Over the last years, Agent Based Models (ABMs) have become an important in-strument to simulate social complex phenomena. At the same time, they have shown interesting implications for learning activities. To this purpose, we report a simulation on helping behavior carried out by means of an Agent Based Model (ABM) based on four types of different virtual agents: Warm-Glow Cooperators (WG), who give help because it makes them feel better; Gratitude Cooperators (GC), who give help because they previously received help; Cooperators (C), who give help because of both the reasons mentioned above; Defectors (D) who do not give help at all. We explore the pro-social behavior of each type of agents and the system where they live for a certain amount of time in different situations. This specific ABM shows, in the most effective way, why we should increase the level of helping behavior in the population. Furthermore, assuming that giving and receiving help can be both considered positive activities, WG and GC agent strategies should be those who allow to derive the greatest benefit overall. Taking also in account the pedagogical implications of ABMs, the present simulation can be considered as a good instrument to explain dynamics of helping behavior in a virtual society.
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