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3 Types of Simulation 29

reapplied for a number of simulated time periods. In traditional micro-simulation,
the behaviour of each individual is regarded as a “black box”. The behaviour is
modelled in terms of probabilities, and no attempt is made to justify these in terms
of individual preferences, decisions, plans, etc. Thus, better results may be gained if
also the cognitive processes of the individuals were simulated.
Opening the black box of individual decision-making can be done in several
ways. A basic and common approach is to use decision rules, for instance, in the
form of a set of situation-action rules: If an individual and/or the environment is
in state X, then the individual will perform action Y. By combining decision rules
and the BDI model quite sophisticated behaviour can be modelled. Other models
of individual cognition used in agent-based social simulation include the use of
Soar, a computer implementation of Allen Newell’s unified theory of cognition
(Newell 1994), which was used in Steve (discussed above). Another unified theory
of individual cognition, for which a computer implementation exists, is ACT-R
(Anderson et al. 2004), which is realized as a production system. A less general
example is the Consumat model (Janssen and Jager 1999), a meta-model combining
several psychological theories on decision-making in a consumer situation. In
addition, nonsymbolic approaches such as neural networks have been used to model
the agents’ decision-making (Massaguer et al. 2006).
As we have seen, the behaviour of individuals could be either deterministic or
stochastic. Also, the basis for the behaviour of the individuals may vary. We can
identify the following categories:

– The state of the individual itself: In most social simulation models, the physical
and/or mental state of an individual plays an important role in determining its
behaviour.
– The state of the environment: The state of the environment surrounding the
individual often influences the behaviour of an individual. Thus, an individual
may act differently in different contexts although its physical and mental state is
the same.
– The state of other individuals: One popular type of simulation model, where the
behaviour of individuals is (solely) based on the state of other individuals, is
those using cellular automata (Schiff 2008). Such a simulation model consists of
a grid of cells representing individuals, each in one of a finite number of states.
Time is discrete and the state of a cell at time t is a function of the states of a finite
number of cells (called its neighbourhood) at time t 1. These neighbours are a
fixed selection of cells relative to the specified cell. Every cell has the same rule
for updating, based on the values in its neighbourhood. Each time the rules are
applied to the whole grid, a new generation is created. In this case, information
about the state of other individuals can be seen as gained through observations.
Another possibility to gain this information is through communication, and in
this case, the individuals do not have to be limited to the neighbours.
– Social states (norms,etc.) as viewed by the agent: For simulation of social
behaviour, the agents need to be equipped with mechanisms for reasoning at
the social level (unless the social level is regarded as emergent from individual