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Cookies must be enabled in your browser while using our system. Toggle navigation. One such approach is agent-based modelling ABM which allows one to simulate the individual actions of diverse agents, and to measure the resulting system behaviour and outcomes over time. The distinction between these new approaches and the more aggregate, static conceptions and representations that they seek to complement, if not replace, is that they facilitate the exploration of system processes at the level of their constituent elements.
Essential to the progression of ABM has been the development of automata approaches more generally. An automaton is a processing mechanism with character- istics that change over time based on its internal characteristics, rules and external input. Automata process information input to them from their surroundings and their characteristics are altered according to rules that govern their reaction to these inputs. Two classes of automata tools — cellular automata CA and agent-based models — have been particularly popular, and their use has dominated the research literature.
The purpose of this chapter is to provide an overview to ABM. The key features of an agent-based model will be presented along with a discussion of what consti- tutes an agent-based model and brief overviews of the main areas of consideration when undertaking modelling.
The advantages and disadvantages of ABM for simulating geographical systems are then discussed before an overview of geographical applications are given.
We conclude the chapter with a summary and discussion of areas that require further consideration. From a pragmatic modelling standpoint, there are several features that are common to most agents Wooldridge and Jennings — extended and explained further by Franklin and Graesser ; Epstein ; Macal and North They are free to interact with other agents, at least over a limited range of situations, and this does not necessarily affect their autonomy.
Groups of agents can exist, but they are spawned from the bottom-up, and are thus amalgamations of similar autonomous individuals. Rational- choice models generally assume that agents are perfectly rational optimisers with unfettered access to information, foresight, and infinite analytical ability Parker et al.
This allows agents to make inductive, discrete, and adaptive choices that move them towards achieving goals. However, agents can also be fixed.
Agents can be designed to alter their state depending on previous states, permitting agents to adapt with a form of memory or learning. Agents can adapt at the individual level e. Often, there are many different types of agents within one simulation.
Agents can be representations of any type of autonomous entity. These could be, for example, people, buildings, cars, land parcels, water droplets or insects. Figure 5. It should be noted that ABM is not the same as object-oriented simulation, although the object-oriented paradigm provides a suitable medium for the development of agent-based models.
A collection of multiple, interacting agents, situated within a model or simulation environment such as represented by the artificial world as shown in Fig.
Here, agents can be representations of animate entities such as humans that can roam freely around an environment or be inanimate, such as a petrol retailer, that has a fixed location but can change state. One rule-set can be applied to all agents or each agent or categories of agents can have its own unique rule set.
For example, the retail petrol agents in Heppenstall et al. Heppenstall maximise profits. However, rules can also be put into action in ignorance of the actions of other agents. More recently there has been a move towards incorporating behavioural frameworks within agent-based models to better represent human behaviour. For example, Malleson et al. This type of work marks a move towards a more sophisticated handling of agent behaviour.
Kennedy provides an overview of different frameworks for handling human behaviour in agent-based models. Agents can interact with each other and amongst themselves and with the environment. Relationships may be specified in a variety of ways, from simply reactive i. The behaviour of agents can be scheduled to take place synchronously i. For example, depending on the space defined for agent interactions, proximity may be defined by spatial distance for continuous space, adjacency for grid cells, or by connectivity in social networks.
Agents within an environment may be spatially explicit, meaning agents have a location in geometrical space, although the agent itself may be static. For example, within a route navigation model, agents would be required to have a specific loca- tion for them to assess their route strategy. Conversely, agents within an environ- ment may be spatially implicit; this means that their location within the environment is irrelevant.
In a modelling context, agent-based models can be used as experimental media for running and observing agent-based simulations. In particular, the roots of ABM are within the simulation of human social behaviour and individual decision-making Bonabeau In this sense ABM has trans- formed social science research by allowing researchers to replicate or generate the emergence of empirically complex social phenomena from a set of relatively simple agent-based rules at the micro-level Balan et al.
This section clarifies the scope of these other techniques and emphasises the distinction from agent-based models. A CA is a discrete dynamic system, the behaviour of which is specified in terms of local rela- tions. The space in a CA system is divided into a lattice or grid of regularly-space cells of the same size and shape, usually square. Each cell has a value either 0 or 1 or on a scale from 0 to 1. The state of a cell and its behaviour is determined by the state of other cells in close proximity at a previous time step, by a set of local rules and by the cell itself Benenson and Torrens ; Torrens ; Wolfram The position of the cells and their neighbourhood rela- tions remain fixed over time.
Unlike agents, CAs cannot have more than one attribute; for example, a cell could be occupied or unoccupied, but the cell could not contain multiple attributes such as building type, date built etc. Both CA and agent-based models, model the complexity of social systems with similar individual level representations.
However, they differ in their emphasis; CA model social dynamics with a focus on the emergence of properties from local inter- actions while agent-based models simulate more complex situations where agents control their own actions based on their knowledge of the environment Birkin and Wu In practice, CA and ABM have often been applied separately to explore a wide variety of geographical phenomena.
This is particularly evident in urban modelling. Heppenstall changes for example, White et al. However, models are increasingly being developed using a combination of CA and ABM techniques to produce flexible and powerful models, and the distinction between them is increasingly becoming blurred.
As with CA and ABM, MSM operates at the level of the individual, is able to simulate the global consequences of local interactions whilst allowing the character- istics of each individual to be tracked over time.
However, crucially in contrast to ABM, MSM only models one-direction interactions: the impact of the policy on the individuals, but not the impact of individuals on the policy and interactions between individuals are not simulated. Birkin and Wu see the relationship between ABM and MSM as compli- mentary; linking the two approaches can help overcomes inherent limitations in both approaches, for example problematic validation in ABM and the absence of real behavioural modelling in MSM.
Examples of the hybridisation of these approaches can be found in the work of Boman and Holm and more recently Wu et al. Here we briefly discuss these approaches describing their advantages and disadvantages.
For a more detailed discussion, the reader is directed to Crooks and Castle While programming from the ground up allows complete control over every aspect of the agent-based model, this can be a time-consuming option unless the researcher is an experienced programmer.
Toolkits do not require substantial coding experience and provide conceptual frameworks and templates that allow the user to design a customised model. These toolkits are often supported by libraries of pre-defined methods and functions that can be easily incorporated into an agent-based model and linked into other software libraries, for example geographical information systems GIS such as OpenMap or GeoTools.
Using a toolkit can greatly reduce the model construction time allowing more time to be dedicated to research. However, drawbacks include a substantial time investment on behalf of the researcher to learn the how to design and implement a model in the toolkit and the programming language the software uses.
After this investment of time, it is possible that the desired functionality is not available. In addition to toolkits, there is a steady increase of available software for con- structing agent-based models. Notable examples include NetLogo and AgentSheets. Utilisation of such software is particularly useful for rapid development of basic or prototype models. The major drawback using software is that researchers are restricted to the design framework supported by the software and maybe unable to extend or integrate additional tools.
These revolve around gaining an understanding and communicating the inner workings of the model but also considerations with respect to verification, calibration and validation of the model itself. It is to these issues that we now turn. Validation is the process of making sure that an implemented model matches the real-world.
Verification is thus as a much a matter of testing the logic of the model through its computer programme as testing its formal logic. It involves checking that the model behaves as expected which is something that is often taken for granted.
Validation relates to the extent that the model adequately represents the system being modelled Casti and in this sense, it involves the goodness-of-fit of the model to data. However, the validity of a model should not be thought of as binary event i. Heppenstall In contrast, calibration involves fine-tuning the model to a particular context and this means establishing a unique set of parameters that dimension the model to its data.
This is not validation per se but calibration can often involve validation because the parameters are often chosen so that performance of the model related to data is optimal in some way, in terms of some criterion of goodness-of-fit, for example. This is a large subject area and suffice it to say, many if not most agent-based models suffer from a lack of uniqueness in parameter estimation due to the fact that their assumptions and processes tend to outweigh the data available for a complete assessment of their goodness-of-fit.
Concerns have been raised pertaining to verification and validation by numerous researchers e. Batty and Torrens ; Crooks et al. Ngo and See present a more detailed discus- sion of how verification, calibration and validation issues can be addressed while Evans raises awareness of error and uncertainty with respect to input data, parameteri- sation, and model form and offers guidance to minimising and understanding such errors.
These issues are only mentioned here to stress to the reader that these are important and need to be considered when working with agent-based models. Some argue that by making models more visual they become more transparent Batty but also by visualising key model processes, helps to convey the model clearly and quickly Kornhauser et al. For example, via the GUI of the model we are able to track the simulation history as advocated by Axelrod Through this we can observe and explain how aggregate outcomes emerge from the local interactions of many individuals.
Moreover, there are also qualitative evaluations of model validity that might be made from visualising outcomes of such models. Patel and Smith provide a review of tools, techniques and methods for such visualizations in the second and third dimensions. Such tools as game engines and virtual worlds see Crooks et al. The dynamic and real-time visualisation and communication options especially those in virtual worlds provided by agent-based models allows us to address the challenge modellers face on how we might com- municate and share agent-based models with all those we seek to influence.
In the past, model results were mainly presented through the discussion of the model outcomes via static charts or screen shoots. However, visualisation alone does not address all the issues relating to the communication of agent-based models. We also need methods to convey the model structure and key model parameters that allow for replication of such models. Replication of models allows others to gain confidence about the model and its under- lying assumptions see Crooks et al.
ABM provides us with tools to explore this change in approach. There are three main claimed advan- tages of the agent-based approach over traditional modelling techniques, such as top-down techniques of non-linear dynamical systems in which related state vari- ables are aggregated e. The agent-based approach: i captures emergent phenomena; ii provides a natural environment for the study of certain systems; and iii is flexible, particularly in relation to the development of geospatial models.
Traditional urban models focused on modelling the system of interest top-down in contrast to model developers who divided the city into a few units, while assuming average behaviour of individuals. Instead agents are often given a representative behaviour; thus we move from average aggregate behaviour to average individual behaviour. Heppenstall an alternative approach.
Bonabeau has identified a non-exhaustive list of conditions where ABMs can be useful for capturing emergent behaviour: 1. Interaction between agents is complicated, non-linear, discontinuous, or discrete i. This can be particularly useful if describing discontinuity of individual behaviour, for example, using differential equations; 2.
The ability to design a heterogeneous population of agents with an agent-based model is significant. Agents can represent any type of unit. Unlike agent-based models, aggregate differential equations tend to smooth out fluctuations. This is important because under certain conditions, fluctuations can be amplified: a system can be linearly stable but susceptible to large perturbations.
Heterogeneity also allows for the specification of agents with varying degrees of rationality. This offers advantages over approaches that assume perfectly rational individuals, if they consider individuals at all; 3. The topology of agent interactions is heterogeneous and complex. Aggregate flow equations usually assume global homogeneous mixing, but the topology of an interaction network can lead to significant deviations from predicted aggregate behavior and, 4.
Agents exhibit complex behaviour, including learning and adaptation. Furthermore, the ability of agent-based models to describe the behaviour and interactions of a system allows for system dynamics to be directly incorporated into the model.
This represents a movement away from the static nature of earlier styles of urban and regional modelling see Batty However, while time in ABMs is still discrete, i. Additionally different processes occur over different time periods, for example, long term economic cycles, daily commuting and hour by hour social interaction.
In many cases, ABM is a natural method for describing and simulating a system composed of real-world entities especially when using object-orientated principles Gilbert and Terna Agent-based simulations provide an opportunity to represent and test social theory which cannot easily be described using mathematical formulae Axelrod The models often map more naturally to the structure of the problem than equation-based models Parunak et al.
The behaviour of individuals cannot clearly be defined through aggregate transition rates e. Individual behaviour is complex. Although hypothetically any process can be explained by an equation, the complexity of differential equations increases exponentially as the complexity of behaviour increases.
Describing complex individual behaviour with equations can therefore become intractable; 3. Activities are arguably a more natural way of describing a system than pro- cesses; and, 4. Agent behaviour is stochastic. Points of randomness can be applied strategically within agent-based models, rather than arbitrarily within aggregate equations. Finally, the agent-based approach to modelling is flexible, particularly in relation to geospatial modelling. Notably, spatial simulations benefit from the mobility that agent-based models offer.
An agent-based model can be defined within any given system environment e. Therefore agent-based models are essentially without scale. It is the phenom- ena of interest which drives the scale to be used, for example, from the micro movement of pedestrians within a building during an evacuation e.
Gwynne et al. Nagel to the study of urban growth e. Brown et al. Additionally as ABM allows for the representation of individual objects, it is therefore possible to combine these objects to represent phenomena at different scales within the same model. Furthermore, agents have the ability to physically move within their environment, in different directions and at different velocities.
Agent mobility makes ABM very flexible in terms of potential variables and parameters that can be specified. Neighbourhoods can also be specified using a variety of mechanisms such as well understood geographical relations such as market catchments areas, travel to work zones, walking distance buffers etc. The implementation of agent interactions can easily be governed by space, networks, or a combination of structures as highlighted in Alam et al.
Significantly, agent-based models can regulate behaviours based on interactions at a specific distance and direction thus allowing for action-at-a-distance.
In addition, agent-based models also provide a robust and flexible framework for tuning the complexity of agents i. Another dimension of flexibility is the ability to adjust levels of description and aggregation. It is easy to experiment with aggregate agents, sub groups of agents, and single agents, with different levels of description coexisting within a model.
Thus, the agent-based approach can be used when the appropriate level of description or complexity is unknown, and finding a suitable level requires exploration. Heppenstall 5. Although common to all modelling techniques, one issue relates to the purpose of the model; a model is only as useful as the purpose for which it is constructed. A model has to be built at the right level of abstraction for every phenomenon, judiciously using the right amount of detail for the model to serve its purpose Couclelis If the level of abstraction is too simple, one may miss the key variables.
Too much detail, and the model will have too many constraints and become overly complicated. Abdou et al. This remains an art more than a science Axelrod Axtell and Epstein provide practical guidelines for the evaluation of model performance depending on the level of model abstraction. The nature of the system being modelled is another consideration. For example, a system based on human beings will involve agents with potentially irrational behaviour, subjective choices, and complex psychology see Kennedy , for an overview of how behavioural frameworks can be implemented in agent-based models.
When combined with active exploration using Uri Wilensky's free and widely used NetLogo programming environment, reading this book equips students and researchers with a new language for generating and expressing scientific theories. A clear, comprehensive, and up-to-date introduction.
This is the best book out there for learning or teaching the art and science of agent-based modeling. I highly recommend it for anyone interested in this essential area of complex systems science.
With this Introduction to Agent-Based Modeling , he and William Rand have set the standard for textbooks on this topic. An essential contribution. Olivier Boissier , Rafael H. Search Search. Search Advanced Search close Close. Request Permissions Exam copy.
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