Learning action selection strategies in complex social systems

In this work, a new method for cognitive action selection is formally introduced, keeping into consideration an individual bias for the agents: ego biased learning. It allows the agents to adapt their behaviour according to a payoff coming from the action they performed at time t-1, by converting an action pattern into a synthetic value, updated at each time, but keeping into account their individual preferences towards specific actions. In agent based simulations, the many entities involved usually deal with an action selection based on the reactive paradigm: they usually feature embedded strategies to be used according to the stimuli coming from the environment or other entities. The actors involved in real Social Systems have a local vision and usually can only see their own actions or neighbours' ones (bounded rationality) and sometimes they could be biased towards a particular behaviour, even if not optimal for a certain situation. Some simulations are run, in order to show the effects of biases, when dealing with an heterogeneous population of agents.