Hybrid and evolutionary Agent-Based Social Simulations using Lévy flight as randomization method

The use of Agent-Based Social Simulations (ABSS) using random-walk algorithms as the randomization approach in an attempt to reproduce human behaviors abound in the literature. However, almost all human actions are nonrandom and include a probabilistic component. This work investigates the use of a probabilistic distribution-based randomization method based on Levy flight as an improvement to the use of traditional random-walk algorithms in cognitive agent models. The fact that Levy flight has been shown to be a more suitable approach than random walk in mimicking natural phenomena, encouraged us to assess whether it could be a more robust randomization method in our agent-based social simulations. Experiments were carried out using Levy flight as the randomization method in our new hybrid evolutionary agent model for ABSS, modeling a realistic scenario of the spread of dengue fever. Previous papers proved that this kind of agent models, which incorporates two components namely, genetic and cultural layers, produce good simulations results. The aim here is to assess whether there is qualitative and quantitative predictive power of the model. This paper looks at the following decision components variations together with the new randomization method: (i) agents only with genetic component; (ii) agents only with cultural component; and (Hi) agents with both genetic and cultural components. The results show the beneficial impact in Levy flight that leads to better prediction in all combinations of decision components.