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By: Emily Singer — Quanta Magazine

Applying game theory to the behavior of genes provides a new view of natural selection.

In what appears to be the first study of its kind, computer scientists report that an algorithm discovered more than 50 years ago in game theory and now widely used in machine learning is mathematically identical to the equations used to describe the distribution of genes within a population of organisms. Researchers may be able to use the algorithm, which is surprisingly simple and powerful, to better understand how natural selection works and how populations maintain their genetic diversity.

By viewing evolution as a repeated game, in which individual players, in this case genes, try to find a strategy that creates the fittest population, researchers found that evolution values both diversity and fitness.

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Written By: — Singularity Hub
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In his latest video, host of National Geographic’s Brain Games and techno-poet, Jason Silva, explores the universe’s tendency to self-organize. Biology, he says, seems to have agency and directionality toward greater complexity, and humans are the peak.

“It’s like human beings seem to be the cutting edge,” Silva says. “The evolutionary pinnacle of self-awareness becoming aware of its becoming.”

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The Unleashed Mind
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New research from Emory University School of Medicine, in Atlanta, has shown that it is possible for some information to be inherited biologically through chemical changes that occur in DNA. During the tests they learned that that mice can pass on learned information about traumatic or stressful experiences – in this case a fear of the smell of cherry blossom – to subsequent generations.

According to the Telegraph, Dr Brian Dias, from the department of psychiatry at Emory University, said: ”From a translational perspective, our results allow us to appreciate how the experiences of a parent, before even conceiving offspring, markedly influence both structure and function in the nervous system of subsequent generations.

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