Emergence and complexity in agent-based modeling: Review of state-of-the-art research
Şehnaz Cenani
Abstract
Agent-based systems are an important application area of artificial intelligence and are used in decision support systems. Rather than being a problem-solving tool, agent-based system is a tool for developing and testing alternative solutions according to various scenarios. In this context, agent-based modeling is a very effective method to support decision makers in emergency situations to evaluate different risk scenarios and then make decisions quickly and effectively. Moreover, agent-based modeling is a very useful method to support decision makers in situations of high complexity and uncertainty. The aim of this study is to review state-of-the-art research and give researchers insights into how to use agent-based modeling while developing decision support systems. This paper introduces current studies performed with several agent-based modeling toolkits and software environments such as NetLogo, AnyLogic, MATSim and Repast. In this paper, after giving a brief definition of an agent-based system and explaining the importance of concepts such as emergence and complexity in the field of agent-based modeling, it is explained who uses the agent-based models for what purpose, when, where, why and how to use agent-based modeling through selected examples from state-of-the-art studies carried out in different research fields. Furthermore, what current studies teach us and how future studies can benefit from agent-based models are briefly discussed.