You have told me, you miss my physics posts. I have missed them, too, and I give it a try. But I cannot help turning this into a cross-over again, smashing together half-digested psychology, physics, IT networking, and badly hidden autobiographical anecdotes.
In 2005 I did research on the incorporation of physics-style thinking and mathematical models into non-science disciplines. Actually, it was a small contribution to an interdisciplinary research project, and I have / should have covered science-y ideas related to how revolutionary new ideas percolate society.
In retrospect, my resulting (German) paper was something in between science writing, thorough research including differential equations in detail – and some bold assumptions, partly inspired by popular science, cliché and science fiction. Probably like my posts, but more long-winded and minus the very obvious rants.
I built on my work in laser-materials processing, superconductivity, phase transitions, and I tried to relate chaos in thermodynamic systems and instabilities in fluids with related non-predictable diffusion of ideas.
Simulation of Rayleigh-Taylor instabilities at the interface of fluids with different densities. You could probably test this with Caffe Latte.
I learned that there is a discipline called Networking Theory:
Many networked structures obey very similar rules. Networks of WWW hyperlinks, citations scientific papers, food chains, and airline networks are called scale-free networks, because the distribution function for the number of links follows a power law.
A small number of nodes has a high number of connections and the structure the networks appears the same on every scale applied – it is self-similar. The power law is only valid for ever growing networks.
Network following a power-law distribution of connections. The backbone of the network is established by a few strong, well-connected nodes, and the vast majority of nodes has only a few connections.
The dynamics of such networks could be modeled using the same math as esoteric Bose-Einstein condensation, which allowed me to combine anything and relate networks and the quantum phenomena in superconductors.
But the basic idea is really simply: The more popular nodes attract more links. This is a winner-take-all model.
Companies have started monetizing network research by analyzing and modelling hidden structures and unveiling the the fabric underlying politics and economy.
Re-visiting that old article of mine I spot an application of physics in something-else-dynamics I have missed: One of the classical non-academic jobs for a (theoretical) physicist is Wall Street quantitative analyst or quant. Quants for example apply models taken from thermodynamics, such as diffusion in supernovas, to the finance world.
I would put The Physics of Wall Street – A Brief History of Predicting the Unpredictable on my Books-to-Read List if it would be available on Kindle, as I enjoyed this review:
The author, James Owen Weatherall is
an assistant professor of logic and philosophy of science at the University of California, Irvine, has two Ph.D.’s — one in physics and mathematics, and one in philosophy.
The book gives an overview of different models that resemble physics or are borrowed from physics – such as the Black-Scholes model that uses Brownian motion to model the dynamic development of prices of derivative financial products. Don’t ask me for details – I am just dropping keywords here.
The book seems to be based on optimistic assumptions:
Weatherall wants a new Manhattan Project to determine what’s wrong with economics, and he thinks it should be based in no small part on the contributions of physics-oriented economists, some of whom he believes have been treated unfairly by the establishment.
Here it is getting very interesting:
He has little use for Nassim Taleb, whose best-selling book “The Black Swan” argues that the models used by traders disastrously underestimated the possibility of very negative outcomes — the black swans. To say that a model failed, Weatherall contends, is not to say that no models can work. “We use mathematical models cut from the same cloth to build bridges and to design airplane engines, to plan the electric grid and to launch spacecraft,”
… as I am currently reading Nassim Taleb’s The Black Swan
In my outdated review article I finally came to the conclusion that some aspects of seemingly complicated systems – including those based on human beings – can be modeled using models of a baffling simplicity in relation to the alleged complexity of human nature. I am not ashamed of pointing out this glaring contradiction with my recent posts on gamification.
Taleb speaks to me – in particular his chapter about Ludic Fallacy. I do enjoy the clichéd characters of Fat Tony, the intuitive deal maker who hacks the real world, versus Dr. John, the nerdy engineering PhD who is fond of building mathematical models.
Taleb says:
Have you ever wondered why so many of these straight-A students end up going nowhere in life while someone who lagged behind is now getting the shekels, buying the diamonds, and getting his phone calls returned? Or even getting the Nobel Prize in a real discipline (say, medicine)
I took all my self-irony pills in order to recover. How could I not remember my indulgence in this diagram proving the braininess / nerdiness of physicists (and philosophers) – and my straight As of course. Did I mention that I am not a high-powered executive today or an accomplished professor? So it is Dr. Jane speaking here.
How could I not remember those enlightening anecdotes in David Goleman’s pop-psychology bestseller on EQ – emotional intelligence, first published in 1996. He told the story of two equally gifted students of mathematics, one becoming a rock star scientist, the other one becoming a mere computer consultant. I have read this book in German, so I will not give you a verbatim quote translated back to English. Actually, Goleman said something like: He pretended / claimed to be happy as a computer consultant. It says a more about me than about Goleman that I can quote this from memory without touching the book. I could say a lot of things about the notion of pretense here, but I will not repeat my most recent loosely related rant.
Goleman and Taleb both agree on the overarching role of intuition, thinking outside-the-box, gut feeling or whatever you call this. Luckily, Taleb is not concerned so much with proving which part of the brain is responsible for what because this is the part of pop-psy books I find incredibly boring. Nobody in his right mind would disagree ( …. with the fact that interpersonal skills are important, not with my judgement of pop-psy books).
Even I tend so say, my modest successes in Mediocristan are largely due to my social skills whereas technical skills are needed to meet the minimum bar. Mediocristan is Taleb’s world of achievements limited by natural boundaries, such as: You will not get rich by being paid on time and material. You might get rich in Extremistan, as a best selling author or musician, but you have to deal with the extremely low probability of such a Black Swan of a success.
I am trying my hands at Occam’s Razor now and attempt to sort out this contradictions.
I believe that mathematical models of society make sense, and I do so without having read more propaganda by econo-physicists. I do so even if I will go on ranting about physicists that went into finance and caused a global crisis, because they just wanted to play with nice physics (as we said at the university) – ignoring that there is more at stake than your next research grant or paper.
Models of society and networks make sense if and only if we try to determine a gross statistical property of an enormous system. This is perfect science based on numbers that are only defined in terms of statistics – such as temperature in thermodynamics.
Malcom Gladwell is a master story teller in providing some convincing examples that proves that sometimes it only context that matters and that turns us into automata. For example subjects – who were not informed about the experimental setup – were inquired about their ethical standards. Would you help the poor? Of course they would. Then the experimental (gamified!) setup urged the subjects to hurry to another location, under some pretext. On their way, they were confronted with (fake) poor persons in need. The majority of persons did not help the poor, not missing the next fake meeting was the top priority. Gladwell’s conclusion is that context very often matters more – and in a simple and predictable – than all our sophisticated ethical constructs.
This is probably similar to our predictability as social networking animals, that is: clicking, liking and sharing automata. People in a stadium clapping their hands will synchronize, in a way similar to fireflies synchronizing their blinking. You can build very simple models and demonstrate them using electrically connected light bulbs equipped with trigger logics – and those bulbs will synchronize after a few cycles.
Enthusiasm ends here.
I believe that using and validating those reliable models we learn something about society that is not exactly ground-breaking.
We can model the winner-take-all behavior of successful blogs to whom all the readers gravitate by Bose-Einstein condensation. But so what? What exactly did science tell us that we did not know before and considered trivial everyday wisdom?
In particular, we learn nothing that would help us, as individual nodes in these networks, to cope with the randomness we are exposed to if we aimed at success in Extremistan.
Mr. Taleb, keep preaching on!
However, I still need to wrap my head around the synthesis of:
- not falling for the narrative fallacy, denarrating, and ignoring TV and blogs.
- but yet: focusing on the control of my decisions and trying to grasp the abstract concepts of probability in every moment.
Black Swan (Wikimedia). I wanted to embed an image of Nathalie Portman in Black Swan ballet dancer’s costume, but I did not find a public domain image quickly, and I am not bold enough to do so without cross-checking copyright issues.
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Further reading:
Linked: The New Science Of Networks
The Tipping Point: How Little Things Can Make a Big Difference
