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How feeling betrayed affects cooperation.

Ramazi P, Hessel J, Cao M - PLoS ONE (2015)

Bottom Line: Then we analyze the evolution of cooperation in a well-mixed population of agents, each of whom is associated with such a payoff matrix.According to the simulations, decreasing the feeling of being betrayed in a portion of agents does not necessarily increase the level of cooperation in the population.However, this resistance of the population against low-betrayal-level agents is effective only up to some extend that is explicitly determined by the payoff matrices and the number of agents associated with these matrices.

View Article: PubMed Central - PubMed

Affiliation: ENgineering and TEchnology institute Groningen (ENTEG), Faculty of Mathematics and Natural Sciences, University of Groningen, Groningen, The Netherlands.

ABSTRACT
For a population of interacting self-interested agents, we study how the average cooperation level is affected by some individuals' feelings of being betrayed and guilt. We quantify these feelings as adjusted payoffs in asymmetric games, where for different emotions, the payoff matrix takes the structure of that of either a prisoner's dilemma or a snowdrift game. Then we analyze the evolution of cooperation in a well-mixed population of agents, each of whom is associated with such a payoff matrix. At each time-step, an agent is randomly chosen from the population to update her strategy based on the myopic best-response update rule. According to the simulations, decreasing the feeling of being betrayed in a portion of agents does not necessarily increase the level of cooperation in the population. However, this resistance of the population against low-betrayal-level agents is effective only up to some extend that is explicitly determined by the payoff matrices and the number of agents associated with these matrices. Two other models are also considered where the betrayal factor of an agent fluctuates as a function of the number of cooperators and defectors that she encounters. Unstable behaviors are observed for the level of cooperation in these cases; however, we show that one can tune the parameters in the function to make the whole population become cooperative or defective.

No MeSH data available.


Percentage of cooperators with respect to the number of iterations when the betrayal factors of the agents change by the function b(.) in (Eq 20) and also when the agents do not update their strategies, but instead cooperate when b < 1 and defect when b > 1.Four different situations are shown after 5 runs and 2000 iterations per simulation with a3 set to 0.5 (yellow), 1 (green), 1.5 (blue) and 2 (red), a1 and a2 both set to 1.
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pone.0122205.g015: Percentage of cooperators with respect to the number of iterations when the betrayal factors of the agents change by the function b(.) in (Eq 20) and also when the agents do not update their strategies, but instead cooperate when b < 1 and defect when b > 1.Four different situations are shown after 5 runs and 2000 iterations per simulation with a3 set to 0.5 (yellow), 1 (green), 1.5 (blue) and 2 (red), a1 and a2 both set to 1.

Mentions: It is also possible to turn the population to full defection or full cooperation almost independent of the initial number of cooperators by tuning the parameters a1, a2 and a3. In Figs 13–15, each of the variables a1, a2 and a3 are varied in turn to see what their respective effects on the levels of cooperation are. From the figures one can see that large values of a1 and a3 will lead to full cooperation and larger values of a2 will lead to full defection. This knowledge can be used to tune the model once experimental data from social experiments are known.


How feeling betrayed affects cooperation.

Ramazi P, Hessel J, Cao M - PLoS ONE (2015)

Percentage of cooperators with respect to the number of iterations when the betrayal factors of the agents change by the function b(.) in (Eq 20) and also when the agents do not update their strategies, but instead cooperate when b < 1 and defect when b > 1.Four different situations are shown after 5 runs and 2000 iterations per simulation with a3 set to 0.5 (yellow), 1 (green), 1.5 (blue) and 2 (red), a1 and a2 both set to 1.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4414597&req=5

pone.0122205.g015: Percentage of cooperators with respect to the number of iterations when the betrayal factors of the agents change by the function b(.) in (Eq 20) and also when the agents do not update their strategies, but instead cooperate when b < 1 and defect when b > 1.Four different situations are shown after 5 runs and 2000 iterations per simulation with a3 set to 0.5 (yellow), 1 (green), 1.5 (blue) and 2 (red), a1 and a2 both set to 1.
Mentions: It is also possible to turn the population to full defection or full cooperation almost independent of the initial number of cooperators by tuning the parameters a1, a2 and a3. In Figs 13–15, each of the variables a1, a2 and a3 are varied in turn to see what their respective effects on the levels of cooperation are. From the figures one can see that large values of a1 and a3 will lead to full cooperation and larger values of a2 will lead to full defection. This knowledge can be used to tune the model once experimental data from social experiments are known.

Bottom Line: Then we analyze the evolution of cooperation in a well-mixed population of agents, each of whom is associated with such a payoff matrix.According to the simulations, decreasing the feeling of being betrayed in a portion of agents does not necessarily increase the level of cooperation in the population.However, this resistance of the population against low-betrayal-level agents is effective only up to some extend that is explicitly determined by the payoff matrices and the number of agents associated with these matrices.

View Article: PubMed Central - PubMed

Affiliation: ENgineering and TEchnology institute Groningen (ENTEG), Faculty of Mathematics and Natural Sciences, University of Groningen, Groningen, The Netherlands.

ABSTRACT
For a population of interacting self-interested agents, we study how the average cooperation level is affected by some individuals' feelings of being betrayed and guilt. We quantify these feelings as adjusted payoffs in asymmetric games, where for different emotions, the payoff matrix takes the structure of that of either a prisoner's dilemma or a snowdrift game. Then we analyze the evolution of cooperation in a well-mixed population of agents, each of whom is associated with such a payoff matrix. At each time-step, an agent is randomly chosen from the population to update her strategy based on the myopic best-response update rule. According to the simulations, decreasing the feeling of being betrayed in a portion of agents does not necessarily increase the level of cooperation in the population. However, this resistance of the population against low-betrayal-level agents is effective only up to some extend that is explicitly determined by the payoff matrices and the number of agents associated with these matrices. Two other models are also considered where the betrayal factor of an agent fluctuates as a function of the number of cooperators and defectors that she encounters. Unstable behaviors are observed for the level of cooperation in these cases; however, we show that one can tune the parameters in the function to make the whole population become cooperative or defective.

No MeSH data available.