systems We live in a very complex world where we face complex phenomena such as the formation of social norms and emergence of new disruptive technologies. To better understand such phenomena, social scientists often use a reductionism approach where they reduce complex systems to lower-level variables and model the relationships among them through a scheme of equations such as partial differential equations (PDE). This approach that is called equation-based modeling (EBM) has some basic weaknesses in modeling real complex systems. EBMs emphasize nonrealistic assumptions, such as unbounded rationality and perfect information, while adaptability, evolvability, and network effects go unaddressed. In tackling deficiencies of reductionism, the framework of complex adaptive systems (CAS) has proven very influential in the past two decades. In contrast to reductionism, in the CAS framework, complex phenomena are studied in an organic manner where their agents are supposed to be both boundedly rational and adaptive. As a powerful methodology for CAS modeling, agent-based modeling (ABM) has gained a growing popularity among academics and practitioners. ABMs show how agents’ simple behavioral rules and their local interactions at micro-scale can generate surprisingly complex patterns at macro-scale. In biology Agent-based modeling has been used extensively in biology, including the analysis of the spread of epidemics, and the threat of biowarfare, biological applications including population dynamics, stochastic gene expression, plant-animal interactions, vegetation ecology, migratory ecology, landscape diversity, sociobiology, the growth and decline of ancient civilizations, evolution of ethnocentric behavior, forced displacement/migration, language choice dynamics, cognitive modeling, and biomedical applications including modeling 3D breast tissue formation/morphogenesis, the effects of ionizing radiation on mammary stem cell subpopulation dynamics, inflammation, and the human immune system. Agent-based models have also been used for developing decision support systems such as for breast cancer. Agent-based models are increasingly being used to model pharmacological systems in early stage and pre-clinical research to aid in drug development and gain insights into biological systems that would not be possible a priori. Military applications have also been evaluated. Moreover, agent-based models have been recently employed to study molecular-level biological systems. Agent-based models have also been written to describe ecological processes at work in ancient systems, such as those in dinosaur environments and more recent ancient systems as well. In epidemiology Agent-based models now complement traditional compartmental models, the usual type of epidemiological models. ABMs have been shown to be superior to compartmental models in regard to the accuracy of predictions. Recently,