An aspect-oriented domain-specific language for modeling multi-agent systems in social simulations
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
| Hi-index | 0.00 |
This paper investigates a new hybrid evolutionary agent model for agent-based social simulations (ABSS), which incorporates two decision components: (i) sub-symbolic (genetic) and (ii) symbolic (cultural). These components are coherently combined to produce a more plausible agent model. Experiments were carried out using the Plausible Agents Matrix (PAX) framework, and modeled a real dengue fever spreading scenario. They aim to analyze the qualitative and quantitative predictive power of the model. Previous work has explored the impact of structuring elements on agents’ behaviors and the impacts of communication mechanisms on agents’ behaviors using PAX. In this paper we investigated three types of agent models regarding to the combination of decision components: (1) agents only with genetic component; (2) agents only with cultural component; and (3) agents with both genetic and cultural components. Results show the importance of each component in the model and their synergic effects when combined.