Agent-based modelling and simulation (ABMS) is a relatively new approach to modelling systems composed of autonomous, interacting agents. Agent-based modelling is a way to model the dynamics of complex systems and complex adaptive systems. Such systems often self-organize themselves and create emergent order. Agent-based models also include models of behaviour (human or otherwise) and are used to observe the collective effects of agent behaviours and interactions. The development of agent modelling tools, the availability of micro-data, and advances in computation have made possible a growing number of agent-based applications across a variety of domains and disciplines. This article provides a brief introduction to ABMS, illustrates the main concepts and foundations, discusses some recent applications across a variety of disciplines, and identifies methods and toolkits for developing agent models.

Purpose: This paper is to describe development of the features and functions of Repast Simphony, the widely used, free, and open source agent-based modeling environment that builds on the Repast 3 library. Repast Simphony was designed from the ground up with a focus on well-factored abstractions. The resulting code has a modular architecture that allows individual components such as networks, logging, and time scheduling to be replaced as needed. The Repast family of agentbased modeling software has collectively been under continuous development for more than 10 years. Method: Includes reviewing other free and open-source modeling libraries and environments as well as describing the architecture of Repast Simphony. The architectural description includes a discussion of the Simphony application framework, the core module, ReLogo, data collection, the geographical information system, visualization, freeze drying, and third party application integration. Results: Include a review of several Repast Simphony applications and brief tutorial on how to use Repast Simphony to model a simple complex adaptive system. Conclusions: We discuss opportunities for future work, including plans to provide support for increasingly large-scale modeling efforts.

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The economic impact of community-associated methicillin-resistant Staphylococcus aureus (CA-MRSA) remains unclear. We developed an economic simulation model to quantify the costs associated with CA-MRSA infection from the societal and third-party payer perspectives. A single CA-MRSA case costs third-party payers $2,277 – $3,200 and society $7,070 – $20,489, depending on patient age. In the United States (US), CA-MRSA imposes an annual burden of $478 million - 2.2 billion on third-party payers and $1.4 billion - 13.8 billion on society, depending on the CA-MRSA definitions and incidences. The US jail system and Army may be experiencing annual total costs of $7 – 11 million ($6 – 10 million direct medical costs) and $15 – 36 million ($14 – 32 million), respectively. Hospitalization rates and mortality are important cost drivers. CA-MRSA confers a substantial economic burden to third-party payers and society, with CA-MRSA-attributable productivity losses being major contributors to the total societal economic burden. Although decreasing transmission and infection incidence would decrease costs, even if transmission were to continue at present levels, early identification and appropriate treatment of CA-MRSA infections before they progress could save considerable costs.

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We have developed AgentCell, a model using agent-based technology to study the relationship between stochastic intracellular processes and behavior of individual cells. As a test-bed for our approach we use bacterial chemotaxis, one of the best characterized biological systems. In this model, each bacterium is an agent equipped with its own chemotaxis network, motors and flagella. Swimming cells are free to move in a 3D environment. Digital chemotaxis assays reproduce experimental data obtained from both single cells and bacterial populations.