and who interact with each other and the environment.a number of discrete agents with characteristics.Using spatial microsimulation to generate realistic input populations is one way that predictive ABMs can be made to more closely represent the population under study.Īlthough ABMs can be very diverse, all agent-based models involve at least three things (Castle and Crooks 2006): ABMs require information about the individual people, households or other units on which the population of agents can be based. One of the main barriers to the success of predictive models is reliable, high-resolution data. These attempt to represent the system to a certain level of accuracy in order to predict or explore future states of the system. These explore a system at an abstract level, in an attempt to better understand the underlying dynamics that drive the system. Discussions in the literature are more nuanced, but a broad categorisation is adequate here. This allows ABMs to tackle a very wide range of problems: “agent-based modelling can find new, better solutions to many problems important to our environment, health, and economy” (Grimm and Railsback 2011).ĪBMs can broadly be divided into two general categories. Agent-based models (ABMs) allow analysis of problems that are highly non-linear and emergent, in which the dominant processes guiding change can only be seen after the event. This will be introduced in due course, but prior knowledge of it would be advantageous.Īgent-based modelling is a powerful and flexible approach for simulating complex systems. The chapter uses NetLogo, a language designed for ABM. Agent-based models consist of interacting agents (frequently - but not necessarily - representing people) and an environment that they inhabit. 41 It covers one of the most enticing and potentially useful extensions of the methods developed in the book: agent-based modelling (ABM). This chapter, contributed by Maja Založnik, is the most advanced chapter of the book. This is a preview version of a chapter in the book Spatial Microsimulation with R (Lovelace and Dumont 2016), published by CRC Press.
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