and external pressures so as to maintain their functionalities. The task of harnessing that complexity requires consideration of the agents themselves—their diversity, connectedness, and level of interactions. Framework Recent work on the Modeling and simulation of Complex Adaptive Systems has demonstrated the need for combining agent-based and complex network based models. describe a framework consisting of four levels of developing models of complex adaptive systems described using several example multidisciplinary case studies: Complex Network Modeling Level for developing models using interaction data of various system components. Exploratory Agent-based Modeling Level for developing agent-based models for assessing the feasibility of further research. This can e.g. be useful for developing proof-of-concept models such as for funding applications without requiring an extensive learning curve for the researchers. Descriptive Agent-based Modeling (DREAM) for developing descriptions of agent-based models by means of using templates and complex network-based models. Building DREAM models allows model comparison across scientific disciplines. Validated agent-based modeling using Virtual Overlay Multiagent system (VOMAS) for the development of verified and validated models in a formal manner. Other methods of describing agent-based models include code templates and text-based methods such as the ODD (Overview, Design concepts, and Design Details) protocol. The role of the environment where agents live, both macro and micro, is also becoming an important factor in agent-based modelling and simulation work. Simple environment affords simple agents, but complex environments generates diversity of behaviour. Multi-Scale modelling One strength of agent-based modelling is its ability to mediate information flow between scales. When additional details about an agent are needed, a researcher can integrate it with models describing the extra details. When one is interested in the emergent behaviours demonstrated by the agent population, they can combine the agent-based model with a continuum model describing population dynamics. For example, in a study about CD4+ T cells (a key cell type in the adaptive immune system), the researchers modelled biological phenomena occurring at different spatial (intracellular, cellular, and systemic), temporal, and organizational scales (signal transduction, gene regulation, metabolism, cellular behaviors, and cytokine transport). In the resulting modular model, signal transduction and gene regulation are described by a logical model, metabolism by constraint-based models, cell population dynamics are described by an agent-based model, and systemic cytokine concentrations by ordinary differential equations. In this multi-scale model, the agent-based model occupies the central place and orchestrates every stream of information flow between scales. Applications In modeling complex adaptive