The science of humility

Research over the past few decades, particularly studies in business and management, has shown, that humility works. It is a powerful force for good and increases the chance of success in a range of human endeavors. Surprisingly to some, the meek do inherit the earth! John Dickson summarizes some of this social science research in his book, Humilitas: A Lost Key to Life, Love, and Leadership. Dickson defines humility as follows: “Humility is the noble choice to forgo your status, deploy your resources or use your influence for the good of others before yourself. More simply, you could say the humble person is marked by a willingness to hold power in service of others. Humility presupposes your dignity…Humility is willing. It is a choice. Otherwise, it is humiliation…Humility is social. It is not a private act of self-depreciation…Humility is more about how I treat others than how I think about myself.” 

Jim Collins, was a Professor at Stanford University when he led a large team who studied the characteristics of eleven highly successful companies with the findings reported in his book, Good to Great: Why Some Companies Make the Leap... and Others Don't. They discovered that each of these companies was led by distinct individuals, who he characterized as a Level 5 Executive who “builds enduring greatness through a paradoxical blend of personal humility and professional will”. 

In a Harvard Business Review article, Changing the Mind of the Corporation, Roger Martin, a Professor of Management at the University of Toronto argues that the decline of a successful business is characterized by “the deterioration of necessary feedback” and then by a “proliferation of organization defensive routines”. Managers “become impervious to learning of any kind.” The antidote is listening and learning because “people are naturally scientific: they make hypotheses, collect information, criticize each other’s demonstrated conclusions. The challenge is to channel this energy into an open discourse on the fate of the company, not into an underground discourse on the prejudices of the CEO.”

The value of making decisions based on a range of opinions is described by a concept in statistics and the social sciences, known as “the wisdom of the crowd.’’ It was first proposed in 1906 by the statistician Francis Galton after he observed a contest at a county fair to guess the weight of a slaughtered ox. The median guess from eight hundred participants was accurate within one percent of the true weight. Web resources such as Wikipedia, Quora, and Stack Exchange rely on collective human knowledge. The errors associated with individual human judgments are averaged out, provided the participants are truly diverse. There are well defined mathematical theorems to quantify and justify this perspective. Scott E. Page uses these to partially justify the many-models approach that he advocates in the social sciences and mentioned in a previous post. Furthermore, Page has used a range of mathematical models and empirical studies to argue the merits of decision making in teams being based on the consideration of diverse opinions.

In summary, it appears that research in the social sciences has shown that humility is a key to success in life. The meek do inherit the earth.

Minimal effective Hamiltonian for spin-crossover materials

My colleagues and I just put a preprint on the arXiv. I am particularly proud of it.  As always, comments would be appreciated.

Equivalence of elastic and Ising models for spin-crossover materials

Gian Ruzzi, Jace Cruddas, Ross H. McKenzie, Ben J. Powell 

Spin crossover (SCO) materials are reversible molecular switches; and occur in a wide range of near octahedral transition metal complexes and frameworks with d4−d7 electron configurations. SCO systems present collective spin-state phase transitions that show hysteresis, multistep transitions, gradual transitions, and anti-ferroelastic phases. Ising models have often been employed to model these behaviors, as they are far easier to solve than more realistic elastic models. However, previously Ising models have required phenomenological parameters that do not have a clear physical origin. 

We present an exact mapping from an elastic model of balls and springs to the Ising model. The resulting Ising coupling constants arise only from the elastic interactions, and are independent of the lattice dynamics, i.e., there are no isotope effects. The elastic interactions, and hence the Ising coupling constants can be determined from the measurements of the bulk and shear moduli. The Ising coupling constants can be frustrated, their signs can be negative or positive, and their magnitude agrees well with previous estimates from fits of experimental spin-transition curves. The Ising coupling constants follow a power law for large separations between metal centers, in particular an inverse square law for the square lattice. For the square lattice with nearest neighbor elastic interactions this model predicts a diverse range of spin-state orders including multistep transitions.

 

Quantum matters for the public

I have now finished my first draft of  Chapter 7 of Condensed Matter Physics: A Very Short Introduction. The main purpose of the chapter is to introduce quantum states of matter. It is arguably the most challenging of the chapters to write and to understand. But, it is potentially the most fascinating.

I welcome comments and suggestions. However, bear in mind that my target audience is not the typical reader of this blog, but rather your non-physicist friends and family.

I think it still needs a lot of work, particularly to be less technical. For example, I should probably drop Aharonov Bohm ...

The goal is for the chapter to be interesting, accessible, and bring out the excitement and importance of condensed matter physics.

Iconic images of science

Over history, a number of images have become iconic representations of science in the public mind. Here are some that I am particularly aware of.

1. solar system

This represents the predictability of Newtonian mechanics and the fact that we don't live in a geocentric universe.

2. microscope

This represents the whole new world that opened up. It is interesting that I don't think the telescope gets quite as much attention.

3.  atom

It is interesting that this classical picture has persisted in spite of quantum theory. Furthermore, this atom has often actually been equated more with nuclear physics, rather than actual atomic physics.

4. double helix of DNA

This has come to represent not just molecular biology but genetic engineering in particular.

What do you think? Are these the most significant and popular images of science?

Now I come to my main point. 

Is the image below of a SARS-CoV2 virion going to become an iconic scientific image?

Science begins and ends with humility

Science requires humility. Any scientific investigation starts with acknowledging ignorance. Scientific progress requires a willingness to admit mistakes and accept evidence, even when it goes against cherished and esteemed beliefs, theories, and colleagues.

As a scientist, I am fascinated by the science of covid-19, from the genetic code of the virus to the mathematical modeling of epidemics. I find it amazing how much we do know. It is also amazing how much we do not know.

The SARS-CoV2 virus is one of many coronaviruses; a name derived from the crown-like appearance of a virus particle in an electron microscope. The points on the crown are called spike proteins; they are attached to a spherical surface composed of other proteins. The diameter of the virus particle is about one-tenth of a micron. If you lined up ten thousand particles next to each other in a straight line they would be the size of a pinhead. The complete details of the atomic composition and geometrical arrangement of these spike proteins have been determined. This sphere (virus capsid) encapsulates the genetic information that is encoded in a single strand of an RNA molecule. The spike proteins allow a virus particle to attach itself to and enter a human respiratory cell. Inside the cell the virus particle bursts releasing the RNA molecule that then moves to the ribosome of the cell which then makes many copies of the RNA molecule. The information in each of these molecules is then used to manufacture the proteins that compose a virus particle. The copies of the RNA and proteins then reassemble into thousands more virus particles that then leave the host cell and move onto more cells.

It is amazing we know so much. Furthermore, we know the exact details of this genetic information. The RNA molecule in the SARS-CoV2 virus particle consists of a unique sequence of 33,000 letters (G, U, T, or C). In the laboratory, scientists can make a molecule with exactly this sequence and use these molecules to make artificial copies of the virus particles. We know so much. It is amazing. Aren’t we clever!

The flu epidemic of 1918-1920 killed more people than World War I. Back then we did not even know that RNA existed, the structure of any protein molecule, what the genetic code was, the mechanism of infection, how to mathematically model the spread of epidemics or the relative merits of different strategies for managing epidemics. We now know so much more. Today, this knowledge is saving thousands of lives.

Yet, we know so little. Although we know all the amazing details above, we cannot predict the structure of the virus particles. Furthermore, we don’t know the design of effective and safe drugs and vaccines to treat covid-19. A vaccine has never been developed for a coronavirus. This does not mean that it is not possible or even that it won’t happen in the next year. We also don’t really know how to balance the medical benefits and the economic and social costs of lockdowns.

Modeling, understanding, and describing social, political, and economic phenomena is even more difficult than physical, chemical, and biological phenomena. Scott E. Page is a Professor of Political Science, Complex Systems, and Economics at the University of Michigan. He teaches an online course, ``Model thinking’’ that has been taken by more than a million people. In his recent book, The Model Thinker, Page makes the case for using multiple models to describe human behavior.

We conclude with a plea for humility and empathy. In constructing models of people, a modeler must be humble. Given the challenges of diversity, social influence, cognitive errors, purpose, and adaptation, our models will inevitably be wrong, which is why we take a many-model approach.

Parenthetically, I note that this many-model approach is similar to the method of multiple hypotheses advocated by John Platt and that I have blogged about previously.

In 1932, Albert Einstein responded to a letter from Queen Elizabeth of Belgium, who complimented him on his lucid explanation to her of various topics in theoretical physics. Einstein wrote: 

It gave me great pleasure to tell you about the mysteries with which physics confronts us. As a human being, one has been endowed with just enough intelligence to be able to see clearly how utterly inadequate that intelligence is when confronted with what exists. If such humility could be conveyed to everybody, the world of human activities would be more appealing.

Quoted in Helen Dukas and Banesh Hoffman, Albert Einstein, The Human Side: Glimpses from His Archives. Princeton, NJ: Princeton University Press, 1979, 48.

The text above is an extract from a draft chapter that I have contributed to a forthcoming book produced with my friends in the "holy" scribblers group.

More to follow.