The Collapse of Scientism and the Rebirth of Science

 

The oldest image (1228-1229) we have of Francis of Assisi (1182 – 1226). Not a portrait, but probably not far from the real aspect of Francis. He engaged in a bold attempt to reform the corrupt Catholic Church in Europe. He failed, but he left a trace in history from which we can still learn much. In our times, the corrupt organization that we need to reform is Science, turned now into a state ideology to oppress people and destroy nature. Maybe we need a new St. Francis to reform it, or maybe it needs to be dismantled and rebuilt from scratch in a new structure. Here, I discuss this story and I also reproduce a post by Luisella Chiavenuto (a little long, but worth reading) who has perfectly understood the situation and proposes that what we call "science of complexity" is a completely new kind of science, different from the old Galilean version.

By Ugo Bardi

 

With the turn of the 2nd millennium in Europe, the Catholic Church had gone through the involution that's typical of all large organizations. It had become huge, bureaucratic, corrupt, and inefficient. A once idealistic and pure organization had been defeated by the arch-corrupter of everything human: money. 

Earlier on, Europe had emerged out of the collapse of the Roman Empire as a lean, non-monetized society that had no impulse to grow and conquer outside lands. But the re-monetization of Europe started when rich silver mines were found in Eastern Europe with the turn of the millennium.

At that time, Europe was bubbling with a new wealth, a new assertiveness, a new way of seeing the world. Once you have money, you can have an army. Once you have an army, you can search for enemies. And once you have enemies, you can attack them and make more money. With the first crusade, started in 1096, Europe started its transformation from a sleepy peninsula of Eurasia to a military and financial machine that would engage in the conquest of the world. It succeeded at that over half a millennium of conquests. 

Against all this, a man surged. His name was Francis of Assisi (1182 – 1226) and he perfectly understood the root cause of the corruption: money. In Francis's view, money was the "Devil's Dung" and neither himself nor his followers would touch it.

It was a bold plan to reform the Church from the inside. The impact of Francis was enormous on his contemporaries, so much that we still remember him and love him. But, ultimately, he and his followers failed. Money is a truly powerful demon. 

In 1517, nearly three centuries after Francis, things came to a head when Pope Leo X authorized the sale of indulgences in Germany. Selling salvation for money was too much, and it was then that Martin Luther nailed the text of his 95 theses on the door of a church in Wittemberg. 

It was the start of the decline of the "Catholic" ("universal") Church that ceased to be universal at that time. It survived for a few centuries as a regional Church, until it was replaced with scientism as the founding myth of the Western world. The decline seems to be complete nowadays with empty churches and bewildered flocks, terrorized by TV scientists predicting doom for them. It is the triumph of scientism.

But things never stand still, cycles are always ongoing, and the triumph of scientism already shows signs of decline. Science is corrupted from inside by the same demon that corrupted the Church in the late Middle Ages: money. 

It is a tall order that of reforming such a huge and entrenched organization as science is nowadays, but for everything there comes the day of reckoning; redde rationem villicationis tuae: iam enim non poteris villicare. (Luke, 16:2)

So, we need to reform science to turn it from a support for the oppression of humankind to what it was at the beginning: "natural philosophy," which means "love for the knowledge of the natural world," not "knowledge for destroying the natural world" as it is understood in the "scientism" paradigm. In short, we need a human science, otherwise it is not science.

In the following, a post by Luisella Chiavenuto who perfectly understands these points and describes them in detail. It is not impossible to reform science and see its rebirth in a new form. The key point is that the science of complexity is a new science, very different from the old Galilean science. We need to recognize this difference and move onward to tackle a new world using new instruments.

 ____________________________________________________________________

The Paradigm of Scientism and Complexity

By Luisella Chiavenuto -- Translated and condensed from "Umanesimo e Scienza"

 

We live in a period of rapid change and redefinition of any kind of identity, including scientific identity. It is no longer just a matter of a normal scientific debate (which has become more and more impossible) but of a real internal split in Science.

The scientism paradigm was based on the research of domination over nature - and more and more on its reprogramming, according to the interests of humanity - for a certain period has improved the conditions of life.

Then the trend reversal started. And now the main planetary problems are caused and aggravated by the current techno-scientific model that reached the height of its power and at the same time the peak of its unsustainability, in every sector.

A model in which almost all of what we call "Science" is merged with technology and economy - so as to be inseparable in every aspect. And the large transnational corporations are dominated by the transversal power of the IT corporation.

It is a model in which the war against "the human" - and within the human psyche - tends to replace the physical war. The planetary battlefield is now our feelings and our cognitive - and epistemological - patterns.

However, there is also a new, emerging model based on a radically different scientific and cultural paradigm that proposes a science capable of self-criticism, and a technology that is more humble and friendly to the Nature that sustains us - and to our own human nature from which we are constituted.


The clash between different scientific models

The two models, the dominant and the emerging one, thus give rise to two different scientific methods - based in turn on two different worldviews and visions of the knowledge process.

In the case of the dominant paradigm, scientism, knowledge derives from an exclusive use of scientific rationality, which considers truth preeminently, if not exclusively, only that which is "measurable," and to be pursued only what is conveyed by increasingly powerful technologies, with immediate and sectorial effectiveness and whose negative effects at a distance of time and space are not - in principle - taken into account,

The philosophy on which this type of science is based is declaredly neo-Scientism, therefore for certain important aspects, it is in relation of continuity with the Cartesian paradigm. The interpretative metaphor adopted is that of the world seen as a network of computers interconnected and guided by the computational cognitive model - within a technocratic and reductionist conception of the concept of "system".

This model of science is proposed as an exclusive model, based on the principle of established authority, i.e. the major international and local scientific institutions - within which, however, there are also different positions, although marginal ones.

In the case of the emerging paradigm, on the other hand, scientific rationality becomes one of the possible cognitive dimensions - assumed, therefore, not to impose themselves, but to integrate harmoniously with the other cognitive faculties from which we are constituted: the historical and social dimension (historical experience, philosophies, social disciplines ...) and the symbolic dimension (art, music, literature, spirituality ...)

The philosophy at the base of this emerging paradigm can be defined as a vision of reality based on the concept of complexity of unlimitedly stratified interconnected systems. The interpretative metaphor is that of the world seen as a living organism, in which each element is constitutively connected and interdependent on the others.

It is a vision that leads to the concept of symbiont, which means forms of life not only physically associated, but that evolve together in a co-evolution. The concept of phylogenetic symbiont in turn leads to the concept of holistic symbiont - with infinite levels of stratification, in turn, included in a universal Totality.

This model of science is proposed as an inclusive model, based on the principle of freedom of thought - It also includes the Dominant Paradigm, but in a relativized form, that is subjected to radical critical revision and placed within a wider conceptual framework.


The dynamics of the paradigms

In synthesis, we can say that we are seeing a clash between the scientism paradigm and the paradigm of complexity.  Of course, these are abstract concepts, useful for orientation. Moreover, they must be considered as "paradigms" by their very nature composed of different elements: only the combination of these elements - and of their historical roots - can provide a valid criterion of judgment.
 
In particular, the concept of "System" is very important for both paradigms, but it is conceived and developed in a very different way. This is due mainly because the two paradigms have origin from cognitive models so different that they can be defined as substantially opposite to each other. But the boundaries between these paradigms are never traced in a clear-cut stable way.

Rather, they are osmotic, contradictory, and fragmented processes that unfold over time, giving rise to a "dynamic of paradigms "  taking place simultaneously on a historical scale and on an individual scale, that is, in the realm of the personal psyche. Finally, and increasingly frequently, the keywords of the scientific and political debate undergo a process of mimicry, through which their meaning is turned upside down.

Sometimes this reversal occurs through the deliberate use of advertising techniques - sometimes it is the result of a confusion of thought. The line between the two is blurred, and often very blurred.


The Current Crisis

In this period, we have witnessed an epochal nemesis of the enlightenment reason. With a unilateral and unrestrained development, technoscience has definitively reversed itself into its opposite: an obfuscation and a radical repudiation of rationality itself. Having severed any link with the complexity of life, this approach becomes structurally obtuse.

A good fraction of the political and economical sectors make use of this obfuscation of reason by using the crisis and the implosion of scientific thought for power purposes - or sometimes of declared impotence. In turn, they feed a market of technological products in which the military and civilian sectors are structurally intertwined, as it was from the beginning. Every macro-economic sector is by now structurally interwoven - and dominated - by the companies that manage the backbone of IT tools.  (The new era of epistemic dominance).

The information corporation, being a network of power transversal to all the great corporations (energy, financial, material, cognitive, and media) - unifies them and allows similar cultural and political lines shared on a planetary scale. These convergent choices occur both through deliberate and centralized public decisions - and through processes of involuntary "systemic" automatism, parceled out and not made explicit.

State Science, therefore, proposes solutions that are dead ends. That is, it imposes a framing in hyper-sectorial complications, deadly for the social, economic, and ecological fabric - and for the human psyche. This framing is deadly for the very concept of humankind and civilization, because
- through the practice of misdirection/distancing/masking - is eroded at the root of the bond of mutual trust between people, which is the foundation of the human interaction.

Moreover, the pact of trust between citizens and institutions is also eroded, because with the health passport, and the like, it is established that basic human rights are granted only to those who accept the decisions of the State, which can suspend human rights on the basis of health conditions (all sick until proven otherwise) and behavior in the most personal choices (denying the freedom of care - and so the way is paved for any subsequent abuse).

For over a year now, the State has been heavily entering the private and emotional life, the choices of the most intimate sphere and the very body of all people, without limits and without counter-balances. Hence, also, the need to resort to a surrogate of religious faith - in science and in vaccine miracles - to be able to support what is not sustainable with a reasonable use of reason.

Moreover, all the premises (scientific, legal, and customary) remain in place for the same model of management of the epidemic to be proposed again at the seasonal resumption of variants, or other threats. Finally, this approach seems destined to become the basic political-scientific model, usable in its basic lines to face all emergencies.

So not only the upcoming health threats but also the climate emergency, much more impressive and complex, - as well as the crises of energy and food resources, also related to overpopulation - and caused by an economic model centered on the destruction of essential resources: land, air, water, and natural and social ecosystems. A model that imposes the massive increase of every technology in every field. 

The suspension of human and constitutional rights, increasing computer control for political purposes (Chinese style social control), and the dehumanization of life, in every field. That is the New Normal, presented, and believed by many, as an inevitable choice. But, in addition to confusion, in this madness, there is also a method, whose paradigmatic constants can be recognized.

Recognizing this method can help us understand (in part) why the vast majority of the scientific, academic, and intellectual world has adhered to an irrational and failed description and management of the pandemic.

The Knowledge Process

In this context, the "Humanism and Science" website - and the related Association - propose to use the strong and difficult energy released by the crises, directing it towards a new culture of complexity,
through a dialogue - self-critical and integrative - between science and humanism.

In a similar way, an integration between the different dimensions and cognitive languages from which we are constituted as individuals has also sought: the rational dimension, the historical-social dimension, and the symbolic dimension.  
   
This progressive integration can lead to qualitative leaps, to changes of great intensity in personal and collective life. (Of course, the interaction described here is only a "method", and as such can have different outcomes, depending on the purposes and the general vision of those who practice it).

In this site, we deal with ideas, art, and music: not to create entertainment but on the contrary to look for creative interaction, a mutual influence that brings depth, beauty, and harmony in the process of research and knowledge - both personal and collective.

The basic orientation can be condensed into Dostoyevsky's phrase: "Beauty will save the world". Remembering also the meaning of the word "beauty" in ancient Greek: kalòs, which means at the same time "Beautiful, True, Good".

It is an orientation that, however, does not forget the ambivalence of Nature, with its dual aspect of "mother and stepmother". Awareness of the seriousness of systemic breakdowns - both ongoing and future - can, however, join with a vision of life that is not exhausted within what we commonly define as "physical reality."

In turn, this shift may imply a Metanoia, or even a "repentance," not in the superficially moralistic sense, but in the etymological meaning of the terms: a profound change of thought, of concrete life, of vision of the world and of oneself - a change provoked by restlessness, by pain, and by a crisis with no apparent way out - but also provoked and sustained by an intuition of happiness and intensity of life,  presented as real and endowed with intrinsic truth.

In order to identify this kind of truth, one can resort to a concept that is often misrepresented in its original meaning, and which can be summarized by the word "Transcendence" - in a meaning that does not devalue immanence, but rather includes it in a more infinite and indefinable horizon.

The Evaluation of Time

With this horizon open to the dynamics of different cultural paradigms, one can understand the fascinating complexity and the (critical and self-critical) encounter between humanistic culture and scientific culture. And the necessity of such an encounter to illuminate with a new light, and at the same time an ancient one, the ethical choices to which we are called. Dramatic choices that seem to lack, literally, a ground on which to base themselves.

In fact, the scientism ideology is based on the devaluation of the past - in the name of the magnificent fates and progressions of Technoscience, capable of solving all problems by increasing its power. In this way, it implements a split from the past, closing itself to the possibility of learning from historical experience. That is, it precludes the possibility of seriously understanding our cultural roots - through a revision that is critical, but - even before being critical - capable of studying and grasping their deepest meaning.

The result of this split (which is also a split from our deepest psyche) is the present poverty in cultural and human depth, radically alien to any form of charm, beauty and depth. The foreshadowing of the future is thus delegated, mainly, to the literary genre of science fiction - in which the technical power of reshaping nature usually appears as a nightmare, or at least an obligatory solution, and in turn the generator of new and greater nightmares.

However, in the immediate future, technoscience offers ephemeral solutions of "safety," and its social consensus on that.
 

The Ideological side

Scientism as an ideology is proposed as a faith that often borders on the religious exaltation of man's power over life, a substitute that fills the void left by traditional religions - a form of "fundamentalist" exaltation - blind to any warning of the Nemesis in action.

But the collapse of intellectual and ethical credibility of the Scientism ideology causes a consequent strengthening of its authoritarianism, even if it coexists with a strand of theories and practices, including neuro-technologies, self-declared as having a "democratic" purpose).   

In the Emerging Paradigm, instead, the past-future opposition is overcome, and the intimate link between the past and the future is grasped, both on the level of ideas, and on the wider, life-giving human level. This link can also be defined as a sense of "nostalgia".  Nostalgia for the past combined with nostalgia for the future, that is, desire and hope.

These are much more than just feelings: they are powerful archetypes. endowed with great creative energy - indispensable for a life that is not mere survival of the body, and for this reason constantly on the verge of suicide for lack of horizons and meaning.

"... I was a fireplace, inhabited by flame.
Invaded by a subdued and burning joy.
I was not just a stone fireplace,
but a messenger
Of lost Confidence.

A messenger of the indefinite Hope
That abandoned in the smoke, rises ...
Wounded and powerful in its pain,
rises to cover the roofs
and the distant rocks... "

    
Luisella Chiavenuto June 2021

                        


Bibliography
- Plato "Dialogues"
- F. Capra "The Turning Point" - T. Kuhn "The Structure of Scientific Revolutions".
- U. Bardi "Seneca Effect"  "Who is the Emperor of the World?
The new era of the epistemic dominion"

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 time. Finally, 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

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