Impact of memory and bias in kinetic exchange opinion models on random networks
In this work we consider the effects of memory and bias in kinetic exchange opinion models. We propose a model in which agents remember the sign of their last interaction with each one of their pairs. This introduces memory effects in the model, since past interactions can affect future ones. We have also considered the impact of a parameter $p$ that regulates how often an agent changes its interaction to match its opinion, thus introducing bias in the interactions. For high values of $p$ an agent is more likely to start having a negative interaction with an agent of opposing opinion and a positive interaction with an agent of the same opinion. The model is defined on the top of random networks with mean connectivity $\langle k \rangle$. We analyze the impact of both $p$ and $\langle k \rangle$ on the emergence of ordered and disordered states in the population. Our results suggest a rich phenomenology regarding critical phenomena, with the presence of metastable states and a non-monotonic behavior of the order parameter. We show that the fraction of neutral agents in the disordered state decreases as the bias $p$ increases.