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"OH WATERS, TEEM WITH MEDICINE TO KEEP MY BODY SAFE FROM HARM, SO THAT I MAY LONG SEE THE SUN." - Rig Veda
In 1953, at the dawn of modern computing, Nils Aall Barricelli played God. Clutching a deck of playing cards in one hand and a stack of punched cards in the other, Barricelli hovered over one of the world’s earliest and most influential computers, the IAS machine, at the Institute for Advanced Study in Princeton, New Jersey. During the day the computer was used to make weather forecasting calculations; at night it was commandeered by the Los Alamos group to calculate ballistics for nuclear weaponry. Barricelli, a maverick mathematician, part Italian and part Norwegian, had finagled time on the computer to model the origins and evolution of life.
"Our brains, perched atop a network of nerve cells that ascend the length of our bodies, are thought to have arisen once in an animal hundreds of millions of years ago and then evolved over time. However, new findings suggest instead that brains and nervous systems originated multiple times from scratch."
Nobody knows why we don’t observe these kinds of strange superpositions in the macroscopic world. For some reason, quantum mechanics just doesn’t work on that scale. And therein lies the mystery, one of the greatest in science.
(...)Bolotin then goes on to show that the problem of solving the Schrödinger equation is at least as hard or harder than any problem in the NP class. This makes it equivalent to many other head-scratchers such as the travelling salesman problem. Computational complexity theorists call these problems NP-hard.
"In 1999, the Danish physicist Per Bak proclaimed to a group of neuroscientists that it had taken him only 10 minutes to determine where the field had gone wrong. Perhaps the brain was less complicated than they thought, he said. Perhaps, he said, the brain worked on the same fundamental principles as a simple sand pile, in which avalanches of various sizes help keep the entire system stable overall — a process he dubbed “self-organized criticality.”
Classical phase transitions require what is known as precise tuning: in the case of water evaporating into vapor, the critical point can only be reached if the temperature and pressure are just right. But Bak proposed a means by which simple, local interactions between the elements of a system could spontaneously reach that critical point — hence the term self-organized criticality.
Think of sand running from the top of an hourglass to the bottom. Grain by grain, the sand accumulates. Eventually, the growing pile reaches a point where it is so unstable that the next grain to fall may cause it to collapse in an avalanche. When a collapse occurs, the base widens, and the sand starts to pile up again — until the mound once again hits the critical point and founders. It is through this series of avalanches of various sizes that the sand pile — a complex system of millions of tiny elements — maintains overall stability.
Theoretical neurobiology offers a simpler explanation for all of these effects—from a Bayesian perspective, as the brain is progressively optimized to model its world, its complexity will decrease. A corollary of this complexity reduction is an attenuation of Bayesian updating or sensory learning.
These days, Dawkins makes the news so often for buffoonery that some might wonder how he ever became so celebrated. The Selfish Gene is how. To read this book is to be amazed, entertained, transported. For instance, when Dawkins describes how life might have begun — how a randomly generated strand of chemicals pulled from the ether could happen to become a ‘replicator’, a little machine that starts to build other strands like itself, and then generates organisms to carry it — he creates one of the most thrilling stretches of explanatory writing ever penned. It’s breathtaking.
Dawkins assembles genetics’ dry materials and abstract maths into a rich but orderly landscape through which he guides you with grace, charm, urbanity, and humour. He replicates in prose the process he describes. He gives agency to chemical chains, logic to confounding behaviour. He takes an impossibly complex idea and makes it almost impossible to misunderstand. He reveals the gene as not just the centre of the cell but the centre of all life, agency, and behaviour. By the time you’ve finished his book, or well before that, Dawkins has made of the tiny gene — this replicator, this strip of chemicals little more than an abstraction — a huge, relentlessly turning gearwheel of steel, its teeth driving smaller cogs to make all of life happen. It’s a gorgeous argument. Along with its beauty and other advantageous traits, it is amenable to maths and, at its core, wonderfully simple.
The financial crisis clearly illustrated the importance of characterizing the level of ‘systemic’ risk associated with an entire credit network, rather than with single institutions. However, the interplay between financial distress and topological changes is still poorly understood. Here we analyze the quarterly interbank exposures among Dutch banks over the period 1998–2008, ending with the crisis. After controlling for the link density, many topological properties display an abrupt change in 2008, providing a clear – but unpredictable – signature of the crisis. By contrast, if the heterogeneity of banks' connectivity is controlled for, the same properties show a gradual transition to the crisis, starting in 2005 and preceded by an even earlier period during which anomalous debt loops could have led to the underestimation of counter-party risk. These early-warning signals are undetectable if the network is reconstructed from partial bank-specific data, as routinely done. We discuss important implications for bank regulatory policies.