "not a possibility for the near future." The engineers of Ford thought otherwise when, in the same year (1957), they proposed the concept of a nuclear-powered car, the Ford Nucleon. But nobody really believed that such a car could ever be produced. At the beginning of the nuclear age, there was no concern about climate change, and no one foresaw the need or the possibility of entirely replacing fossil fuels from the world's energy infrastructure.

The idea of hydrogen as an element of the new nuclear infrastructure started gaining weight only in the 1960s, in parallel with the problems that the nuclear industry was experiencing. The assessments of the world's uranium ores showed that mineral uranium was not abundant enough to support a large expansion of nuclear energy as envisaged at that time. But the industry had a technological solution: "fast" reactors that could be used to "breed" fissile materials in the form of plutonium. The fast reactor technology could have increased the duration of the uranium reserves of several hundred years, perhaps thousands. 

Fast reactors turned out to be more expensive and complex than expected, but the problem was not technological, it was strategic. The "plutonium-based economy" would have generated a gigantic proliferation problem. It was clear to the Western leaders that diffusing this technology all over the world put them at risk of losing the monopoly of weapons of mass destruction that they shared with the Soviet Union. 

So, if fast breeders were to be built, they needed to be only a few and to be very large to allow tight military control. They also needed to be large to exploit economies of scale. But that led to another problem: how to carry the energy to consumers? Electrical lines have a distance limit of the order of a thousand km, and can hardly cross the sea. The kind of plants envisaged at that time would be spaced much more than that from each other. It was at this point that the idea of hydrogen as an energy carrier crept in. It could have been used to distribute nuclear energy at a long distance without the need to distribute the reactors themselves. 

It was a concept discussed perhaps for the first time in 1969 by the Italian physicist Cesare Marchetti, He was, (now he is in his 90s) a creative scientist who proposed that just 10 gigantic fast reactors of a few TW each would have been enough to power the whole world. The reactors could be built on remote oceanic islands, where the water needed for cooling would have been abundantly available. Then, the energy would have been transformed into liquid hydrogen at low temperature and carried everywhere in the world by hydrogen carrier ships. In the image from one of Marchetti's papers, you see how an existing coral atoll in the South Pacific Ocean, Canton Island, could be converted into a Terawatt power nuclear central.

To paraphrase the theme of Disney's "nuclear manifesto" of 1957, the hydrogen genius was now out of the bottle. In 1970, John Bockris, another creative scientist, coined the term "hydrogen-based economy." In the meantime, NASA had started using hydrogen-powered fuel cells for the Gemini manned spacecraft program. It was only at this point that the "hydrogen car" appeared, replacing in the public's imagination the obviously unfeasible nuclear-powered car. 
 
It was a daring scheme (to say the least), but not impossible from a purely technological viewpoint. But, as we all know, the dreams of a plutonium economy failed utterly. With the oil crisis of 1973, the nuclear industry seemed to have a golden opportunity. Instead, it collapsed. We can see in the Ngrams how the concept of "fast breeder" picked up interest and then faded, together with that of nuclear energy. The reasons for the downfall of the nuclear industry are complex and controversial but, surely, can't be reduced to accusing the "Greens" of ideological prejudices. Mainly, the decline can be attributed to two factors: one was the fear of nuclear proliferation by the US government, the other the opposition of the fossil fuel industry, unwilling to cede the control of the world's energy production to a competitor. Whatever the causes, in the 1980s the interest in a large expansion of the nuclear infrastructure rapidly declined, although the existing plants remained in operation.

And hydrogen? The downfall of nuclear energy could have carried with it also the plans for hydrogen as an energy carrier, but that didn't happen. The proponents repositioned the concept of "hydrogen economy" as a way to utilize renewable energy. 

One problem was that renewable energy, be it solar, wind, or whatever, is inherently a distributed technology, so why would it need hydrogen as a carrier? Yet, renewables had a problem that nuclear energy didn't have, that of intermittency. That required some kind of storage and hydrogen would have done the job, at least in theory. Add that at in the 1980s there were no good batteries that could have powered road vehicles, and that made the idea of a "hydrogen car" powered by fuel cells attractive. Then, you may understand that the idea of a hydrogen-based economy would maintain its grip on people's imagination. You can see in the figure (from Google Ngrams) how the concept of "hydrogen car picked up interest. 

It was a short-lived cycle of interest. It was soon realized that the technical problems involved were nightmarish and probably unsolvable. Fuel cells worked nicely in space, but, on Earth, the kind used in the Gemini spacecraft were rapidly poisoned by the carbon dioxide of the atmosphere. Other kinds of cells that could work on Earth were unreliable and, more than that, required platinum as a catalyst and that made them expensive. And not just that, there was not enough mineral platinum on Earth to make it possible to use these cells as a replacement for the combustion engines used in transportation. In the meantime, oil prices had gone down, the crises of the 1970s and 1980s seemed to be over, so, who needed hydrogen? Why spend money on it? The first cycle of interest in the hydrogen-based economy faded out in the mid-1980s. 

But the story was not over. Some researchers remained stubbornly committed to hydrogen and, in 1989, Geoffrey Ballard developed a new kind of fuel cell that used a conducting polymer as the electrolyte. It was a significant improvement, although not the breakthrough that it was said to be at the time. Then, in 1998, Colin Campbell and Jean Laherrere argued that the world's oil resources were being rapidly depleted and that production would soon start declining. It was a concept that, later on, Campbell dubbed "Peak Oil." In 2001, the attacks on the World Trade Center of New York showed that we lived in a fragile world where the supply of vital crude oil that kept civilization moving was far from guaranteed. Two years later, there would come the invasion of Iraq by the US, not the first and not the last of the "wars for oil." 

All these factors led to a return of interest in hydrogen energy, stimulated by the popular book by Jeremy Rifkin, "The Hydrogen Economy," published in 2002. The new cycle of interest peaked in 2006 (again, look at the Ngrams results, above), and then it faded. The problems that had brought the first cycle to its end were still there: cost, inefficiency, and unreliability (and not enough platinum for the fuel cells). Besides, a new generation of batteries was sounding the death knell for the idea of using hydrogen to power vehicles. Look at the compared cycles of hydrogen and of lithium batteries.

 Note the different widths of the peaks. It is typical: technologies that work (lithium) keep being mentioned in the scientific literature. Instead, technologies that are fads (hydrogen) show narrow peaks of interest, then they disappear. You can't just keep telling people that you'll bring them a technological marvel without ever delivering it. 

At this point, you would be tempted to say that hydrogen as an energy carrier and storage medium is a dead platypus. But no, the discussion on the hydrogen economy is restarting, research grants are being provided, plans are being made. 

Did something change that's generating this new cycle? Not really, the technologies are still the same. Surely there have been marginal improvements, but hydrogen remains an expensive and inefficient method to store energy. So, why this new round of interest in hydrogen?

The vagaries of memes are always open to interpretation, and, in this case, we can suppose that one of the elements that push hydrogen back to the global consciousness lies in its origins of supporting technology for a centralized economy, the one that would have resulted from the widespread use of fast breeder reactors. In this sense, hydrogen is in a different league from that of most renewable technologies that exist and operate over a distributed network. 

So, even if the nuclear industry is today a pale shadow of what it was in the 1960s, there remains the fossil fuel industry to champion the role of centralized energy supply. And, obviously, the fossil fuel producers, who produce hydrogen from fossil sources, are those who are going to benefit most by a return to hydrogen, no matter how short-lived it will be. 

There may be another, deeper, reason for the success of the hydrogen meme with the public. It is because most people, understandably, resist change even when they realize that change is necessary. So, replacing fossil fuels with electricity-producing renewables is something that will force most of us to radical changes in our lifestyle. Conversely, hydrogen promises change with no change: it would be just a question of switching from a dirty fuel to a clean one, and things would remain more or less the same. We would still fill up the tanks of our cars at a service station, we would still have electric power on demand, we would still take two weeks of vacation in Hawai'i once per year. 

Unfortunately, people change only when they are forced to and that's what's probably going to happen. But, for a while, we can still dream of a hydrogen-based society that seems to be curiously similar to that of the US suburbs of the 1960s. Dreams rarely come true, though. 

 

Monday, May 10, 2021

Memes that Kill: Witch Burning and Other Extraordinary Popular Delusions


A modern interpretation of Anna Göldi, executed in 1782 for witchcraft in Glarus, Switzerland. She is said to have been the last witch killed in Europe, at least as the result of a formal trial. The story of the great witch hunts of the 16th and 17th centuries remains a mystery in many respects. What caused this folly to take hold of the minds of the Europeans? And what caused that folly to abate? It turns out that evil has a natural cycle of growth and decline. It is possible to accelerate the decline of a killer meme if good people get together in rejecting it.


In 1841, Charles MacKay published his "Extraordinary Popular Delusions and the Madness of the Crowds." It was a milestone: the first study in the field that today we call memetics, a term coined by Richard Dawkins for how ideas ("memes") spread in the collective human consciousness. MacKay was perhaps the first to state publicly that the great witch hunts of the late 16th and early 17th centuries were a form of collective madness. Not even Voltaire (1694-1778) had touched on that subject, despite his criticism of all kinds of religious superstitions

As exterminations go, the war on witches was not the worst on record. In Europe, it caused about 50 thousand victims over a little more than a century. But it was so shockingly cruel in targeting mostly helpless women that it is remembered to this day as a form of collective madness. With us, the expression "witch hunt" is even proverbial. 

The extermination of the European witches generated plenty of studies in modern times, mostly concentrated on the causes of the phenomenon. Explanations are many but, in general, it is agreed that it was related to the stress generated by the Reformation and the associated wars. Apparently, torturing and killing women was a form of stress release. The human mind must have plenty of serious problems, evidently, but this much we know not just because of witch hunting.

In any case, it happened, and we should be happy that it didn't last more than it did. But this generates another question: what made the hunt cease? It is a fundamental point: if we could understand what makes people stop believing in killer memes, we might stop them earlier. 

But few of the studies examining the war on witches make a specific effort to understand why the hunting ceased. The general idea seems to be that when the conditions that caused the hunt disappeared, things returned to their normal state. Sometimes, it is also proposed that the enlightenment movement put an end to the killings. Lesson and Ross (1) proposed in 2018 that:

"The seventeenth century, however, was the time of the scientific revolution, whose effects may have eventually eroded popular belief in witchcraft, eroding popular demand for witchcraft prosecutions along with it until witch trials could finally be easily abandoned by religious producers. "

Not to disparage a study that's excellent in many respects, but this interpretation seems to me completely wrong. The death penalty for people found guilty of poisoning or harming others had the aspect of a rational response of society to a threat that, at the time, looked real and documented. During the 16th and 17th centuries, science had little to say about whether or not it was possible to poison people using herbal concoctions or other methods.

As Chuck Pezeshky says, "truth is the reliable and valid representation of information that allows shared coordination of action inside a social network." In some cases, this social representation coincides with the scientific views of the matter, but that is not the rule and it is not even common. 

Finding witches and killing them was not just a job for inquisitors. The book by Trevor-Roper "The European Witch-Craze" (1991) tells us how widespread was the belief, and how intensely it was believed that killing witches was a social duty for everyone, to be done for the good of everyone else. A leader who didn't engage in witch hunting was seen as a bad leader. In some regions, expressing doubts on the idea that killing witches was a good thing could be dangerous. 

If truth is a social concept, then we need to understand witch hunting in a social context, in the form of the entities that we call memes. What makes memes live and die? Charles MacKay gives us an interesting hint when he says, “Men, it has been well said, think in herds; it will be seen that they go mad in herds, while they only recover their senses slowly, and one by one.” As I said, MacKay was a great innovator and this sentence, in itself, is a correct statement of how memes propagate. They behave exactly like physical pathogens in an epidemic: you are infected by others but you recover by yourself. 

Indeed memetic infections can be described by the same equations used in epidemiology, as we showed in a 2018 paper together with my colleagues Perissi and Falsini. Epidemics are the result of internal feedbacks that, in turn, are the result of the networked structure of the system. This internal structure generates typical "bell shaped" cycles. Witchcraft trials are probably the first historical case of a memetic cycle for which we have quantitative data (from Leeson and Ross, 2018 (1)).

The model tells us that the diffusion of the memetic epidemic is a collective phenomenon due to people being infected by others. Conversely, the epidemic declines because people become "immune" to the meme. The concept of "herd immunity" holds not just for physical epidemics, but also for virtual ones. It is what makes society eventually resistant to these killer memes. So, the first step to fight one of these memes is to reject them individually.

There is an even more fundamental point about the decline of killer memes here, well expressed by Trevor-Roper:

Third rank intellectuals and officials started saying that the craze was unjust and irrational. And what they said was taken for granted. Then came the intelligentsia, showing that what it said for two centuries was wrong because of some minor detail in the interpretation of the scriptures. And that was the end of the process.

This statement marks a difference between physical and memetic epidemics. A physical epidemic doesn't care too much about human hierarchies: a king may die of the plague just like any commoner. But, in a social network, the propagation of memes is affected by the hierarchical structure: people tend to trust authorities more than other sources of information. Trevor-Roper hit a profound truth with his statement: witch hunting declined because ordinary people ("third rank intellectuals and officials") started realizing that the meme was evil and that they (or their wives, sisters, or mothers) risked being burned at the stake. And they stopped believing in the official truth as spoken by the leaders.

So, it seems that if we want to stop evil memes, we have to do that starting from the bottom. We can't put too much hope in laws, tribunals, treaties, and lofty principles: they are all under the elites' control and can be bent, transmogrified, or ignored. The leaders, typically, have an interest in maintaining alive memes that are profitable for them. But the memetic war is fought at all levels of the social network. People may be dazed for a while by the "Shock and Awe" treatment they receive from above, but in the long run, they understand. We cannot expect to be able to stop evil all of a sudden but an evil meme cannot last for long when good people get together to fight it. If history is a guide, evil is surprisingly fragile.


An meiner Wand hängt ein japanisches Holzwerk
Maske eines bösen Dämons, bemalt mit Goldlack.
Mitfühlend sehe ich
Die geschwollenen Stirnadern, andeutend
Wie anstrengend es ist, böse zu sein.

On my wall hangs a Japanese carving,
The mask of an evil demon, decorated with gold lacquer.
Sympathetically I observe
The swollen veins of the forehead, indicating
What a strain it is to be evil.

 Bertolt Brecht 

 


1. Peter T. Leeson, Jacob W. Russ The Economic Journal, Volume 128, Issue 613, 1 August 2018, Pages 2066–2105, https://www.peterleeson.com/witch_trials.pdf

Thursday, May 6, 2021

Waiting for the end of the world - Sugar and the Information Paradox.

 


Amelia the Amoeba is the protagonist of a chapter of my book " Before the Collapse " (Springer 2019). She is a Naegleria Fowleri who has the rather nasty habit of devouring human brains but, apart from this, she kindly lent herself to be an example in the book of the mechanisms of growth of living creatures. In the following post, Alessandro Chiometti again uses the example of single-celled creatures for an interesting discussion on how our brains are destroyed, not by a brain-eating amoeba, but by an excess of available information. As a post, goes a little against the principles of modern "throwaway information", in the sense that rather than starting with trying to impress you with some flashy information, it gives you a little lesson in chemistry. But if you feel like working on it just a little, you'll see that it is a very interesting and thought-provoking post. It suggests that too much information is doing to us the same thing that too much sugar could do to Amelia: it kills our brains. And you'll learn some chemistry, too! (UB)


By  

We are used to call "sugar" a substance that is actually sucrose, one of the many existing "sugars" which are referred to in organic chemistry as carbohydrates. These compounds can be formed by a single molecule of any sugar (monosaccharides) or by several molecules (polysaccharides). Sucrose is a disaccharide formed by the union of the two monosaccharides, glucose and fructose.

Although these two molecules have the same brute formula (C6H12O6), they are very different: glucose forms a six-atom ring while fructose forms a five-atom one but, above all, it is glucose that is the primary source of energy for every living being.



The role of glucose in the various aerobic and anaerobic cycles is fundamental for the production of the molecule that carries energy in the cell (ATP) and therefore for any cellular engine that requires energy. All the nutrients we consume throughout our lives are transformed by the body into glucose or stored as precursors of this in various forms (e.g. glycogen), ready for use.

In short, it can be said that glucose, and therefore its various precursors present in nature, is what allows “life” as we know it, in the sense of mobility, movement, sport, physical and intellectual effort, growth. It is certainly no coincidence that when you want to cultivate a bacterial culture with a suitable growth medium, the sugar supply must always be guaranteed. Like us, bacteria and other microorganisms grow and multiply thanks to glucose and therefore to sugar, of course.

However, have you ever noticed that we can keep sucrose for decades at room temperature and nothing happens to it?

It does not go bad, molds do not grow and, if greedy children or ants do not get their hands on it, even after years we find it exactly where we left it. And we can consume it safely without fear that some bacteria have grown in it.

And this, I guarantee, will happen for any sugar solution in which the sugar percentage is greater than 70% (for example, honey).

This is because microorganisms are very sensitive to what we call "osmotic pressure," and for this reason when they are in contact with pure sugar or salt crystals, or being in a too concentrated solution of these, they simply die. Instantly.

The cell of a microorganism is held together by the cell membrane which is called a "semipermeable membrane." It is a barrier that, when surrounded by a liquid phase, lets the solvent in but not the solutes dissolved in it. In an aqueous solution, in practice, water would pass through this membrane but not the salt dissolved in it.

But what happens when a semipermeable membrane separates two solutions of different solute concentrations? In this case, the solvent (water in general) passes through it from the most diluted part to the most concentrated part (thanks to the strength of the osmotic pressure). The result is that the two concentrations will be equalized until they are identical.

If we are talking about a closed system like a cell it is obvious that just so much water can be contained in it. The result of a strong osmotic pressure may be that the cell will explode from inside or, vice versa, it will dry out into a ghost of itself in the desperate attempt to dilute the external concentration. That will happen to all living cells.

I know that this was a very long introduction but, it was necessary to attempt the risky speculative reasoning on what is happening in our society as regards the possibility of accessing information.

The more time passes, the more it seems evident to me that the enormous amount of knowledge that we have at our disposal has in no way increased the knowledge of people or their ability to draw conclusions. following these. Rather the opposite.

Apart from the tsunami of fake news and orchestrated disinformation, all of us today have access to an amount of data and information that was unthinkable until a few decades ago. We can access the NASA website to find out how the permafrost melting is going in real time, we can access the John Hopkins University to know every death and every contagion due to Covid on planet earth, we can see the measures taken by each country and understand who has guessed or not the management of the pandemic, we can access the sites of evolutionary biology and know the progress of the sixth mass extinction.

Yet, there is something that's going wrong. Functional illiteracy is skyrocketing. We do not know how to distinguish between an astronomy site and an astrology site. In front of a three-variable graph, we have the same attitude of the Kubrik's apes in front of the black monolith.

Many people find it increasingly difficult to complete the reading of an article that fits on a single A4 page. (By the way, are you still reading?)

And many of them, even if they read it,  remain convinced that the article proves them right even if it says the opposite of what they claimed.

Where's the problem? Where is the osmotic paradox that can justify this?

I am trying to find a correlation here (warning: speculation on reasoning already speculative per se ) by comparing the information paradox with the "sugar paradox." It seems to me that the more information comes into contact with our minds, the more Holbachian common sense comes out of our heads. It should be obvious that common sense is not learned in books. Once, we had enough of it to distinguish a charlatan from a scientist. Not anymore.

Now, let me be clear: I know very well that there has never been a golden age of information, and that there have always been profiteers of people's good faith (the “Ponzi scheme” was born in 1918, not the day before yesterday). Nevertheless, perhaps we have been suffering positivist optimism. We thought that more information was always a good thing, just like a bacterium may think that the more sugar around, the better. We really hoped that having the possibility of accessing so much information, people would have been if not better, at least more aware.

But not for me, as George Gershwin said (*).

Patience, it will be for the next species.

 

 

(*) In the original version, Chiometti referred to the Italian singer Brunori Sas

 

 

 

 

Sunday, May 2, 2021

Cataclysms and the Megamachine: Is History a Cycle or a Progression?

This image by the Tuscan painter Piero della Francesca exudes such power that it may truly blow your mind. Apart from the mastery of the composition, the perfection of the details, the fascination of the human figures, a canvas in the hands of a grand master is not just an image: it is a message. In this case, all the figures are static, there is no one moving. Yet, the painting carries the message of a tremendous movement forward in time. It shows a great change occurring: something enormous, deep, incredible: the triumph of life over death. And those who sleep through it are missing the change without even suspecting that it is happening. Just like us, sleepwalkers in a changing world, where gigantic forces are awakening right now. 


"Cataclysms" (*) is a recent book by Laurent Testot (Univ. Chicago Press, 2020) that goes well together with "The End of the Megamachine" (Zero Books, 2020) by Fabian Scheidler of which I wrote in a previous post

Both books see human history using the approach that I call "metabolic." It means to take the long view and see humankind in terms of a living entity. Call it a "machine" (as Scheidler does), call it "Monkey" (as Testot does), call it a "complex system" (as it is fashionable, nowadays), or maybe a holobiont (as I tend to do). It is the same: humankind is a creature that moves, grows, stumbles onward, destroys things, builds new things, keeps growing, and, eventually, collapses. 

Bot "Cataclysms" and the "Megamachine" catch this multiform aspect of the great beast and both emphasize its destructive aspects. Both understand that the thing is moving. More than that, its trajectory is not uniform, it goes in bumps. It is a continuous sequence of growth and collapse, the latter usually faster than the former (what I call "The Seneca Effect"). 

So, what's happening? Is history going in cycles, or is it progressing in some ways? It is a question that has been asked and answered in various ways over centuries of historiography, at least from when Edward Gibbon (in 1776) started wondering why the mighty Roman Empire had disappeared. 

For the Christian eschatological view, there was no doubt that the Empire had served its purpose and it had to disappear to leave space for a new world which, in turn, was bound to disappear in the Final Judgement. For the thinkers of the 19th century, instead, a different kind of teleology was at work. It was an interpretation of Darwin's ideas that saw evolution as a movement toward higher and higher levels of perfection, with the white European man as the pinnacle of the trend. 

Later, these ideas started to look naive, and a catastrophistic streak of thought started to grow. The collapse of the Western Civilization was clearly seen for the first time in a telescope aimed at the future in 1972, in the study sponsored by the Club of Rome titled "The Limits to Growth." The study had gone full cycle, returning to the old eschatological view of the end of the world. It was a cataclysm. Unavoidable, unless the megamachine could do something that the megamachine could not do: to stop growing.

But the universe is complex and the best-laid computer models of mice and men gang aft agley. Over the history we knew, no collapse has ever been the final one. After every collapse, there has been a rebound. So, history is both a cycle and a progression. There is something on the other side of the unavoidable collapse we are facing nowadays. All collapses bring change: it may well be their purpose in the universe. Just as the Romans couldn't imagine what would come after that their empire was gone, for us it is impossible to imagine what will come after us. We can only perceive that something enormous is stirring. Now we see it through the glass of our models, darkly: but then we will see it face to face.


(*) I had started this post with the idea of writing a review of Testot's book, but as I kept writing, the text grew by itself and it became something else. But, about "Cataclysms," by all means it is a great book -- not just dealing with catastrophic events but giving you an organic view of history, full of concepts and ideas that you cannot find anywhere else. By all means, do read it! It will change the way you see the world.