4 ABMs with Closed Agents

We will first investigate the potential for novelty creation in those ``classical'' ABMs having closed agents.

As already outlined, this might be typically implemented according to the scheme depicted in figure 1. Its basic underlying idea is clear: there is a central scheduler which invokes the update() method for each individual agent, according to some scheduling algorithm. In each case, the update() method invokes other appropriate methods (on the agent itself or on other agents in its neighborhood) in order to implement the agents' behavioural rules. Where the agents are thought of as modelling organisms then these behaviours may include such things as ``movement'', ``feeding'', ``reproduction'', ``death'' etc. The distinct ``types'' of agents correspond to different classes. The repertoire of classes is, however, fixed, and usually comparatively small.

**Figure 1:** Schematic outline of a typical ABM with ``closed'' agents. The agents' `update()` methods are called according to a schedule. The update usually consist of a series of more basic actions to be taken, such as movement, uptake of food and reproduction and death. How exactly these actions are performed and under which conditions they are called may be variable--within some pre-defined limits--which enables evolutionary agent adaptation.
$\includegraphics[width=0.45\textwidth]{classstruct.eps}$

By definition, a closed agent can only perform those tasks, or exhibit those behaviours, that are pre-coded in its methods. Consequently, any variations in agent behaviour in the course of model execution have to have been preconceived, at some level, by the programmer. The available classes (which is say the class methods) are not subject to variation.

Variability is firstly limited to varying populations of the particular set of implemented classes; and within the instances of a particular class, variability is limited to state variations--i.e., of the values of the state variables. However, it is common in ALife models to partition the state variables between some which can vary during the normal, somatic life-time of an agent; and others which vary only when new agents are being created (i.e., at reproduction). The latter may be regarded as behavioural parameters or, in biological terms, represent agent genotype.

In this category of model then, the scope of evolution is either to select among the specific, pre-programmed, classes (if these come into darwinian competition) or to select among lineages within those classes created by parameter variation (``mutation'').

This can, of course, provide useful and interesting tests of selectional conjectures; but insofar as the whole scheme boils down to the exploration of pre-determined variants of a small, fixed, set of agent classes, it seems it cannot qualify as generating novelty in our sense--at least, not at the level of the individual agents. It remains conceivable that genuinely novel phenomena may arise at some more macroscopic, collective, level, involving groups (colonies?) of agents. We shall see later (section Novelty in Artificial Chemistries) that this concept of novelty creation at the level of the collective behaviour of multiple agents--in the absence of novelty creation at the level of agents themselves--is a key motivation for a particular form of ABM known as an Artificial Chemistry (AC).