Cognitive biased action selection strategies for simulations of financial systems

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. This can give good results at an aggregate level, but in certain situations (e.g. Game Theory), cognitive agents, embedded with some learning technique, could give a better representation of the real system. 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. In the paper, a method for cognitive action selection is formally introduced, keeping into consideration an individual bias: 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.