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TRANSFORMING THE DILEMMA
Author(s) -
Taylor Christine,
Nowak Martin A.
Publication year - 2007
Publication title -
evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.84
H-Index - 199
eISSN - 1558-5646
pISSN - 0014-3820
DOI - 10.1111/j.1558-5646.2007.00196.x
Subject(s) - stochastic game , replicator equation , reciprocity (cultural anthropology) , traveler's dilemma , kin selection , mathematical economics , dilemma , social dilemma , superrationality , strong reciprocity , prisoner's dilemma , group selection , evolutionary game theory , selection (genetic algorithm) , evolutionary dynamics , natural selection , normal form game , game theory , population , microeconomics , biology , economics , mathematics , evolutionary biology , computer science , repeated game , artificial intelligence , social psychology , sociology , psychology , geometry , demography
How does natural selection lead to cooperation between competing individuals? The Prisoner's Dilemma captures the essence of this problem. Two players can either cooperate or defect. The payoff for mutual cooperation, R , is greater than the payoff for mutual defection, P . But a defector versus a cooperator receives the highest payoff, T , where as the cooperator obtains the lowest payoff, S . Hence, the Prisoner's Dilemma is defined by the payoff ranking T > R > P > S . In a well‐mixed population, defectors always have a higher expected payoff than cooperators, and therefore natural selection favors defectors. The evolution of cooperation requires specific mechanisms. Here we discuss five mechanisms for the evolution of cooperation: direct reciprocity, indirect reciprocity, kin selection, group selection, and network reciprocity (or graph selection). Each mechanism leads to a transformation of the Prisoner's Dilemma payoff matrix. From the transformed matrices, we derive the fundamental conditions for the evolution of cooperation. The transformed matrices can be used in standard frameworks of evolutionary dynamics such as the replicator equation or stochastic processes of game dynamics in finite populations.