Exploring Selfish versus Altruistic Behaviors in the Ultimatum Game with an Agent-Based Model

Andrea Scalco, Andrea Ceschi, Riccardo Sartori, Enrico Rubaltelli


In the present study we developed a simulation where agents play repeatedly the ultimatum game with the aim of exploring their earnings for several thresholds of willingness to accept proposals. At the same time, the scope is also to provide a simple and easily understandable simulation of the ultimatum game. Particularly, the simulation generates two kinds of agents, whose proposals are generated accordingly to their selfish or selfless behavior; subsequently, agents compete in order to increase their wealth playing the ultimatum game with a random-stranger matching. The trend emerged by simulation charts shows how, even when altruistic agents bid higher proposals than those following selfish behaviors, the average mean cash earned is higher for the former agents than the latter. A second fact is that, looking at the system as a whole, altruistic punishment leads to a reduction of the resources exploited by the agents. Finally, we introduced the psychological construct of trait emotional intelligence, briefly discussing the value of its implementation into computational simulations.