Components

5.1.1.1 Agents

An agent represents an actor or decision-making unit that is central to the topic or issue we are interested in. For example, an agent could be an individual person, a

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household, a firm or organisation, a government department or regulator, or a state.

In theory, any entity which we believe is important to the social system we wish to model, that can be reasonably assumed to have some decision-making rules, and that we can capture in computer code, can be represented as an agent. A model can have any number of agents (subject to computer processing power) we wish to include, though we would typically expect more than one agent to be present in a model.

Agents are autonomous, that is to say they have their own behaviour or decision-making rules. These micro-level rules will represent our ideas, or observations of how the agent behaves in the social system. For example, in the SWAP model, the agents are farmer households, making decisions about whether to adopt SWC or not. An-other example of an agent might be a voter, deciding which party to vote for in an election. The complexity of these rules can vary widely. Agents may have simple rules based on rationality and utility maximisation; for example, a consumer choos-ing between two products based on microeconomic theory. Other models use rules which make social factors key; for example agents may copy others, or follow pre-vailing norms. Agents may also have behaviour rules which allow them to adapt, or learn based on the outcomes of their previous decisions and the resulting outcomes.

The decision of which architecture to use is based on the purpose of the model, be-liefs about the nature of real-world actors’ decisions, and/or practical and technical considerations.

Central to the use of ABM, is the idea that agents interact. They interact with each other, and with their environment. This can be implemented in different ways, but typically involves agents passing information between each other, affecting each oth-ers perception of the world, directly affecting othoth-ers’ behaviour rules, or making changes to the environment. These interactions are one of the attributes that distinguishes ABM from other modelling approaches.

A third defining characteristic of agents in ABMs is that they are typically hetero-genous. Agents will normally have various attributes or parameters that affect their behaviour rules. For example, an agent that represents a household may have attrib-utes such as, the number of people in the household, income, education levels etc.

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ABM allows us to create agents with different levels of these attributes to reflect our beliefs about the distributions of these attributes in the real-world. The attributes are then typically used in the behaviour rules of agents.

When we define and implement agents’ behaviours and their attributes, we may use theories, real-world data, or a combination of both to underpin our implementation.

Using theory to underpin these micro-level rules is common (Berger and Schreinemach-ers,2006;Valbuena et al.,2008) and means the results of the model can be used to help us develop our understanding of these theories. Using real-world data is potentially more valid, but relies on intensive data collection (particularly for behaviour rules) (Valbuena et al.,2008).

5.1.1.2 The Environment

Once we have specified our agents and their rules, they are placed within an environ-ment. The environment can be used in multiple ways. Firstly, it may not have signi-ficant meaning; agents will not interact with it, or move about it. Alternatively, it may represent a conceptual space, for example a social space, where agents close to one an-other are akin to friends or have similar beliefs, and agents far apart are strangers or have differing beliefs. Thirdly, the environment may represent a real physical space, in which agents may move about.

The environment is typically represented by a grid of cells, similar to a chess-board, Each cell may have attributes, in a similar manner as agents, that affect their inter-action with agents. For example, in the SWAP model, each cell has a ‘soil quality’

attribute, which affects the agent’s decision about the need for conservation on that cell. Attributes may diffuse around the environment, if the real-world system exhibits such behaviour (e.g., pollution). As with agents, we may wish to use real-world data to initialise cells’ attributes. This may be in the form of levels of attributes, or can be in the use of GIS data to set up an environment in a spatial pattern equivalent to that in the real-world system.

5.1.1.3 Running the Model

Once the agents and the environment have been specified, the model is typically ready to run. A run of the model iterates the behaviour rules of agents over multiple time

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steps. The model designer will have to decide for how long the model may run, and how many decisions are to be made within each time step. Typically, agents will all make one cycle of their behaviour rule in one time-step, in a randomised order, if not concurrently.

5.1.1.4 Outputs

After a model has been run, or typically, run multiple times, we can gather outputs.

Whilst it is possible to record the ‘histories’ of individual agents, it is much more common to focus on the aggregate or macro-level results of the model. Among these we normally hope to see some emergent result or phenomena. There is much debate around emergence, and no definition is universally accepted or agreed upon (Salgado, 2012).Parker et al.(2003, p. 323) defines emergent results as “aggregate outcomes that cannot be predicted by examining the elements of the system in isolation”. Epstein and Axtell(1995, p. 6) offer a marginally more inclusive definition of emergence being characterised by “organization into recognizable macroscopic social patterns”. These two definitions give a good sense of the way in which emergence is conceived in this thesis. An intuitive example of a macro-level emergent phenomenon is a traffic-jam. A traffic-jam cannot be described easily by examining the behaviour of one driver alone, but is clearly a result of many drivers interacting.

These outputs may be of interest either as a one-off measure at the end of the model-ling time-frame, or as a trend over time, within the modelmodel-ling time-frame. The outputs of a model are normally gathered over multiple runs. Results tend to vary from run to run by varying degrees (owing to the model representing a complex system), and so multiple runs are used to generate average results, with associated confidence inter-vals. Experiments may be set up to find averages over several different initialisations of the model. For example, parameters of interest may be changed systematically to explore their effect on the outputs. By combining repeats of the model, with different setups, the number of runs required in an experiment can become very large.