Responding to scientific uncertainty

Science provides an impressive path to certainty in some areas, particularly in physics. However, as scientists seek to describe increasingly complex entities, moving from chemistry to biology, and then to humans and societies, the level of uncertainty increases.

One observes a wide range of responses to scientific knowledge being uncertain. Here are a few.

Denial. Science is about facts and absolute truth. There really isn’t a problem. We should just trust the scientists.

Minimisation. There is some uncertainty, but it isn’t anything to be concerned about. Some scientists will also minimise any uncertainty about their own research. This may occur because of career ambition. Others will minimise public discussion of uncertainty to try and avoid promoting the science scepticism discussed below.

Optimistic perseverance. The uncertainty is openly acknowledged. Some of the uncertainty does not matter for what we need to know. Other uncertainties can be reduced by further scientific work, such by more precise measurements with new instruments or by developing more sophisticated theories.

Total scepticism. There is a suspicion about the validity of most scientific knowledge, particularly that which is perceived to have philosophical, religious, or political implications.

Suspicion about science

In spite of the success of science at describing the material world and leading to powerful and useful technologies, there is much public suspicion of science. On the one hand, this is understandable given that science has led to technologies with undesirable health, environmental and social consequences. Some scientists, governments and companies have lied about these consequences and hidden them from the public. Human subjects have been abused in medical experiments. Drugs that were claimed to be effective and safe turned out to be ineffective or have undesirable side effects. Science has been used for ideological purposes. Sometimes scientists have faked results to advance their own careers. However, these failures should not undermine our trust in reliable scientific knowledge. Distinctions should be made between the bodies of knowledge, the applications of that knowledge, and the actions of institutions. I now discuss several common claims in public discussion that are used to justify scepticism of scientific knowledge.

Science is always changing. 

One day, scientists tell you that chocolate is good for your health, and the next year they say it is bad for you. And that is just the start. Then there are eggs, wine, running marathons, and cheese. They just can’t make up their mind. So why should we trust them? At one time, they believed in phlogiston and the aether. Now they say they don’t exist. Aristotle was replaced by Newton, who was replaced by Einstein. So why believe in human-induced climate change, biological evolution, vaccines, the Big Bang theory, or Einstein’s theories?

It is true that scientific knowledge does develop and change over time. However, today we have incredibly detailed observations and theories in physics, astronomy, chemistry, biology, and geology. Any future changes will be relatively minor because they will have to be consistent with all the knowledge we have now. Furthermore, when theories change, such as when Einstein superseded Newton, they don’t show that the old theory was completely wrong, but rather that it applied in a limited domain. For example, Newton’s theories of motion and gravity are extremely reliable when it comes to objects that are much larger than atoms, less dense than a black hole, and are moving at speeds less than about 10,000 kilometres per second. This is why engineers spend years learning Newton’s theories, not Einstein’s. If you want to build a good bridge or a rocket, Newton is good enough. He is not wrong.

Update. (Jan. 19). I just discovered that the NY Times had a recent op-ed Science Keeps Changing. So Why Should We Trust It?

“Well, that’s just a theory.” 

In popular debate, such a refrain may be applied to the theory of biological evolution, the Big Bang theory in cosmology, or human-induced climate change. The claimant usually wants to dismiss a particular theory as just idle speculation. Here, the term “theory” is used in the same sense as everyday speculations, such as “I have a theory as to why the president resigned,” or “I have a theory about why my computer is running so slowly.” These are just stories that sound somewhat plausible. In contrast, scientific theories in physics, such as quantum theory and Einstein’s theories of relativity, have precisely defined mathematical formulations that have been checked for logical consistency, made specific predictions, and tested to great precision in experiments. They are not “just theories.” For example, for the Big Bang theory about the beginning of the universe and Darwin’s theory of biological evolution and diversity, there are many independent lines of evidence that are consistent with each theory.  

Scientists cannot be trusted. 

They are not committed to the truth, but rather to their own interests and agendas, related to their careers, politics, and religion. They close ranks and support the status quo of current scientific “dogma”, rather than being open to original thinkers who critique it and propose alternative theories. They don’t want to lose their well-paid jobs and lucrative grants. 

On the one hand, scientists can be conservative and resistant to new ideas. On the other hand, there are significant career incentives to overturn existing knowledge and have your radical new theory accepted. That is how some scientists become famous and win Nobel Prizes. The reasons it does not happen very often are not necessarily for social or ideological reasons. Many of the theories we have today can explain an awful lot. It requires a lot of evidence, carefully acquired and checked, to convince people that those theories need to be modified, let alone abandoned. This may take decades. But it does happen. An example is the Big Bang theory of the universe, whose acceptance was initially resisted because it went against the prevailing view that the universe did not have a beginning. In biology, the discovery in 1970 of the enzyme reverse transcriptase went against a popular version of the “Central dogma” of molecular biology that DNA was always converted to RNA and not the reverse. That discovery led to a Nobel Prize.

I don’t trust scientists. I will do my own research. There is lots of good material from unbiased sources on the internet.

The internet provides a range of information and perspectives on practically any issue imaginable, including science. The material is particularly vast and controversial on biological evolution, the beginning of the universe, fundamental physics, the age of the earth, climate change, and medicine. Since the covid-19 pandemic, scepticism of the effectiveness and safety of vaccines has increased. 

Ivermectin is a drug that was developed as a treatment for parasite worms. Its incredible success was recognised by the award of the 2015 Nobel Prize in Physiology or Medicine to William Campbell and Satoshi Omura, who discovered the drug. During the pandemic, high-profile politicians and social media influencers promoted ivermectin as a treatment for covid-19, even after systematic medical studies showed it was ineffective. Recently, it has gained a reputation as a “miracle” drug that can even cure cancer, but this is being suppressed by the medical establishment. All clinical trials have shown the drug is ineffective for human ailments, beyond deworming. Nevertheless, there are groups on social media with hundreds of thousands of members that discuss the conspiracy, how to get the drug, and the experiences of participants using it to treat a wide range of ailments. Danny Lemoi, a founder of one of the largest groups, died in 2023 after taking massive daily doses of the drug for several years to treat a heart condition. Afterwards, one member of the group wrote “No one can convince me that he died because of ivermectin. He ultimately died because of our failed western medicine which only cares about profits and not the cure.”

Fans of ivermectin claim that they are escaping the biases and vested interests of the medical establishment and Big Pharma as they pursue the truth. However, they are not escaping bias and vested interests. Successful social influencers build their reputations and million-dollar incomes from promoting scepticism. If there is no conspiracy, just scientific uncertainty and occasional incompetence and malpractice, their following collapses. Populist politicians build their careers on criticism of and stoking resentment towards elites, such as the medical establishment. The authority of the medical establishment is replaced with the authority of the popular opinion of a group of people whose views are shaped by social media algorithms, intuition, and anecdotal experience.

My purpose in giving the example of Ivermectin is not to start a detailed critique of science scepticism. Rather, it is to illustrate the role that the interplay of trust, authority, and tradition plays in how we determine what is true and what to act on. There are two competing traditions here: the populism of alternative medicine and the elitism of professional medicine. Each has its own sources of authority. In the end, it boils down to who we trust. We do not have the time, energy, resources or inclination to check the veracity of every single piece of information we have access to. We take shortcuts. This is what tradition does for us, for better and worse. Thus, we cannot escape tradition. We are all swimming in traditions, many of which are in conflict with one another. The question is whether we are aware of it and what we do with that awareness.

Self-organised criticality and emergence in economics

A nice preprint illustrates how emergence is central to some of the biggest questions in economics and finance. Emergent phenomena occur as many economic agents interact resulting in a system with properties that the individual agents do not have.

The Self-Organized Criticality Paradigm in Economics & Finance

Jean-Philippe Bouchaud

The paper illustrates several key characteristics of emergence (novel properties, universality, unpredictability, ...) and the value of toy models in elucidating it. Furthermore, it illustrates the elusive nature of the "holy grail" of controlling emergent properties. 

The basic idea of self-organised criticality

"The seminal idea of Per Bak is to think of model parameters themselves as dynamical variables, in such a way that the system spontaneously evolves towards the critical point, or at least visits its neighbourhood frequently enough"

A key property of systems exhibiting criticality is power laws in the probability distribution of a property. This means that there are "fat tails" in the probability distribution and extreme events are much more likely than in a system with a Gaussian probability distribution.

Big questions

The two questions below are similar in that they concern the puzzle of how markets produce fluctuations that are much larger than expected when one tries to explain their behaviour in terms of the choices of individual agents.

A big question in economics

"A longstanding puzzle in business cycle analysis is that large fluctuations in aggregate economic activity sometimes arise from what appear to be relatively small impulses. For example, large swings in investment spending and output have been attributed to changes in monetary policy that had very modest effects on long-term real interest rates."

This is the "small shocks, large business cycle puzzle", a term coined by Ben Bernanke, Mark Gertler and Simon Gilchrist in a 1996 paper. It begins with the paragraph above. [Bernanke shared the 2022 Nobel Prize in Economics for his work on business cycles].

A big question in finance

The excess volatility puzzle in financial markets was identified by Robert Shiller: The volatility "is at least five times larger than it "should" be in the absence of feedback". In the views of some, this puzzle highlights the failings of the efficient market hypothesis and the rationality of investors, two foundations of neoclassical economics. [Shiller shared the 2013 Nobel Prize in Economics for this work]. 

"Asset prices frequently undergo large jumps for no particular reason, when financial economics asserts that only unexpected news can move prices. Volatility is an intermittent, scale-invariant process that resembles the velocity field in turbulent flows..." (page 2)

Emergent properties

Close to a critical point, the system is characterised by fat-tailed fluctuations and long memory correlations.

Avalanches. They allow very small perturbations to generate large disruptions.

Dragon Kings

Minsky moment

The holy grail: control of emergent properties

It would be nice to understand superconductivity well enough  to design a room-temperature superconductor. But, this pales in significance compared to the "holy grail" of being about to manage economic markets to prevent bubbles, crashes, and recessions.

Bouchaud argues that  the quest for efficiency and the necessity of resilience may be mutually incompatible. This is because markets may tend towards self-organised criticality which is characterised by fragility and unpredictability (Black swans).

The paper has the following conclusion

"the main policy consequence of fragility in socio-economic systems is that any welfare function that system operators, policy makers of regulators seek to optimize should contain a measure of the robustness of the solution to small perturbations, or to the uncertainty about parameters value.

Adding such a resilience penalty will for sure increase costs and degrade strict economic performance, but will keep the solution at a safe distance away from the cliff edge. As argued by Taleb [159], and also using a different language in Ref. [160], good policies should ideally lead to “anti-fragile” systems, i.e., systems that spontaneously improve when buffeted by large shocks."

Toy models

Toy models are key to understanding emergent phenomena. They ignore almost all details to the point that critics claim that the models are oversimplified. The modest goal of their proponents is simply to identify what ingredients may be essential for a phenomenon to occur. Bouchaud reviews several such models. All provide significant insight.

A trivial example (Section 2.1)

He considers an Ornstein-Uhlenbeck process for a system relaxing to equilibrium. As the damping rate tends to zero [κ⋆ → 0], the relaxation time and the variance of fluctuations diverge at the same rate. In other words, "in the limit of marginal stability κ⋆ →0, the system both amplifies exogenous shocks [i.e., those originating outside the system] and becomes auto-correlated over very long time scales."

The critical branching transition (Section 2.2)

The model describes diverse systems: "sand pile avalanches, brain activity, epidemic propagation, default/bankruptcy waves, word of mouth,..."

The model involves the parameter R0 which became famous during the COVID-19 pandemic. R0 is the average number of uninfected people who become infected due to contact with an infected individual. For sand piles R0 is the average number of grains that start rolling in response to a single rolling grain.

when R0 = 1 the distribution of avalanche sizes is a scale-free, power-law distribution 1/S^3/2, with infinite mean.

"most avalanches are of small size, although some can be very large. In other words, the system looks stable, but occasionally goes haywire with no apparent cause."

A generalised Lotka-Volterra model (Sections 3.3 and 4.2) 

This provides an analogue between economic production networks and ecology. Last year I reviewed recent work on this model, concerning how to understand the interplay of evolution and ecology.

A key result is how in the large N limit (i.e., a large number of interacting species/agents) qualitatively different behaviour occurs. Ecosystems and economies can collapse. 

 "any small change in the fitness of one species can have dramatic consequences on the whole system – in the present case, mass extinctions...

"most complex optimisation systems are, in a sense, fragile, as the solution to the optimisation problem is highly sensitive to the precise value of the parameters of the specific instance one wants to solve, like the Aij entries in the Lotka-Volterra model. Small changes of these parameters can completely upend the structure of the optimal state, and trigger large-scale rearrangements,..." 

Balancing stick problem (Section 3.4)

 The better one is able to stabilize the system, the more difficult it becomes to predict its future evolution! 

Propagation of production delays along the supply chain (Section 4.1)

An agent-based firm network model (Section 4.3)

This has the phase diagram shown below. The horizontal axis is the strength of forces counteracting supply/demand and profit imbalances. The vertical axis is the perishability of goods.

There are four distinct phases.

Leftmost region (a, violet): the economy collapses; 

Middle region (b, blue): the economy reaches equilibrium relatively quickly;

Right region (c, yellow): the economy is in perpetual disequilibrium, with purely endogenous fluctuations. 

The green vertical sliver (d) corresponds to a deflationary equilibrium

Phase diagrams illustrate how quantitative changes can produce qualitative differences.

Universality

The toy models considered describe emergent phenomena in diverse systems, including in fields other than economics and finance. 

Here are a few other recent papers by Bouchaud that are relevant to this discussion.

Navigating through Economic Complexity: Phase Diagrams & Parameter Sloppiness

From statistical physics to social sciences: the pitfalls of multi-disciplinarity

This includes the opening address from a workshop on "More is Different" at the College de France in 2022.

Why is there so much symmetry in biological systems?

 One of the biggest questions in biology is, What is the relationship between genotypes and phenotypes? In different words, how does a specific gene (DNA sequence) encode information that allows a very specific biological structure with a unique function to emerge?

Like big questions in many fields, this is a question about emergence.

In biology, this mapping from genotype to phenotype occurs at many levels from protein structure to human personality. An example is how the RNA encodes the structure of a SARS-CoV2 virion.

A fascinating thing about biological structures is that many have a certain amount of symmetry. The human body has reflection symmetry and many virions have icosahedral symmetry. What is the origin of this tendency to symmetry? Could evolution produce it?

Scientists will sometimes make statements such as the following about evolution.

Symmetric structures preferentially arise not just due to natural selection but also because they require less specific information to encode and are therefore much more likely to appear as phenotypic variation through random mutations.

How do we know this is true? Can such a statement be falsified? Or at least, can we produce concrete models or biological systems that are consistent with this statement?

There is a fascinating paper in PNAS that addresses the questions above.

Symmetry and simplicity spontaneously emerge from the algorithmic nature of evolution 

Iain G. Johnston, Kamaludin Dingle, Sam F. Greenbury, Chico Q. Camargo, Jonathan P. K. Doye, Sebastian E. Ahnert, and Ard A. Louis 

Here are a few highlights from the article. First, how one gets specific about information content and algorithms.

Genetic mutations are random in the sense that they occur independently of the phenotypic variation they produce. This does not, however, mean that the probability P(p) that a Genotype-Phenotype [GP] map produces a phenotype p upon random sampling of genotypes will be anything like a uniformly random distribution. 

Instead, ... arguments based on the coding theorem of algorithmic information theory (AIT) (7) predict that the P(p) of many GP maps should be highly biased toward phenotypes with low Kolmogorov complexity K(p) (8). 

High symmetry can, in turn, be linked to low K(p) (6, 9–11). An intuitive explanation for this algorithmic bias toward symmetry proceeds in two steps: 

1) Symmetric phenotypes typically need less information to encode algorithmically, due to repetition of subunits. This higher compressibility reduces constraints on genotypes, implying that more genotypes will map to simpler, more symmetric phenotypes than to more complex asymmetric ones (2, 3). 

2) Upon random mutations these symmetric phenotypes are much more likely to arise as potential variation (12, 13), so that a strong bias toward symmetry may emerge even without natural selection for symmetry.

The authors consider several concrete models and biological systems that illustrate this bias toward symmetry. The first involves the structure of protein complexes, as given in the Protein Data Base (PDB).

A) Protein complexes self-assemble from individual units. 

(B) Frequency of 6-mer protein complex topologies found in the PDB versus the number of interface types, a measure of complexity 

𝐾˜
$$K˜(p). 

Symmetry groups are in standard Schoenflies notation: C6, D3, C3, C2, and C1. There is a strong preference for low-complexity/high-symmetry structures. 

(C) Histograms of scaled frequencies of symmetries for 6-mer topologies found in the PDB (dark red) versus the frequencies by symmetry of the morphospace of all possible 6-mers illustrate that symmetric structures are hugely overrepresented in the PDB database. 

Note the logarithmic scales for the probabilities (frequencies), meaning that the probabilities span four orders of magnitude. The authors claim that "many genotype–phenotype maps are exponentially biased toward phenotypes with low descriptional complexity. "

This intuition that simpler outputs are more likely to appear upon random inputs into a computer programming language can be precisely quantified in the field of AIT (7), where the Kolmogorov complexity K(p) of a string p is formally defined as a shortest program that generates p on a suitably chosen universal Turing machine (UTM). 

From AIT the authors produce a bound (equation 1, and below), that exhibits the exponential decay of probability with complexity, similar to that seen in their graphs, such as the one shown below, for a model gene regulatory network that is modeled by 60 ordinary differential equations (ODEs). The red dashed line is the bound below.

𝑃(𝑝)≤2−𝑎𝐾˜(𝑝)−𝑏,  [1

Scaled frequency vs. complexity 𝐾˜(𝑝)for the budding yeast ODE cell cycle model (30). Phenotypes are grouped by complexity of the time output of the key CLB2/SIC1 complex concentration. Higher frequency means a larger fraction of parameters generate this time curve. The red circle denotes the wild-type phenotype, which is one of the simplest and most likely phenotypes to appear. The dashed line shows a possible upper bound from Eq. 1. There is a clear bias toward low-complexity outputs.

One minor comment is that I was surprised that the authors did not reference the classic 1956 paper by Crick and Watson. They introduced the concept of "genetic economy". Prior to any knowledge of the actual structure of virions, they predicted that virions would have icosahedral symmetry because that reduced the cost of the genome coding for the structure of the virion.

Hence, it would be interesting to explore the relationship between the PNAS paper and this one.

Structural puzzles in virology solved with an overarching icosahedral design principle

Reidun Twarock & Antoni Luque

There is a nice New York Times article about the PNAS paper. I thank Sophie van Houtryve for bringing that to my attention leading me to the PNAS paper.

Covid-19 in a different world

 

Covid-19 has turned the world upside down. Different people and communities have had very different experiences. In my state of Queensland, it is almost a different world. To illustrate I share the data above, which prompted a three-day lockdown in Brisbane. For reference, Queensland has a population of 5.2 million.

A number of factors have contributed to the relatively positive situation. Australia is an island. Our borders were closed early. There was unity between state and federal governments. Generally, lockdowns have been pronounced promptly. Although Australians do not like authority and are a rebellious bunch, lockdowns and mask mandates have generally been observed. We are not immune from conspiracy theories and vaccine hesitancy. But, overall we have not been plagued by the same level of "politicisation" that has hobbled other countries. 

In some ways, I feel I am living in a different world.

Yet, some of this good fortune should not lead to pride and complacency. Things may still come unstuck. The Delta variant is spreading in Sydney and half the country is in lockdown. The government vaccine rollout has been dubbed a "stroll out".  Only 12 percent of the population has been fully vaccinated. Australia is currently ranked last among the OECD countries. 

The associated "blame game" has even been featured in The New York Times.

The science and politics of the origins of covid-19

I want to begin by stating some hypotheses. Some may be obvious. Others may be contentious. I will number them so that people can easily make comments about specific ones. The underlying issues are illustrated in recent debates about the possible origins of covid-19.

1. Systematic critical thinking is essential to scientific progress and public policy. Healthy doses of skepticism can be valuable.

2. Science progresses well by making multiple hypotheses and examining carefully what evidence is consistent with each of the hypotheses. This is something that Murray Gell-Mann wished someone had told him when he was twenty years old.

3. Transparency is essential to science. People need to share data, including primary data. The more that such data is publicly available the better. This is what open science advocates. 

4. Science is built on ethical conduct, both implicit and explicit. It is important that declarations of conflicts of interest are not just a box-ticking exercise.

5. Scientists cannot have allegiance to some greater authority than truth and integrity. Problematic allegiances include to a company, a family, an institution, a political party, or to a nation. An example is the case of the recent change to the charter of Fudan University, indicative of the stranglehold that the Chinese Communist Party has over Chinese universities.

6. Given these issues about integrity and conflicts of interest luxury journals are problematic because there is a conflict of interest between the commercial success of the publisher in the short term (achieved by promoting hype, i.e. newsworthy sexy scientific breakthroughs, even if they are wrong) and the boring work of doing careful painstaking science.

7. One approach to solving some of these problems is self-regulation of scientific communities. However, when sub-communities (e.g. virologists, string theorists) self-regulate this may be impeded by conflicts of interest.

8. Given the issues above, science journalists need to be more critical and skeptical. Too often they seem in awe of scientists and want to promote hype as it sells. Journalists need to ask more hard questions about conflicts of interest, weak reasoning, claimed "breakthroughs", hype, proposed great technological applications, and the "science as saviour" narrative.

9. There is a fear among scientists about publically speaking about scientific uncertainty and ambiguity. This fear is understandably driven by the experience of "skeptics" latching onto uncertain statements to promote climate change denialism, young-earth creationism, and anti-vaccines. Thus, a great challenge in public engagement is to educate about the role of uncertainty in science.

10. Science always occurs in a political context whether it is in Australia, Romania, or China. The context will always have some influence, but it should not be determinative.

11. The greater the stakes (whether potential Nobel Prizes, company profits, government scandal, a disaster) in play, the greater the likelihood will be for mistakes, corruption, deception, and cover-up. Consequently, the level of scientific diligence and regulation needs to be proportionate to the possible benefits and risks. Extraordinary claims require extraordinary evidence.

12. Beware of the argument from authority. A hypothesis should be accepted or rejected based on the quality of the reasoning and evidence provided, not on the scientific prestige (or lack thereof) of the proponent.

All of the claims above I see played out recently in debates about the origins of covid-19. Two distinct hypotheses are dissected in a helpful and long article recently published in the Bulletin of Atomic Scientists.

The origin of COVID: Did people or nature open Pandora’s box at Wuhan?  Nicholas Wade 

Hypothesis 1. The virus spread from a wet market in Wuhan. The virus was zoonotic, i.e. as a result of evolution it crossed the species barrier from bats to humans.

Hypothesis 2. The virus spread from the Wuhan Institute of Virology where a research group was investigating bat viruses and doing "gain of function" research to see how the bat viruses might be modified genetically into a form that could infect humans. 

The article is worth reading because it carefully lays out the science while also raised many of the issues I mention above. A few things that I learned follow.

There is significant evidence that the MERS, SARS1, Ebola viruses are zoonotic. The evidence consists of finding intermediate genetic forms in intermediate species. Often this evidence was found within months of the disease outbreak. In contrast, after 18 months there is still no evidence of intermediate forms for SARS2.

The "gain of function" research in Wuhan was being funded by the USA National Institutes of Health, via a grant to the EcoHealth Alliance of New York, led by Peter Daszak. Wade writes

"We stand together to strongly condemn conspiracy theories suggesting that COVID-19 does not have a natural origin,” a group of virologists and others wrote in the Lancet on February 19, 2020, when it was really far too soon for anyone to be sure what had happened. Scientists “overwhelmingly conclude that this coronavirus originated in wildlife,” they said, with a stirring rallying call for readers to stand with Chinese colleagues on the frontline of fighting the disease.

Contrary to the letter writers’ assertion, the idea that the virus might have escaped from a lab invoked accident, not conspiracy. It surely needed to be explored, not rejected out of hand. A defining mark of good scientists is that they go to great pains to distinguish between what they know and what they don’t know. 

It later turned out that the Lancet letter had been organized and drafted by Peter Daszak, ... This acute conflict of interest was not declared to the Lancet’s readers. To the contrary, the letter concluded, “We declare no competing interests.”

Wade points out that there is no direct evidence for either of the two hypotheses (which he calls theories).

He also talks quite a bit about "who is to blame" and claims that we need to know the answer as to which hypothesis is correct in order to know how to prevent the next pandemic. However, I disagree. Based on the evidence we already have we can conclude the following.

A. New deadly viruses can be zoonotic. The best way to reduce their likelihood is to close wet markets and reduce environmental destruction.

B. Even if SARS2 did not spread from the "gain of function" research in Wuhan it is completely plausible that it could have. Thus, given such risks that research should be stopped until a case is made that the possible benefits outweigh the risks and that it is done with much greater transparency and regulation than currently.

For balance I include an extract from Wikipedia

In May 2021, Wade published an article which advanced the claim that COVID-19 likely originated from a leak at the Wuhan Institute of Virology.^[12][13] Wade's article generated significant controversy,^[14] and has become one of the most-cited pieces in support of the lab leak hypothesis.^[15] This claim is at odds with the prevailing view among scientists that the virus most likely has a zoonotic origin.^{[16][17][18][19]} Some experts have supported taking the lab leak possibility seriously, while the majority consider it very unlikely, calling it "speculative and unsupported".^[20][21] Others noted the explosive and implausible nature of Wade's allegations about virologists conspiring to avoid blame for causing the pandemic,^[22] with Science-Based Medicine among those calling Wade's argument a conspiracy theory.^[23]

Another article worth reading (recommended by a commenter on this blog) is

Beijing’s useful idiots: Science journals have encouraged and enforced a false Covid narrative by Ian Birrell.

Increased competition for admission to USA PhD programs?

 We live in different times. There is some anecdotal evidence that this year admissions to leading graduate schools in the USA have become a lot more competitive, particularly for international applicants. Doug Natelson has discussed the issue, highlighting that it is important for unsuccessful applicants to know that these are exceptional times and their lack of success does not reflect on their ability and potential, but rather on structural issues.

I have a few questions for readers.

A. Is it your experience (whether as an applicant, recommender, or decider) that it is more competitive this year? Have you seen any articles about this?

B. If so, which of the following factors are particularly causing this crunch? (Doug mentions some of these factors.)

1. Fewer current Ph.D. students are graduating because of delays or lack of job opportunities due to the pandemic. This leaves less money for new students.

2. Universities are nervous about making offers to international students because of pandemic-related travel restrictions and uncertainty. There is a preference for domestic students.

3. Some universities are undergoing budget cuts or are very uncertain about their financial stability. This has flowed on to reduced admissions.

4. There are more applicants because of limited alternative job opportunities.

5. Other factors?

It will help all concerned if we can have a more accurate picture of what is going on. I raise the issue because I was surprised and disappointed that I encouraged a student to apply and wrote a glowing reference (neither of which I do very often) but he did not succeed. 

Please do share what you do know.

Classic fails about predicting the scientific future

There are many statements that are attributed to famous scientists that turn out to be widely wrong. A decade ago I wrote about one from Brian Pippard predicting the end of condensed matter physics in the 1960s.

However, just like Einstein quotes, it turns out that many of these statements in the popular folklore are often wrongly attributed. For example, according to the Wikipedia entry on Lord Kelvin,

The statement "There is nothing new to be discovered in physics now. All that remains is more and more precise measurement" has been widely misattributed to Kelvin since the 1980s, either without citation or stating that it was made in an address to the British Association for the Advancement of Science (1900).^[86] There is no evidence that Kelvin said this,^[87][88] and the quote is instead a paraphrase of Albert A. Michelson, who in 1894 stated: "… it seems probable that most of the grand underlying principles have been firmly established … An eminent physicist remarked that the future truths of physical science are to be looked for in the sixth place of decimals."^[88]

I love Bill Bryson's book A Brief History of Nearly Everything.  I recently read in it that the Surgeon General of the United States of America, William Stewart, said in 1967:

“The time has come to close the book on infectious diseases. We have basically wiped out infection in the United States.”

Wow, that sure was wrong. There are now 9 million covid-19 cases and more than 200,000 deaths in the USA. I was going to write a blog post about this. But, then I discovered these two nice articles.

In 2015 an article in the New Yorker
One of Science’s Most Famous Quotes Is False By Michael Specter

A 2013 article in the journal Infectious Diseases of Poverty
On the exoneration of Dr. William H. Stewart: debunking an urban legend 
Brad Spellberg and Bonnie Taylor-Blake

Basically, Stewart never said this, nor held this view. In contrast, many of his contemporaries did.

And then there is all the hype about current materials research leading to revolutions in computing, medicine, transportation, commerce, ...