Competition and cooperation among different punishing strategies in the spatial public goods game.
Inspired by the fact that people have diverse propensities to punish wrongdoers, we study a spatial public goods game with defectors and different types of punishing cooperators. During the game, cooperators punish defectors with class-specific probabilities and subsequently share the associated costs of sanctioning. We show that in the presence of different punishing cooperators the highest level of public cooperation is always attainable through a selection mechanism. Interestingly, the selection does not necessarily favor the evolution of punishers who would be able to prevail on their own against the defectors, nor does it always hinder the evolution of punishers who would be unable to prevail on their own. Instead, the evolutionary success of punishing strategies depends sensitively on their invasion velocities, which in turn reveals fascinating examples of both competition and cooperation among them. Furthermore, we show that under favorable conditions, when punishment is not strictly necessary for the maintenance of public cooperation, the less aggressive, mild form of sanctioning is the sole victor of the selection process. Our work reveals that natural strategy selection cannot only promote, but sometimes also hinders competition among prosocial strategies.
School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China.
Xiaojie
X
Chen
Institute of Technical Physics and Materials Science, Centre for Energy Research, Hungarian Academy of Sciences, P.O. Box 49, H-1525 Budapest, Hungary.
Attila
A
Szolnoki
Faculty of Natural Sciences and Mathematics, University of Maribor, Koroška cesta 160, SI-2000 Maribor, Slovenia.|Department of Physics, Faculty of Sciences, King Abdulaziz University, SA-21589 Jeddah, Saudi Arabia.|CAMTP-Center for Applied Mathematics and Theoretical Physics, University of Maribor, Krekova 2, SI-2000 Maribor, Slovenia.
Matjaž
M
Perc
D003163:Competitive Behavior; D003198:Computer Simulation; D003299:Cooperative Behavior; D005717:Games, Experimental; D006801:Humans; D008960:Models, Psychological; D009010:Monte Carlo Method; D011336:Probability; D011678:Punishment / Q000523:psychology*