ON LOCAL PRISONER'S DILEMMA GAME WITH PARETO UPDATING RULE

2000 ◽  
Vol 11 (08) ◽  
pp. 1539-1544 ◽  
Author(s):  
E. AHMED ◽  
A. S. ELGAZZAR

Prisoner's Dilemma games with two and three strategies are studied. The corresponding replicator equations, their steady states and their asymptotic stability are discussed. Local Prisoner's Dilemma games are studied using Pareto optimality. As in the case with Nash updating rule, the existence of tit for tat strategy is crucial to imply cooperation even in one dimension. Pareto updating implies less erratic behavior since the steady state configurations are mostly fixed points or at most 2-cycle. Finally, Prisoner's Dilemma game is simulated on small-world networks which are closer to real systems than regular lattices. There are no significant changes compared to the results of the regular lattice.

2014 ◽  
Vol 2014 ◽  
pp. 1-6
Author(s):  
Yuntao Shi ◽  
Bo Liu ◽  
Xiaoliang Kou ◽  
Xiao Han

We address the problem of the punishment and feedback mechanism for the evolution game on small-world network with varying topology. Based on the strategy updating rule, we propose a new punishment and feedback mechanism; that is, all the individuals of the network will play ann-round Prisoner’s Dilemma Game firstly and then, for the most defectors, their neighbors will punish them and break the connecting link with them and set up the new connecting link for themselves. The mechanism can make the degree of the whole network decrease. We find that the mechanism can help keep the cooperators surviving and make them avoid being wiped out by the defectors. With the mechanism being adopted, the number ofn-round Prisoner’s Dilemma Game (PDG) almost has no effect on the evolution game. Furthermore, the probability of the average connectingkand the scale of the network is related to the result of the evolution game.


1995 ◽  
Vol 76 (1) ◽  
pp. 322-322
Author(s):  
Brian Betz

120 subjects played a six-choice Prisoner's Dilemma game in which a simulated other employed either GRIT or GRIT/Tit-For-Tat with varying levels of communication. Analysis indicated that the addition of Tit-For-Tat to GRIT offers no advantages over the standard GRIT strategy.


2013 ◽  
Vol 280 (1766) ◽  
pp. 20131475 ◽  
Author(s):  
Indrikis Krams ◽  
Hanna Kokko ◽  
Jolanta Vrublevska ◽  
Mikus Āboliņš-Ābols ◽  
Tatjana Krama ◽  
...  

Reciprocal altruism describes a situation in which an organism acts in a manner that temporarily reduces its fitness while increasing another organism's fitness, but there is an ultimate fitness benefit based on an expectation that the other organism will act in a similar manner at a later time. It creates the obvious dilemma in which there is always a short-term benefit to cheating, therefore cooperating individuals must avoid being exploited by non-cooperating cheaters. This is achieved by following various decision rules, usually variants of the tit-for-tat (TFT) strategy. The strength of TFT, however, is also its weakness—mistakes in implementation or interpretation of moves, or the inability to cooperate, lead to a permanent breakdown in cooperation. We show that pied flycatchers ( Ficedula hypoleuca ) use a TFT with an embedded ‘excuse principle’ to forgive the neighbours that were perceived as unable to cooperate during mobbing of predators. The excuse principle dramatically increases the stability of TFT-like behavioural strategies within the Prisoner's Dilemma game.


2012 ◽  
Vol 21 (10) ◽  
pp. 108702 ◽  
Author(s):  
Xiang-Sheng Fang ◽  
Ping Zhu ◽  
Run-Ran Liu ◽  
En-Yu Liu ◽  
Gui-Yi Wei

2011 ◽  
Vol 22 (04) ◽  
pp. 401-417 ◽  
Author(s):  
JUN TANIMOTO

We present and numerically investigate a quadruple co-evolutionary model for 2 × 2 Prisoner's Dilemma games which allows not only for agents to adopt strategy (Cooperation C or Defection D) and for network topology, but also for the probability of link rewiring that controls the speed of network evolution and the updating rule itself. The results of a series of simulations reveal that C agents in a coexisting phase increase their rewiring probability to avoid neighboring D agents' exploitation through the Game Exit Option. This evolutionary process leads most agents to adopt pairwise updating even though Imitation Max update adopted by all agents brings a higher payoff.


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