Computational Model Library

In-group favoritism due to friend selection strategies based on fixed tag and within-group reputation (version 1.1.0)

The explanations are as follows. 1.Each agent has a fixed tag (blue or red). He/she cannot change it through a simulation. A group is a collective of agent with the same tag. 2.Each agent has a friend list. It means who is his/her friend and enemy before playing the giving game. 3.Each agent has a strategy. He/she can change it through a simulation. 4.Suppose that agent i encounters j. A strategy is composed of two parts. One corresponds to the case that i belongs to the same group as j and the other does to the case that i belongs to a different group. Each part will be either of the following C, T, I, or D sub-strategy. C regards another agent as a friend regardless of another’s good or bad reputation. T regards another agent as a friend if another’s reputation is good and as an enemy if bad. I regards another agent as a friend if another’s reputation is bad and as an enemy if good. D regards another agent as an enemy, regardless of his/her good or bad reputation. 5.Each generation consists of five phases: reputation, perception, action, selection and mutation. 6.In the reputation phase, group members evaluate how cooperative an agent was toward them (reputation), and share it among group members. Agent j’s reputation for agent i is assumed to be j’s reputation in group I where i belongs to. 7.In the perception phase, each agent updates his/her friend list using his/her strategy. 8.In the action phase, each agent encounters other agents at random and plays a giving game. In the game, agent i cooperates with agent j if j is i’s friend and not if j is i’s enemy. If i cooperates with j, i pays a cost c and j receives a benefit b (b > c). If i does not cooperate with j, both receive and pay nothing. 9.In the selection phase, agents with lower payoffs abandon their strategies and imitate the strategy of the agent with the highest payoff. 10.In the mutation phase, some agents are assigned at random to abandon their strategies and adopt randomly selected strategies.

Download Version 1.1.0
Version Submitter First published Last modified Status
1.1.0 Yutaka Nakai Fri Mar 28 12:41:40 2014 Fri Mar 28 12:41:40 2014 Published
1.0.0 Yutaka Nakai Fri Mar 28 12:34:55 2014 Fri Mar 28 12:34:55 2014 Published

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