From the Simons Foundation's 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.Previously on the Quanta channel:
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.
Some biologists say that the findings are too new and theoretical to be of use; researchers don’t yet know how to test the ideas in living organisms. Others say the surprising connection, published Monday in the advance online version of the Proceedings of the National Academy of Sciences, may help scientists understand a puzzling feature of natural selection: The fittest organisms don’t always wipe out their weaker competition. Indeed, as evidenced by the menagerie of life on Earth, genetic diversity reigns.
“It’s a very different way to look at selection,” said Stephen Stearns, an evolutionary biologist at Yale University who was not involved in the study. “I always find radically different ways of looking at a problem interesting.”
The algorithm, which has been used to solve problems in linear programming, zero-sum games and a dozen other sophisticated computer science problems, is used to determine how an agent should weigh possible strategies when making a series of decisions. For example, imagine that you have 10 financial experts giving you advice on how to invest your savings. Each day you have to choose to follow one of them. At the start of the investment period, you know nothing about how well each expert performs. But every day, the multiplicative weights update algorithm, as it is called, instructs you to boost the probability of choosing the experts who have given the best advice and decrease it for those who have performed poorly.
“If you do this day after day, at the end of the year, you will do almost as well as if you had followed the best expert from the beginning,” said Christos Papadimitriou, a computer scientist at the University of California, Berkeley. “It’s as if you were omniscient in the beginning, singling out the best expert and following their advice day after day.”
Papadimitriou and his collaborators came across the connection between game theory and evolution when they were searching for a mathematical explanation of sex...MORE
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