Evolutionary Game Theory
Since Darwin, the goals of evolution seemed to be to create the fittest organism for the given environment. But, according to a recent publication in the Proceedings of the National Academy of Sciences a new approach to viewing the “choices” evolution makes. A Computer Science professor from UC Berkeley was looking at the equations that biologists used for population genetics. These equations describe how the occurrences of certain genetic variations tend to change across generations of a species. The example of this given in the article discusses how certain plants may have thrived in the climates of the past but due to global warming, they no longer have the advantage in more recent generations. The professor, Umesh Vazirani, noticed that these equations were strikingly similar to an algorithm used to play a repeated coordination game. A coordination game is a scenario in game theory where success is based on picking mutually beneficial options. He wondered how game theory could be applied to evolutionary changes at the genetic and chromosomal level.
Using game theory in evolution makes perfect sense. The players in this game are the specific genes of an organism and each gene will have various strategies that correspond to different genetic mutations and variations. This game is played over and over as generations progress and at the end of each game, each player (the genes) evaluates the aptitude of the mutation towards the current environment. The genes will then increase the “weighting” of that trait if it was a benefit to the organism, increasing the inherent value and likelihood of the species continuing to possess such a trait. On the other hand, a trait that proved disadvantageous to the organism will be “downgraded” and will be less likely to appear in the next generation. This makes sense in the current understanding of evolution. But this application of game theory doesn’t always produce the most intuitive results.
Researchers also found that using this new method allowed a deeper focus on the specific genes of a species rather than a single member of the population. On top of that, this algorithm produced results that showed that evolution doesn’t always favor the most positive trait in a population. The algorithm demonstrated that genes in these games also chose diverse traits as dominant strategies, sometimes more often than the best traits. This new application of game theory is shedding a new light on how biologists study the evolutionary patterns of the species of Earth.
Source: http://www.theguardian.com/science/2014/jun/20/game-theory-life-qanta-magazine