The disappointing story of superconductivity in Strontium Ruthenate

In 1994 superconductivity was discovered in strontium ruthenate (Sr2RuO4). This attracted considerable interest because it had a perovskite crystal structure, just like the cuprates. Furthermore, it was a stoichiometric compound and so not plagued by impurities like the cuprates.

In 1998, things got more interesting when NMR Knight shift measurements were interpreted as evidence for triplet superconductivity.

Analogues were made with triplet Cooper pairing in superfluid 3He mediated by ferromagnetic spin fluctuations.

Triplet pairing is associated with odd-parity (spatial) and time-reversal symmetry breaking. Evidence for the latter was claimed from muon spin relaxation (muSR) and the polar Kerr effect.

There are subtle questions about whether a bulk sample of a triplet superconductor exhibits spontaneous magnetisation. Leggett discussed this in an Appendix of his textbook. It turns out the magnetisation probably only exists on the edges.

Aside. The metallic phase is of interest because (unlike the cuprates) it is a Fermi liquid. More recently, it has been argued to be a Hund's metal.

Fueled by hype about topological quantum computing, the past two decades have seen even greater interest in the material due to proposals that it may be a topological superconductor. See for example, this paper.

Now we come to the disappointment. It turns out that the original Knight shift measurements were flawed, probably due to a problem with thermometry.

Recent, careful Knight shift measurements suggest spin-singlet pairing. They were described in a Physics Today article by Alex Lopatka in 2021, An unconventional superconductor isn’t so odd after all. The article describes all the intricacies and challenges of these measurements. Stuart Brown is to be commended for persisting with this problem.

What about the Kerr effect and muSR measurements suggesting time-reversal symmetry breaking?

The polar Kerr effect involves rotation of the plane of polarisation of the electromagnetic radiation by an angle of 65 nanoradians! There is only one group in the world (at Stanford) that can detect these ultra-minute rotations.

muSR may also be problematic. It is not really known where the implanted muon sits in the crystal or what effect it has on the surrounding crystal structure. In particular, these perturbations may produce a small local magnetic field which is nothing to do with the claimed global field due to the magnetism associated with the triplet superconductivity. A recent preprint by Warren Pickett considers some of the challenges associated with interpreting these experiments as evidence for time-reversal symmetry breaking.

What is disappointing about this?

Obviously, it would be nice to have a triplet superconductor and even more a topological one.

However, for me, the big disappointment is that it took almost thirty years for the original NMR measurements to be checked and shown to be wrong. This may reflect several sociological problems.

Kauzmann's maxim: people will tend to believe what they want to believe rather than what the evidence before them might suggest.

The condensed matter community tends to be infatuated with exotica.

There is not enough application of Occam's razor. Luxury journals don't want simple explanations or authors to raise doubts or ambiguities.

As far as I am aware, the 1998 Nature paper on the NMR Knight shift has still not been retracted.

This post was stimulated by a helpful colloquium at UQ given recently by James Annett. He has worked on strontium ruthenate for many years and is a co-author of a relevant review article.

Update. 23 April. James Annett pointed out to me that the authors for the 1998 NMR published a paper in 2020 which acknowledges that their original paper was incorrect.

Reduction of the 17O Knight Shift in the Superconducting State and the Heat-up Effect by NMR Pulses on Sr2RuO4

A multi-disciplinary perspective on mental illness

How is mental illness defined? What causes mental illness? How can a person be healed? Answering these questions will be influenced by our answer to the question of what a person is. Returning to the stratification of reality resulting from emergence, we see that there are social, psychological, neurological, physiological, and genetic dimensions to a person. To illustrate the complexity, I now take a brief tour of different university departments to get their unique perspective on mental health. Each represents a different tradition.

Biomedicine

The biomedical model for mental illness is based on the idea that brains are machines involving physical and chemical processes. Mental illness occurs when these processes do not function normally. Over the past few decades, brain imaging techniques have shown differences between the brains of healthy patients and those with mental illnesses such as depression, schizophrenia, and bipolar disorder. The best course of treatment is deemed to be drugs that target the parts of the brain or processes that are dysfunctional. Sometimes, physical interventions such as electrical shock therapies or surgeries are advocated. This biomedical model was embraced and promoted by most psychiatrists until relatively recently.  

Antidepressant drugs have been widely prescribed, and now there are many studies examining their effectiveness, side effects, and biochemical mechanisms. I mention three scientific problems. First, there is a large placebo effect. This is found in studies where two groups of patients are told they are receiving an antidepressant drug. One group receive the actual drug, and the second group receives a placebo, a pill that, unknown to them, does not contain the drug. The proportion of patients reporting a significant improvement in mental health was about 25% for taking the actual drug compared to 10% for those taking the placebo. In other words, it seems that believing one will get better can lead to significant improvements in mental health. 

Second, there is a large variation between patients concerning how effective the drugs are. Patients’ perceptions of change in their mental health range from getting slight worse to no change to large improvements. Third, the biochemical mechanism of the drugs has become controversial. When the class of drugs known as Selective Serotonin Reuptake Inhibitors (SSRIs) were introduced, psychiatrists were confident that they knew how they work. Depressed patients lacked serotonin. SSRIs blocked the reuptake of serotonin into neurons, increasing the levels of this neurotransmitter in the synaptic cleft. However, a recent meta-analysis concluded as follows. 

“The main areas of serotonin research provide no consistent evidence of there being an association between serotonin and depression, and no support for the hypothesis that depression is caused by lowered serotonin activity or concentrations. Some evidence was consistent with the possibility that long-term antidepressant use reduces serotonin concentration.”

In her book, Mind Fixers: Psychiatry's Troubled Search for the Biology of Mental Illness, Anne Harrington, a historian of science at Harvard, commented.  

“Today one is hard-pressed to find anyone knowledgeable who believes that the so-called biological revolution of the 1980’s made good on most or even any of its therapeutic and scientific promises. It is now increasingly clear to the general public that it overreached, overpromised, overdiagnosed, overmedicated and compromised its principles.”

Psychiatry is a tradition, for better or worse. Its proponents persist in their faith that the biomedical model has the best answers to mental illness, even though the evidence for this belief is ambiguous. Science can involve faith. 

The stakes are high. If a patient takes medication, they may get better, worse, or experience no change. If they don’t take medication, they risk missing out on healing.

Psychology 

Psychologists present a multitude of theories of and treatment plans for mental illnesses. The focus is not on biology but on mental processes. Some focus on the subconscious and others on thoughts we are aware of and can articulate. Some focus on current life experience and thinking patterns, whilst others delve into the past, including unresolved childhood conflict or trauma. Sigmund Freud, the founder of psychoanalysis, claimed that depression was due to aggression toward the self.  A century later, there is no empirical evidence to support his claim. Other psychologists claim depression is predominantly a loss of hope. Opinion is divided about the best method of psychotherapy, where a patient has regular sessions with a trained professional to address unhelpful thoughts, emotions, and behaviours. Names for different methods include Cognitive Behavioural Therapy (CBT), Dialectical Behaviour Therapy (DBT), Psychodynamic Therapy, Humanistic Therapy, and Acceptance and Commitment Therapy (ACT).  Central to CBT is the claim that "Irrational thinking is at the root of much emotional distress that people experience."

This diversity of perspectives and treatments highlights the level of scientific uncertainty about both causes and treatment.

I now mention three developments that are receiving increasing attention in psychology research and have a transcendent dimension.

Mindfulness practices. These involve training patients to focus their “attention on the present moment—thoughts, feelings, sensations, and environment—with an attitude of openness, curiosity, and non-judgment. It involves observing experiences directly, rather than overthinking or reacting impulsively. Key elements include breathing techniques, meditation, and bringing awareness to daily activities.”  (Google AI overview).

Forgiveness. The American Psychological Association offers a continuing education article that cites studies showing that practising forgiveness can improve mental health.  

Awe and wonder. Dacher Keltner has made extensive studies of the experience of awe and recounted them in a popular book.  In a recent article with Maria Monroy, they:  “review recent advances in the scientific study of awe, an emotion often considered ineffable and beyond measurement. Awe engages five processes—shifts in neurophysiology, a diminished focus on the self, increased prosocial relationality, greater social integration, and a heightened sense of meaning—that benefit well-being. We then apply this model to illuminate how experiences of awe that arise in nature, spirituality, music, collective movement, and psychedelics strengthen the mind and body.”

Integrated medicine

The past few decades have seen the rise of integrated medicine, which promotes the view that many diseases, both physical and mental, are best treated by a holistic approach that combines treatments from different specialists. For mental health, it proposes that treatments might include not just drug and talking therapies but also address lifestyle issues. This means considering the role of sleep, exercise, diet, stress reduction, connection to nature, and screen time. With regard to diet, this builds on recent research showing deep connections between what goes on in the gut and the brain. Perhaps this is not surprising because our brains are not disembodied. They are part of our bodies and are connected to our whole nervous system.

Sociology 

Sociologists have investigated how mental illness can arise from social isolation. Emile Durkheim (1858-1917) was one of the founders of sociology. His book, Suicide: A Study in Sociology was published in 1897 and pioneered the scientific study of social phenomena. He proposed that suicide comes in four types, being distinguished by the level of imbalance of two social forces: social integration and moral regulation. Based on a detailed analysis of statistical data, Durkheim concluded that suicide was more likely in men than women, for single people than those who are married, for people without children than people with children, among Protestants than Catholics and Jews, among soldiers than civilians, and in times of peace than in times of war.

Since Durkheim, many more sociological studies suggest that social isolation and a lack of meaningful relationships can be a major contributing factor to depression. Some of this research has been reviewed in a popular book, Lost Connections: Uncovering the Real Causes of Depression and the Unexpected Solutions by Johann Hari.  He was motivated by his own experience of being prescribed and taking antidepressants for many years without consideration of how his social isolation might be a contributing factor.

This short survey of the perspective on mental illness from a range of scientific disciplines illustrates the complexity of the issue, the multifaceted nature of reality, and scientific uncertainty.

Naturally, this survey of different scientific perspectives raises questions about my own experience. Why did the antidepressant drugs seem to work sometimes and not others? Did I experience a placebo effect? Why was mindfulness helpful to me two decades ago but not more recently? What was the role of stress, childhood experiences, social isolation, personal pride, or introversion in creating my mental illness? I simply don’t know the answers to these questions and don’t think I ever will. What does matter is that, somehow at different times, I did experience degrees of healing that allowed me to function, albeit sometimes at diminished levels. Regardless of which traditions you choose to guide your journey and whatever choices you make, trust (faith) is involved.

Quantum BS: piling it higher

Hans Bachor recently gave a talk at UQ, Hype and Trust in Quantum Technologies

Trust is a core value in science, trust in data, analysis, concepts, models. This is achieved in physics by open publishing, scientific discourse, testing, repeating experiments, asking critical questions and designing new tests. Fortunately, science is self-correcting in the long term. Hype includes predictions which sensationalise scientific discoveries and exaggerate the future impact. Increasing competition for funding, visibility or job security can make this more attractive. But it also erodes trust in science by the public and investors and has negative social effects on us the researchers. How can we balance them?

I think this problem more broadly reflects the way universities have become to imitate the social context they are imbedded in, rather than being a critique of those societies.

The sociologist Christian Smith eloquently described the emergence of BS in universities, several years ago.

More superconductivity in Hollywood

I wrote a post about superconductivity being central to the plot of the cult-classic movie, Joe Versus the Volcano. A commenter on the post kindly pointed out that the movie Avatar also features superconductivity. It is nicely captured in this scene.

The Wikipedia entry for Unobtanium is interesting as it describes the long history of the term, predating the movie by decades. I had not heard the term before. It does capture much of the hype and fantasy about research in "advanced materials".

About Avatar the entry states

In the 2009 film Avatar,^[23] "Unobtanium" is the common name of a rare-earth mineral found exclusively in the exomoon Pandora (where the movie takes place, being the fifth moon of the gas giant Polyphemus, which orbits Alpha Centauri A), highly prized (and priced) because of its application as a powerful superconductor material; because of its unusual magnetic properties, entire mountains with high concentrations of unobtanium "levitate" in the atmosphere of Pandora.

The relevance of Labor Day to physicists and philosophers

This past Monday, May 8, was a public holiday in Queensland, marking Labor Day. I don't know why we don't celebrate it on May 1, but that does not matter.

In honour of the event, I post two relevant resources. The first resource is a moving video by Sabine Hossenfelder, who has carved out a post-academic income as a populariser of physics. The video is funny and sad, describing her own experience in academia leading to "Death of a Dream".

Sabine has many poignant observations about the dysfunctionalities of physics in academia, from the personal to the intellectual.

I find it sad that people who leave academia because they could not find a permanent job see themselves as a "failure." First,  most of the select few who get permanent jobs do so because they are at the right place at the right time, not because they are so much more brilliant and productive than others. Second, there is so much more to life than professional success. Finally, Sabine has been an incredible success. She has been able to popularise physics far beyond what has been achieved by others with big names and lots of resources. Furthermore, Sabine has made a significant contribution to the physics community by calling out hype and BS.

The second resource to mark Labor Day is an article, 

In Australian Universities, There Is No Such Thing as a Secure Job, by Danny Wardle. 

It puts a specific (alarming) incident in the broader context of the history of how and why the governance and management of Australian universities have been captured by the ideology of neoliberalism. This has been facilitated by the opportunism and vanity of mediocre academics who become "managers" with million-dollar salaries.

Four scientific reasons to be skeptical of AI hype

The hype about AI continues, whether in business or science. Undoubtedly, there is a lot of potential in machine learning, big data, and large language models. But that does not mean that the hype is justified. It is more likely to limit real scientific progress and waste a lot of resources.

My innate scepticism receives concrete support from an article from 2018 that gives four scientific reasons for concern.

Big data: the end of the scientific method? 

Sauro Succi and Peter V. Coveney

The article might be viewed as a response to a bizarre article in 2008 by Chris Anderson, editor-in-chief at Wired, The End of Theory: The Data Deluge Makes the Scientific Method Obsolete

‘With enough data, the numbers speak for themselves, correlation replaces causation, and science can advance even without coherent models or unified theories’.

Here are the four scientific reasons for caution about such claims given by Succi and Coveney.

(i) Complex systems are strongly correlated, hence they do not (generally) obey Gaussian statistics.

The law of large numbers (central limit theorem) may not apply and rare events may dominate behaviour. For example, consider the power law decays observed in many complex systems. They are in sharp contrast to the rapid exponential decay in the Gaussian distribution. The authors state, "when rare events are not so rare, convergence rates can be frustratingly slow even in the face of petabytes of data."

(ii) No data are big enough for systems with strong sensitivity to data inaccuracies.

Big data and machine learning involve fitting data to a chosen function, such as a "cost function" with many parameters. That fitting involves a minimisation routine which acts on some sort of "landscape." If the landscape is smooth and minima are well-separated and not separated by too large of maxima then the routine may work. However, if the landscape is rough or the routine gets stuck in some metastable state there will be problems, such as over-fitting.

(iii) Correlation does not imply causation, the link between the two becoming exponentially fainter at increasing data size.  

(iv) In a finite-capacity world, too much data is just as bad as no data.

In other words, it is all about curve fitting. The more parameters used the less likely for insight to be gained. Here the authors quote the famous aphorism, attributed to von Neumann and Fermi, "with four parameters I can fit an elephant and with five I can make his tail wiggle."

Aside: an endearing part of the article is the inclusion of tow choice quotes from C.S. Lewis

‘Once you have surrendered your brain, you've surrendered your life’ (paraphrased)

‘When man proclaims conquest of power of nature, what it really means is conquest of power of some men over other men’.

I commend the article to you and look forward to hearing your perspective. Is the criticism of AI hype fair? Are these four scientific reasons good grounds for concern. 

Wading through AI hype about materials discovery

 Discovering new materials with functional properties is hard, very hard. We need all the tools we can from serendipity to high-performance computing to chemical intuition. 

At the end of last year, two back-to-back papers appeared in the luxury journal Nature.

Scaling deep learning for materials discovery

All the authors are at Google. They claim that they have discovered more than two million new materials with stable crystal structures using DFT-based methods and AI.

On Doug Natelson's blog there are several insightful comments on the paper about why to be skeptical about AI/DFT based "discovery".

Here are a few of the reasons my immediate response to this paper is one of skepticism.

It is published in Nature. Almost every "ground-breaking" paper I force myself to read is disappointing when you read the fine print.

It concerns a very "hot" topic that is full of hype in both the science and business communities.

It is a long way from discovering a stable crystal to finding that it has interesting and useful properties.

Calculating the correct relative stability of different crystal structures of complex materials can be incredibly difficult.

DFT-based methods fail spectacularly for the low-energy properties of quantum materials, such as cuprate superconductors. But, they do get the atomic structure and stability correct, which is the focus of this paper.

It is a big gap between discovering a material that has desirable technological properties to one that meets the demanding criteria for commercialisation.

The second paper combines AI-based predictions, similar to the paper above, with robots doing material synthesis and characterisation.

An autonomous laboratory for the accelerated synthesis of novel materials

[we] realized 41 novel compounds from a set of 58 targets including a variety of oxides and phosphates that were identified using large-scale ab initio phase-stability data from the Materials Project and Google DeepMind

These claims have already been undermined by a preprint from the chemistry departments at Princeton and UCL.

Challenges in high-throughput inorganic material prediction and autonomous synthesis

We discuss all 43 synthetic products and point out four common shortfalls in the analysis. These errors unfortunately lead to the conclusion that no new materials have been discovered in that work. We conclude that there are two important points of improvement that require future work from the community: 

(i) automated Rietveld analysis of powder x-ray diffraction data is not yet reliable. Future improvement of such, and the development of a reliable artificial intelligence-based tool for Rietveld fitting, would be very helpful, not only to autonomous materials discovery, but also the community in general.

(ii) We find that disorder in materials is often neglected in predictions. The predicted compounds investigated herein have all their elemental components located on distinct crystallographic positions, but in reality, elements can share crystallographic sites, resulting in higher symmetry space groups and - very often - known alloys or solid solutions. 

Life is messy. Chemistry is messy. DFT-based calculations are messy. AI is messy. 

Given most discoveries of interesting materials often involve serendipity or a lot of trial and error, it is worth trying to do what the authors of these papers are doing. However, the field will only advance in a meaningful way when it is not distracted and diluted by hype and authors, editors, and referees demand transparency about the limitations of their work.  

Gravitational waves and ultra-condensed matter physics

In 2016, when I saw the first results from the LIGO gravitational wave interferometer my natural caution and skepticism kicked in. They had just observed one signal in an incredibly sensitive measurement. A lot of data analysis was required to extract the signal from the background noise. That signal was then fitted the results of numerical simulations of the solutions to Einstein's gravitational field equations describing the merger of two black holes. Depending on how you count about 15 parameters are required to specify the parameters of the binary system [distance from earth, masses, relative orientations of orbits, .... The detection events involve displacement of the mirrors in the interferometer by about 30 picometres!

What on earth could go wrong?!

After all, this was only two years after the BICEP2 fiasco which claimed to have detected anisotropies in the cosmic microwave background due to gravitational waves associated with cosmic inflation. The observed signal turned out to be just cosmic dust! It led to a book, by the cosmologist Brian Keating, Losing the Nobel Prize: A Story of Cosmology, Ambition, and the Perils of Science’s Highest Honor

Well, I am happy to be wrong, if it is good for science. Now almost one hundred gravitational wave events have been observed and one event GW170817 has been correlated with an x-ray observation.

But detecting some gravitational waves is quite a long way from gravitational wave astronomy, i.e, using gravity wave detectors as a telescope, in the same sense as the regular suite of optical, radio, X-ray, ... detectors. I was also skeptical about that. But it does not seem that gravity wave detectors are providing a new window into the universe.

A few weeks ago I heard a very nice UQ colloquium by Paul Lasky, What's next in gravitational wave astronomy?

Paul gave a nice overview of the state of the field, both past and future. 

A key summary figure is below. It shows different possible futures when two neutron stars merge.

The figure is taken from the helpful review

The evolution of binary neutron star post-merger remnants: a review, Nikhil Sarin and Paul D. Lasky

A few of the things that stood out to me.

1. One stunning piece of physics is that in the black hole mergers that have been observed the combined mass of the resulting black hole is three solar masses less than the total mass of the two separate black holes. The resulting loss of mass energy (E=mc^2) of three solar masses is converted into gravitational wave energy within seconds. During this time the peak radiant power was more than fifty times the power of all the stars in the observable universe combined!

I have fundamental questions about a clear physical description of this energy conversion process. First, defining "energy" in general relativity is a vexed and unresolved question with a long history. Second, is there any sense in which needs to describe this in terms of a quantum field theory: specifically conversion of neutron matter into gravitons?

2. Probing nuclear astrophysics in neutron stars. It may be possible to test the equation of state (relation between pressure and density) of nuclear matter. This determines the Tolman–Oppenheimer–Volkoff limit; the upper bound to the mass of cold, non-rotating neutron stars. According to Sarin and Lasky

The supramassive neutron star observations again provide a tantalising way of developing our understanding of the dynamics of the nascent neutron star and the equation of state of nuclear matter (e.g., [37,121,127–131]). The procedure is straight forward: if we understand the progenitor mass distribution (which we do not), as well as the dominant spin down mechanism (we do not understand that either), and the spin-down rate/braking index (not really), then we can rearrange the set of equations governing the system’s evolution to find that the time of collapse is a function of the unknown maximum neutron star mass, which we can therefore infer. This procedure has been performed a number of times in different works, each arriving at different answers depending on the underlying assumptions at each of the step. The vanilla assumptions of dipole vacuum spin down of hadronic stars does not well fit the data [37,127], leading some authors to infer that quark stars, rather than hadronic stars, best explain the data (e.g., [129,130]), while others infer that gravitational radiation dominates the star’s angular momentum loss rather than magnetic dipole radiation (e.g [121,127]).

As the authors say, this is a "tantalising prospect" but there are many unkowns. I appreciate their honesty. 

3. Probing the phase diagram of Quantum Chromodynamics (QCD)

This is one of my favourite phase diagrams and I used to love to show it to undergraduates.

Neutron stars are close to the first-order phase transition associated with quark deconfinement.

When the neutron stars merge it may be that the phase boundary is crossed.

Majorana: mysterious disappearance of a particle and of credibility

The theoretical physicist Ettore Majorana mysteriously disappeared in 1938. Unfortunately, Majorana particles are also going to be associated in history with some mysterious disappearances: their own existence, the prospect of a topological quantum computer, some prominent scientists' reputations, and the credibility of Physical Review journals.

Nine years ago I expressed skepticism that there would ever be a quantum computer based on Majorana fermions. I wish I was wrong. It certainly would be cool. Things are even worse than I thought. The issues (scientific, ethical, technological, hype, ...) were recently highlighted in a strange incident involving a paper from Microsoft that was published by PRB.

I found the commentary of Vincent Mourik on the whole incident enlightening and disturbing. His description is "Here's the full background of my involvement with the recent Microsoft Quantum paper. APS pulled off an arcane unofficial off-the-record peer review already one year ago when it was presented at PRX. And then published it anyway at PRB..."

I do have concerns about the unusual practice used by PRX and the precedent of publishing a paper with incomplete details. However, my more significant concern is that, based on Vincent's report, this paper should never have been published in any self-respecting scientific journal. I fear that in order to "compete" with the luxury journals Physical Review has descended to their low scientific and ethical standards.

I thank Doug Natelson and a commenter on his blog for bringing this sorry saga to my attention.

A few things condensed matter physics has taught me about science (and life)

We all have a worldview, some way that we look at life and what we observe. There are certain assumptions we tend to operate from, often implicitly. Arguably, our worldview is shaped by our experiences: family, friendships, education, jobs, community organisations, and our cultural context (political, economic, and social).

A significant part of my life experience has been working in universities as a condensed matter physicist and being part of a broader scientific community. Writing a Condensed Matter Physics: A Very Short Introduction crystallised some of my thoughts about what CMP might mean in broader contexts. I am more aware of how my experience in CMP has had a significant influence on the way I view not just the scientific enterprise, but also broader philosophical and social issues. Here are a few concrete examples.

Complex systems. The objects studied in condensed matter physics have many interacting components (atoms). Further, there is an incredible diversity of systems (materials and phenomena) that are studied. Many different properties and parameters are needed to characterise a system and its possible states. There are many different ways of investigating each system. Similarly, almost everything else of interest in science and life is a complex system.

Emergence. This is central to CMP. The whole is greater than the sum of the parts. The whole is qualitatively different from the parts. Related features include robustness, universality, surprises, and the difficulty of making predictions. An emergent perspective can provide insights into other complex systems: from biology to psychology to politics.

Differentiation and integration. A key aspect of describing and understanding a complex system is conceptually breaking it into smaller parts (differentiation), determining how those parts interact with one another, and determining how those interacting parts combine to produce properties of the whole system (integration).

Diversity: The value of multiple perspectives and methods. Due to the complexity of condensed matter systems, multiple methods are needed to characterise their different properties. Due to emergence, there are various scales and hierarchies present. Investigating and describing the system at these different scales provides different perspectives on the system. What does the scientist do with all these different perspectives? Interpretation and synthesis are needed. That is not an easy or clearcut enterprise.

 Navigating the middle ground. The most interesting CMP occurs in an intermediate interaction regime that is challenging theoretically. Insight can be gained by considering two extremes that are more amenable to analysis: weak interaction and strong interaction. I had fun using conservative-liberal political tensions as a metaphor for divisions in the strongly correlated electron community.

The Art of Interpretation. Everything requires interpretation: a phone text message, a newspaper article, a novel, a political event, data from a science experiment, and any scientific theory. With interpretation, we assign meaning and significance to something. How we do this is complex and draws on our worldview, both explicitly and implicitly. Regardless of our best intentions, interpretation always has subjective elements.

Synthesis. Given the diversity of data, perspectives, and interpretation, it is a challenge to synthesise them into some coherent and meaningful whole. All the pieces are rarely consistent with one another. Some will be ignored, some discarded, some considered peripheral, and others central. This synthesis is also an act of interpretation.

All models are wrong but some are useful. One way to understand complex systems is in terms of "simple" models that aim to capture the essential features of certain phenomena. In CMP significant progress (and many Nobel Prizes) has resulted from the proposal and study of such models. There is a zoo of them. Many are named after their main inventor or proponent: Ising, Anderson, Hubbard, Heisenberg, Landau, BCS,... All theories in CMP are also models since they involve some level of approximation, at least in their implementation. These models are all wrong, in the sense that they fail to describe all features and phenomena of the system. But, the best models are useful. Their simplicity makes them amenable to understanding, mathematical analysis, or computer simulation. Furthermore, the models can give insight into the essential physics underlying phenomena, predict trends, or be used to analyse experimental data. 

The autonomy of academic disciplines. Reality is stratified. At each level of the hierarchy, one has unique phenomena, methods, concepts, and theories. Most of these are independent of the details of what happens at lower levels of the hierarchy. Given the richness at each level, I do not preference one discipline as more fundamental or important than the others.

Pragmatic limits to knowledge. We know so much.  We know so little. On the one hand, it is amazing to me how successful CMP has been. We have achieved an excellent understanding, at least qualitatively of many emergent phenomena in systems that are chemically and structurally complex (e.g., liquid crystals and superconductivity in crystals involving many chemical elements). On the other hand, there are systems such as glasses and cuprate superconductors that have been incredibly resistant to understanding. Good research is very hard, even for the brilliant. Gains are often incremental and small. This experience leads me to have sober expectations about what is possible, particularly as one moves from CMP to more complex systems such as human societies, national economies, and brains.

Science is a human endeavour. Humans can be clever, creative, insightful, rational, objective, cooperative, fiercely independent and capable of great things. The achievements of science are a great testimony to the human spirit. Humans can also be stubborn, egotistical, greedy, petty, irrational, ruthlessly competitive, and prone to fads, mistakes and social pressures. Science always happens in a context: social, political, cultural, and economic. Context does not determine scientific outcomes but due to human nature, it can corrupt how science is done.

The humanity of scientists leads to a lack of objectivity captured in Walter Kauzmann's maxim: people will tend to believe what they want to believe rather than what the evidence before them suggests that they should believe. My decades of experience working as a scientist leads me to scepticism about extravagant claims that some scientists make, particularly hype about the potential significance (scientific, technological, or philosophical) of their latest discovery or their field of research. Too often such claims do not stand the test of time.

Humility. This brings together practically everything above. The world is complex, people are complex, and human-world interactions are complex. It is easy to be wrong. We often have a pretty limited perspective of what is going on. 

Hysteresis, hype, niches, nudges and social change

The world is a mess. Most people want a better world. Sometimes nothing changes. Sometimes things change incredibly rapidly. Sometimes changes are positive. Other times the change is negative. Often this change is unanticipated, even by experts who have been studying the relevant topic for decades. Wicked problems are things that seem to be incredibly resilient to change. Examples of rapid changes that were (largely) positive and unanticipated were the peaceful collapse of the former Soviet empire, smoking in public becoming taboo, and increased public concern about climate change. Examples of negative changes include the rise of Trumpism, misinformation on social media, and the global financial crisis of 2008.

Many people in government, public policy, NGOs, and social activists want to implement policies and take actions that will produce outcomes that (they believe) are positive. Here I discuss some basic but very important insights from "social physics", such as discussed in my previous two posts.

Suppose the system of interest can be modelled by some type of Ising model where the pseudospin corresponds to two choices (good and bad) for each agent in the system. The policy maker wants to change something such as increase the incentive for agents to make the "good" choice. There are two qualitatively different possible behaviours and they are shown in the Figure below (taken from Bouchaud). 

The vertical axis is the "magnetisation", i.e, the fraction of agents who make the good choice. The horizontal axis is the "external field", i.e, the level of incentive provided for agents to make the good choice. 

Case I. Smooth curve (blue). This occurs when the interaction between agents is weaker than some threshold strength. Suppose that a small but not insignificant minority of agents are already making the good choice and then incentive is increased slightly. If one is near the steep part of the blue curve then this "nudge" can produce a desired outcome for the society.

Case II. Discontinuous curve (red). This occurs when the interaction between agents is greater than some threshold strength. People's choices are influenced more by their friends than by what the government or an NGO is telling them to do. Then one has to provided very large incentives to get a change in agent choice, far beyond the incentive required for a single isolated agent. The system is stuck in a state that is not good for the society as a whole. It is a metastable state, as shown in the figure below.

On the other hand, if the "polarisation field" is sitting near a critical value (5 in the figure, a tipping point), then a "nudge" can lead to a dramatic change for good. 

I think there are important implications for social activists of all stripes. Realistic expectations are key.

1. Don't expect even the best-designed and well-intentioned policy or action to necessarily have the impact you hope for.

2. Be sceptical about hype and ideology. In the public space there are a lot of claims, whether from political parties, pundits, or NGOs, that if we just do X (change this law, donate money, do what my book says, ...) then the good Y will inevitably follow.

The problem with unrealistic expectations is that they lead to disappointment, disillusionment, and burnout. People give up. Then the next fad or "silver bullet" comes along...

Inspired by a rugged landscape perspective, a better and more sustainable approach is that of learning and adaptation. One identifies what one thinks the best "nudge" is, tries something, evaluates the effect, adapts, and tries out some new ideas. One does not claim or expect the first few iterations to produce a significant desired effect. Here, somewhat "random" sampling of the landscape may help. Here a diversity of perspectives and methods can play a positive role. A more concrete version of this argument is in a paper concerned with public health initiatives. Rugged landscapes: complexity and implementation science, by Joseph T. Ornstein, Ross A. Hammond, Margaret Padek, Stephanie Mazzucca & Ross C. Brownson 

Postscript. After posting this I remember reading a recent article in The Economist pointing out how nudges often do not work.

Evidence for behavioural interventions looks increasingly shaky 

The academic literature is plagued by publication bias 

It references three recent Letters in PNAS, including this one, that come to the opposite conclusion to an earlier PNAS paper.

The effectiveness of nudging: A meta-analysis of choice architecture interventions across behavioral domains 

Stephanie Mertens, Mario Herberz, Ulf J. J. Hahnel, and Tobias Brosch

Famous last words

If you ever write a popular book about science I suggest you spend a lot of time honing your very last paragraph. If it is eloquent, grand, and hyperbolic it may be so widely quoted that many people will think that this is actually what the book is about or has proven. Here are a few examples that I often see.

Where then shall we find the source of truth and the moral inspiration for a really scientific socialist humanism? Only, we suggest, in the sources of science itself,..... it is the conclusion to which the search for authenticity necessarily leads. The ancient covenant is in pieces; man at last knows that he is alone in the unfeeling immmensity of the universe, out of which he emerged only by chance. Neither his destiny nor his duty have been written down. The kingdom above or the darkness below: it is for him to choose.''

Jacques Monod, Chance and Necessity: An Essay on the Natural Philosophy of Modem Biology, trans. Austryn Wainhouse (New York: Knopf, 1971), p. 167

But if there is no solace in the fruits of our research, there is at least some consolation in the research itself. Men and women are not content to comfort themselves with tales of gods and giants, or to confine their thoughts to the daily affairs of life; they also build telescopes and satellites and accelerators, and sit at their desks for endless hours working out the meaning of the data they gather. The effort to understand the universe is one of the very few things which lifts human life a little above the level of farce and gives it some of the grace of tragedy.

Steven Weinberg, The First Three Minutes (Basic Books, 1977), pages 154-155.

If we do discover a complete theory, it should in time be understandable in broad principle by everyone, .... Then we shall all ...[discuss] why it is that we and the universe exist. If we find the answer to that, it would be the ultimate triumph of human reason - for then we would truly know the mind of God.  

Stephen Hawking, A Brief History of Time

There is grandeur in this view of life, with its several powers, having been originally breathed by the Creator into a few forms or into one; and that, whilst this planet has gone circling on according to the fixed law of gravity, from so simple a beginning endless forms most beautiful and most wonderful have been, and are being evolved.

Charles Darwin, The Origin of Species

Can you think of any other examples of famous last paragraphs?

A guide through hype about computational chemistry on quantum computer

One of the many problems with hype in science is that it glosses over problems that means they do not get addressed which ultimately hinders real scientific progress. 

There is a lot of hype about how quantum computers will be able to solve problems in materials science that are of industrial significance and thus "herald a new era of chemical research". Such claims are carefully examined and deconstructed in the following preprint. Most of the authors are at Schrodinger, Inc.

How will quantum computers provide an industrially relevant computational advantage in quantum chemistry?

V.E. Elfving, B.W. Broer, M. Webber, J. Gavartin, M.D. Halls, K. P. Lorton, A. Bochevarov

The article is also a useful guide to current state-of-the-art computational chemistry on classical computers.

I reproduce most of the paper abstract below as it is helpful summary.

Numerous reports claim that quantum advantage, which should emerge as a direct consequence of the advent of quantum computers, will herald a new era of chemical research because it will enable scientists to perform the kinds of quantum chemical simulations that have not been possible before. Such simulations on quantum computers, promising a significantly greater accuracy and speed, are projected to exert a great impact on the way we can probe reality, predict the outcomes of chemical experiments, and even drive design of drugs, catalysts, and materials. 

In this work we review the current status of quantum hardware and algorithm theory and examine whether such popular claims about quantum advantage are really going to be transformative. We go over subtle complications of quantum chemical research that tend to be overlooked in discussions involving quantum computers. 

We estimate quantum computer resources that will be required for performing calculations on quantum computers with chemical accuracy for several types of molecules. In particular, we directly compare the resources and timings associated with classical and quantum computers for the molecules H2 for increasing basis set sizes, and Cr2 for a variety of complete active spaces (CAS) within the scope of the CASCI and CASSCF methods. The results obtained for the chromium dimer enable us to estimate the size of the active space at which computations of non-dynamic correlation on a quantum computer should take less time than analogous computations on a classical computer. Using this result, we speculate on the types of chemical applications for which the use of quantum computers would be both beneficial and relevant to industrial applications in the short term.

The authors present a useful typology of claims of quantum advantage that are irrelevant.

1. Irrelevance due to availability of accurate experimental results. 

2. Irrelevance due to availability of conventional computational results. 

3. Irrelevance due to real world complexity:

When simulated chemical processes are very complicated and involve potentially hundreds of intermediates, conformations, or reaction paths, as in catalytic and metabolic pathways, the real research bottleneck lies in a combinatorial explosion of possibilities to probe with simulation.

4. Irrelevance to industrial applications

Many of the issues discussed in the preprint are not unrelated to those associated with hype about using machine learning in computational materials science, and are beautifully critiqued by Roald Hoffmann and Jean-Paul Malrieu.

Towards real materials applications

There is a chasm between finding a material that has a desirable property that is key to a technological application and producing a commercial product. In the hype about materials research, the width of this chasm is too often glossed over.

The Structure of Materials by Samuel M. Allen and Edwin L. Thomas (based on a course in Materials Science and Engineering at MIT) introduces the tetrahedron of

structure, properties, processing, and performance. In condensed matter physics the focus is largely on the relationship between structure and properties. But, for engineering, these are both also related to performance and processing (i.e. ability to make materials and devices).

 The book also emphasises the multiple length scales associated with the structure of "real" materials. The scales range from the atomic scale of Angstroms to the scale of micrometers associated with objects such as grain boundaries, topological defects, and domain walls. These longer length scales are also relevant in liquid crystals, glasses, and polymers.

Sage wisdom on computational materials science

Roald Hoffmann and Jean-Paul Malrieu are two of my favourite living theoretical chemists. Both greatly value the role of concepts and intellectual clarity in theory. Hoffmann has featured in 22 posts on this blog.

They recently published a wonderful trilogy in  Angewandte Chemie.

Simulation vs. Understanding: A Tension, in Quantum Chemistry and Beyond. 

Part A. Stage Setting

Part B. The March of Simulation, for Better or Worse

Part C. Toward Consilience

I add this trilogy to my list of 5 papers every computational chemistry student should read, suggested by me a decade ago. [Malrieu is author of one of those and Hoffmann co-author of another.]

Although the trilogy addresses and uses specific examples from computational quantum chemistry it is just as relevant to anyone interested in computational materials science. Actually, I hope that anyone interested in materials science would read and digest it as it gives a sober and balanced perspective about the relationship between theory, simulation, and understanding.

Articles are timely as they address hype about how AI techniques will "revolutionise" materials theory. 

The articles are beautifully written and engage with broader themes such as philosophy of science, culture, art, and politics.

Finally, I just love this photo of the two authors, both in their eighties. the photo reflects some of the joy they find in science, so beautifully expressed in these articles.

I thank Ben Powell for bringing the papers to my attention.

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.

Implicit versus explicit beliefs

 How can we design a room-temperature superconductor? How can a government stimulate economic growth? How can an NGO help reduce domestic violence? Why do communities become segregated on racial lines? How can I improve my mental health?

These important questions may seem unrelated. However, I propose that often there is a common issue about the strategies that people (whether individuals, professions, NGOs, funding agencies, governments, ...) propose to find answers or when definite answers are proposed.

Many strategies and answers involve a heavy dose of implicit beliefs. These are assumptions that are never stated. They may be elements of a worldview, which according to one definition, is

a commitment, a fundamental orientation of the heart, that can be expressed as a story or in a set of presuppositions (assumptions which may be true, partially true, or entirely false) which we hold (consciously or subconsciously, consistently or inconsistently) about the basic construction of reality, and that provides the foundation on which we live and move and have our being.

 James W. Sire, The Universe Next Door: A basic worldview catalog

These implicit beliefs may relate to values and morality. But I want to focus more on implicit beliefs that are related to academic disciplines such as philosophy of science, psychology, political science, theology, economics, anthropology,  sociology, ...  Most of us have never studied these disciplines and some of us may be skeptical about some of them. But, my point is that everyone has implicit ideas about what is true with regard to the objects these disciplines study. Everyone has a philosophy of science. Everyone has ideas about how minds work and how to change societies. It is just that these beliefs are rarely stated. 

Why does this matter? If implicit beliefs are never stated, they can never be tested, evaluated, critiqued, refined, or rejected. I believe that implicit beliefs are too often based on intuition, prejudice, common sense, or culture (social pressure to conform to accepted wisdom). This is not necessarily bad. Sometimes intuition, common sense, and culture are helpful and correct. We could not survive in life if we did not have them. We simply don't have the time, energy, and resources to constantly question and validate everything. On the other hand, if there is a vacuum, it will get filled with something. A major lesson from scientific history is that intuition, prejudice, common sense are sometimes wrong.

I now give three concrete examples of implicit beliefs. They cover computational materials science, public policy, and social activism.

Understanding materials using computers

Amongst others, there are two things, we would like more computational power to be able to do. One is to do reliable ab initio calculations of the properties of complex molecules and solids, from proteins to crystals with unit cells containing large numbers of atoms.  Another is to do exact diagonalisation (or some alternative reliable method) of many-body Hamiltonians, such as the Hubbard model, on large enough lattices that finite-size effects are minimal or can be reliably accounted for.

Over the past decade, there has been a lot of hype about how quantum computers and/or machine learning techniques will solve these problems and thus initiate a new era of materials understanding, discovery, and design with significant technological and economic benefits. My problem is that these claims usually seem to have the implicit belief that the only obstacle to progress is one of computational power. This fallacy has recently been deconstructed and critiqued in detail in three beautiful essays by Roald Hoffmann and Jean-Paul Malrieu, Simulation vs. Understanding: A Tension, in Quantum Chemistry and Beyond.

Public policy

National economies around the world have been battered by the covid-19 pandemic. In response, governments of prosperous countries are spending big on stimulus packages. This involves taking on massive amounts of debt and significant government intervention in "free" market economies. Will these initiatives achieved their goals, particularly in the long term? Could they actually make things worse? Responses from pundits, both for and against, are laden with implicit beliefs. Unfortunately, economists cannot agree on the answer to the basic question, "Does government stimulus spending actually produce economic growth?" This issue is nicely discussed in a pre-pandemic podcast at Econtalk. 

NGOs and social activism

Many NGOs are about change. They aim to build a better world, addressing problems such as domestic violence, poverty, climate change, corruption, racism,... They aim to promote education, human rights, good governance, democracy, health, transparency, .. I love NGOs. I support many: philosophically, financially, and practically. To survive most NGOs have to raise funds, whether from many small donors or large philanthropies. This requires a well-honed pitch that aims to inspire potential donors to give. Furthermore, the whole operation of most NGOs is laden with implicit beliefs, whether those of the founders, staff or donors.

Consider a hypothetical NGO whose goal is to reduce the number of murders in a country. I chose this example because it may at first appear less controversial and contentious than some. Almost everyone thinks murder is wrong (always) and societies should stop reduce it. But why do murders occur? Revenge, passion, drugs, alcohol, money, politics, racism, ... Will making the purchase of guns difficult reduce the murder rate? Gun lobbyists will claim "Guns don't kill people. Criminals do! Law-abiding citizens need guns for self-defense." (cringe). There are many other alternative strategies: increasing penalties (longer jail terms or even the death penalty), the number of police, weapons for police, community policing, drug rehabilitation, breaking up gangs, ... Wow! It's complicated. My main point is that the hypothetical NGO will probably have an implicit belief that one particular strategy is the best one. Furthermore, if you identify and question this belief reasonable debate may not follow, but it may even be claimed that you don't care about stopping murder.

Some NGOs and philanthropies have become mindful of these issues. In response to a grant application that I helped an NGO write we were asked what our "theory of change" was? I discovered that there is a whole associated "industry. According to theoryofchange.org (!)

One organisation which began to focus on these issues was the US-based Aspen Institute and its Roundtable on Community Change. ... [leading] to the publication in 1995 of New Approaches to Evaluating Comprehensive Community Initiatives. In that book, Carol Weiss, ... hypothesized that a key reason complex programs are so difficult to evaluate is that the assumptions that inspire them are poorly articulated. She argued that stakeholders of complex community initiatives typically are unclear about how the change process will unfold and therefore give little attention to the early and mid-term changes that need to happen in order for a longer term goal to be reached.  

This led to software designed to help organisations plan initiatives, with a particular emphasis on teasing out assumptions embedded in plans. A related method is construction of a logframe matrix [logical framework].

All models are wrong but some are useful. I first learnt this aphorism from Scott Page, in his wonderful course Model Thinking at Coursera.  Models help us think more clearly. Simple quantitative models, such as agent-based models, in the social sciences, have the value that their assumptions can be clearly stated, and then the consequences of these assumptions can be investigated in a rigorous manner.

What do you think? Are there examples that you think involve implicit beliefs that need to be stated explicitly?