An experimental investigation of evolutionary dynamics in the Rock-Paper-Scissors game.
Game theory describes social behaviors in humans and other biological organisms. By far, the most powerful tool available to game theorists is the concept of a Nash Equilibrium (NE), which is motivated by perfect rationality. NE specifies a strategy for everyone, such that no one would benefit by deviating unilaterally from his/her strategy. Another powerful tool available to game theorists are evolutionary dynamics (ED). Motivated by evolutionary and learning processes, ED specify changes in strategies over time in a population, such that more successful strategies typically become more frequent. A simple game that illustrates interesting ED is the generalized Rock-Paper-Scissors (RPS) game. The RPS game extends the children's game to situations where winning or losing can matter more or less relative to tying. Here we investigate experimentally three RPS games, where the NE is always to randomize with equal probability, but the evolutionary stability of this strategy changes. Consistent with the prediction of ED we find that aggregate behavior is far away from NE when it is evolutionarily unstable. Our findings add to the growing literature that demonstrates the predictive validity of ED in large-scale incentivized laboratory experiments with human subjects.
Program for Evolutionary Dynamics, Harvard University, Cambridge, MA 02138, USA.
Moshe
M
Hoffman
CentER, Department of Economics, Tilburg University, PO Box 90153, LE 5000 Tilburg, The Netherlands.
Sigrid
S
Suetens
1] Rady School of Management, UC San Diego, La Jolla, 92093-0553 CA [2] CREED, University of Amsterdam, The Netherlands.
Uri
U
Gneezy
1] Program for Evolutionary Dynamics, Harvard University, Cambridge, MA 02138, USA [2] Department of Mathematics, and Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, 02138.
Martin A
MA
Nowak
D000818:Animals; D005075:Biological Evolution; D005716:Game Theory; D005717:Games, Experimental; D006801:Humans