Differential survival and reproduction of individuals due to differences in phenotype; a key mechanism of evolution.
Evolutionary Stable Strategies (ESS) are a fundamental concept in evolutionary game theory. They provide a framework for understanding how strategies evolve and persist in populations over time.
An Evolutionary Stable Strategy is a strategy that, if adopted by a population in a game, cannot be invaded by any alternative strategy that is initially rare. It is an equilibrium refinement of the Nash Equilibrium. In simpler terms, an ESS is a strategy that, once it is established in a population, will resist change.
For a strategy to be considered an ESS, it must meet two conditions:
Invasion Condition: If all members of a population adopt this strategy, then no mutant strategy could invade and increase in the population under the influence of natural selection.
Stability Condition: If almost all members of a population adopt this strategy, and only a small proportion adopt a different strategy, then the original strategy should do better.
ESS has been observed in various scenarios in nature and society. Here are a few examples:
Animal Behavior: In certain species of lizards, males exhibit two distinct behaviors: territorial males, who guard a specific territory and mate with the females within it, and sneaker males, who do not have a territory and try to mate with females in the territories of others. These two strategies form an ESS, as a population cannot be invaded by a third strategy, and neither strategy can fully replace the other.
Traffic Flow: Driving on the left or right side of the road can be considered an ESS. Once a norm is established (driving on the right in the US, for example), any deviant behavior (driving on the left) is quickly eliminated because it leads to negative outcomes (accidents).
Understanding Evolutionary Stable Strategies provides valuable insights into the dynamics of strategic interactions in various fields, from biology to economics to social sciences. By studying these strategies, we can better understand the forces that shape behavior and decision-making in complex systems.