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There is a parallel between today's stock market and Fischer random chess. The last time we faced a global pandemic was in 1918, and this might as well have been in the BC era. Few of us were alive then, but even the history books are not that useful, as the structure of the US and global economy, the central bank system, the diversity and dynamism of society, and the state of technological progress are nothing like the world knew then. Most of the mental models we as investors rely on are based on an environment that no longer exists. The only common denominator between now and then is that humans have not really changed that much - it takes a few millennia to rewire our DNA and thus our fundamental behavior.

Physicists who think carefully about time point to troubles posed by quantum mechanics, the laws describing the probabilistic behavior of particles. At the quantum scale, irreversible changes occur that distinguish the past from the future: A particle maintains simultaneous quantum states until you measure it, at which point the particle adopts one of the states. Mysteriously, individual measurement outcomes are random and unpredictable, even as particle behavior collectively follows statistical patterns. This apparent inconsistency between the nature of time in quantum mechanics and the way it functions in relativity has created uncertainty and confusion.

The Forum's perspective on present and future technological and societal changes is captured in their 'Principled Framework for the Fourth Industrial Revolution.' Philbeck explained the four principles that characterize the Fourth Industrial Revolution. * Think systems, not technologies. Individual technologies are interesting, but it is their systemic impact that matters. Emerging technologies challenge our societal values and norms, sometimes for good, but sometimes also in negative ways; the Fourth Industrial Revolution will have civilization-changing impact-on species, on the planet, on geopolitics, and on the global economy. Philbeck suggested that wealth creation and aggregation supported by this phase of technological innovation may challenge societal commitments to accessibility, inclusivity, and fairness and create the need for relentless worker re-education. As Philbeck stated, "The costs for greater productivity are often externalized to stakeholders who are not involved in a particular technology's development." * Empowering, not determining. The Forum urges an approach to the Fourth Industrial Revolution that honors existing social principles. "We need to take a stance toward technology and technological systems that empowers society and acts counter to fatalistic and deterministic views, so that society and its agency is not nullified," said Philbeck. "Technologies are not forces; we have the ability to shape them and decide on how they are applied." * Future by design, and not by default. Seeking a future by design requires active governance. There are many types of governance-by individuals, by governments, by civic society, and by companies. Philbeck argued that failure to pay attention to critical governance questions in consideration of the Fourth Industrial Revolution means societies are likely to allow undemocratic, random, and potentially malicious forces to shape the future of technological systems and th

The damage we are not attending to is the deeper nature of the crisis-the collapse of multiple coupled complex systems. Societies the world over are experiencing what might be called the first complexity crisis in history. We should not have been surprised that a random mutation of a virus in a far-off city in China could lead in just a few short months to the crash of financial markets worldwide, the end of football in Spain, a shortage of flour in the United Kingdom, the bankruptcy of Hertz and Niemann-Marcus in the United States, the collapse of travel, and to so much more.

The application of network science to biology has advanced our understanding of the metabolism of individual organisms and the organization of ecosystems but has scarcely been applied to life at a planetary scale. To characterize planetary-scale biochemistry, we constructed biochemical networks using a global database of 28,146 annotated genomes and metagenomes and 8658 cataloged biochemical reactions. We uncover scaling laws governing biochemical diversity and network structure shared across levels of organization from individuals to ecosystems, to the biosphere as a whole. Comparing real biochemical reaction networks to random reaction networks reveals that the observed biological scaling is not a product of chemistry alone but instead emerges due to the particular structure of selected reactions commonly participating in living processes. We show that the topology of biochemical networks for the three domains of life is quantitatively distinguishable, with >80% accuracy in predicting evolutionary domain based on biochemical network size and average topology. Together, our results point to a deeper level of organization in biochemical networks than what has been understood so far.

The application of network science to biology has advanced our understanding of the metabolism of individual organisms and the organization of ecosystems but has scarcely been applied to life at a planetary scale. To characterize planetary-scale biochemistry, we constructed biochemical networks using a global database of 28,146 annotated genomes and metagenomes and 8658 cataloged biochemical reactions. We uncover scaling laws governing biochemical diversity and network structure shared across levels of organization from individuals to ecosystems, to the biosphere as a whole. Comparing real biochemical reaction networks to random reaction networks reveals that the observed biological scaling is not a product of chemistry alone but instead emerges due to the particular structure of selected reactions commonly participating in living processes. We show that the topology of biochemical networks for the three domains of life is quantitatively distinguishable, with >80% accuracy in predicting evolutionary domain based on biochemical network size and average topology. Together, our results point to a deeper level of organization in biochemical networks than what has been understood so far.

Human networks are not random. We connect to others who're like us in some way; social scientists call this homophily. A point of similarity might be something as fundamental to our identity as gender, religion, ethnicity, age, values, or beliefs. Or it could be, and often is, socioeconomic status. Or geographic-say, a neighborhood, city, or region where you grew up. Or an enthusiasm-football, a popular video game, or a breed of dog. When we meet someone at a party, we quickly try to find out if our networks connect in some way. Where are you from? What do you do? How do you know the host? Homophily is a human universal and an essential piece of our mental equipment for sustaining cooperation.

The simple insight that most changes are random had a profound effect on genetics, evolution and ecology.

To control an epidemic, authorities will often impose varying degrees of lockdown. In a paper in the journal Chaos, scientists have discovered, using mathematics and computer simulations, why dividing a large population into multiple subpopulations that do not intermix can help contain outbreaks without imposing contact restrictions within those local communities.