Evolutionary Game Theory

Classical game theory, with its mathematical beauty and complexity, has received criticism exactly for being too demanding of computational capability. People complain that that much of the calculation is simply not human. The criticizing contributions come directly from the empirical, experimental and behavioral literature. As a result, the narrative in theoretical literature has shifted substantially from the hyper-rational agent to less demanding, more realistic assumptions. The inspiration for this comes from observing nature.

Nature plays a game that spans billions of years, without any conscious effort on calculation, but sometimes its solution becomes our definition of perfect. How does that happen? Naturally, the idea of evolution in nature originates in biology. The line of thought can be traced back to Darwin and the accumulated contribution before him. Darwin was an enthusiast and his meticulous observations brought this idea together as we know it today. But in 1973, it was Maynard Smith, another biologist, who formally brought game theory and evolution together. He observed that animals have many conflicts but that not all these conflicts lead to war. Because war is a loss for both sides, they develop rituals to show strength and then back down. Maynard Smith analyzed this situation in the form of a game using the language of evolution for the first time. From this point, game theorists started to apply the mechanism of the survival of the fittest to treat social interactions. In other words, we put cultural norms as units under a selection and mutation process. It is worth mentioning that generalized Darwinism (this term is coined by Dawkins, 1983) has gained ground in many critical fields such as economics and technology (with the genetic algorithm).

In game theory and economics, the evolutionary approach simply exploits the large number effect. It lets a population of many individuals play the game or let the game be played over and over again by an individual. The player may not have the ability to do complex optimization but she learns by trial and error. This approach shows that a simple motivation at the individual level (i.e. payoff responsiveness) can make remarkable patterns emerge at macro level. For example, evolutionary game theory has been offering many explanations for questions such as: How does the social norm emerge in society? What is the rationale for property rights? How is it that different societies divide the pie differently? In fact, in many cases, this approach leads to the same hyper solutions that are already established in the game theory's classical literature.

Dawkins, R., 1983. Universal Darwinism. In: Bendall, D.S. (Ed.), Evolution from Molecules to Man.  Cambridge University Press, Cambridge, pp. 403-425.

Maynard Smith, J.. Evolution and Theory of Games. Cambridge University Press, 1982.

Maynard Smith J. and Price G.R. 1973. The logic of animal conflict. Nature 246: 1518.

Maynard Smith J. and Szathmary E. 1995. The Major Transitions in Evolution. Oxford University Press, Oxford

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