In this section we would like to discuss the problem of novelty creation in computational systems in general and ABMs in particular.
The notion of novelty is a very difficult one. What is genuinely novel and what is just a different manifestation of something old is often hard to decide and the qualification depends largely on personal experience, insight and knowledge. To some degree, novelty is in the eye of the beholder, because something can only be novel relative to something or somebody. We shall not therefore pretend to be able to explicitly write down conditions under which we would accept a phenomenon (in the real or artificial worlds) as novel.
Of course, in a philosophical sense, it is sometimes contested whether there is ``real'' novelty at all in the world. But insofar as we are willing to stipulate that some interesting form of novelty does arise in nature, and continues to do so on an ongoing basis, and that this is exemplified in the phenomena of life itself, then it is reasonable to look for artificial or synthetic systems that can be at least comparably creative.
As a starting point, we might say that a ``novel'' state is reached whenever a system moves to a new (not previously occupied) point in its micro-state phase space. A slightly stronger form of novelty would be when the micro-state phase space itself changes--due to the creation or destruction of agents. However, given that, as we have already noted, each micro-state completely entails the next micro-state we can hardly identify anything expressed purely in these terms as ``novel'' in our sense.
Thus, for example, a trivial ABM might consist of a single agent whose only behaviour is to create new agents of the same class. This would result in a system with a constantly (indeed, exponentially) expanding--and thus ``novel''--micro-state phase space; but we would clearly want to exclude this as an interesting or substantive example of the creation of novelty. Indeed, we might say that this is precisely the source for the intuition that computer models cannot, in general, exhibit novelty of any worthwhile kind.
Having said this, it seems that novelty should be looked for on some meso- or macro-scale. The attentive reader will already have noticed the somewhat paradoxical situation we find ourselves in. On the one hand, in some sense, the micro-state of the model contains all there is to know about the model's state; but on the other hand this undigested information is not adequate to judge whether something interestingly novel has happened in the system. In order to decide this we must first employ the selective analysis rule(s) which leads us to the macro-scale. It is clear that novelty at the micro-scale does not translate necessarily into a novel macro-state or macroscopic behaviour; depending on the rules which leads from micro-state to macro-state, the latter might even be completely unaffected by ``novelty'' of the former. To put it another way, in the absence of the rules that lead from micro-state to macro-state it is impossible to decide whether a certain novel micro-state is also macroscopically novel. Novelty creation on the macro-scale thus appears to depend as much on the micro/macro relationship as on the micro-state itself.
Another possible approach to the question of novelty focuses on the issue of deterministic versus stochastic dynamics. That is to say, the unsatisfying or uncreative nature of deterministic computational models is attributed precisely to the fact that the micro-state completely or uniquely determines the next (micro-)state (and thus, given the analysis rule, of the macro-state). Of course, the opposite extreme would be a ``completely'' stochastic dynamics--where there is no correlation between current state and next state at all. Such a model will then, at each time-step, take a completely random position in phase space. No doubt each microstate is then ``novel'', in the sense of ``unpredictable''; but again, this is hardly an any more interesting form of novelty. Such a random state trajectory has--with overwhelming probability--no interesting intrinsic regularity; neither would there be a sensible analysis rules which could lead to interesting, (in the sense that it would not teach us anything interesting about the world) macro behaviour. We will therefore exclude this trivial kind of complete stochastic variation as an example of genuine novelty. But this still leaves us with the possibility of novelty generation in the intermediate case of deterministic dynamics leavened with some stochastic elements.
ABMs do often combine deterministic rules with stochastic elements; amongst other things, this allows the implementation of evolutionary models. Many will be intuitively inclined to regard some of those systems as novelty producing. However a closer look suggests that, if novelty is produced at all, it is rather strictly limited; see in this context, for example, (Bedau & Brown, 1997; Bedau et al., 1997).
A first hint in this direction is the fact that repetitive runs of the system, with the same initial conditions, but different pseudo-random seeds typically lead to qualitatively similar behaviours.3For example in the Tierra system (Ray, 1996), starting with the original ancestor organism, the phenomena of ``parasites'' and ``hyperparasites'' will essentially always emerge. Furthermore, very often the analogue is true for a variation of the initial conditions. This indicates already in those examples that it is not really the randomness that introduces the putatively novel phenomena into the system; the stochastic elements only change details of the occurrence of these phenomena. If the randomness really were the source of novelty, then a different seed of the pseudo-random number generator should presumably (nearly always) give rise to qualitatively different behaviour of the model.
Thus it seems that in most ALife systems the stochastic elements fulfill the function of perturbation from an otherwise deterministic attractor of the microscopic dynamics--potentially allowing ongoing transitions between these attractors, which would not be possible in a purely deterministic system. Transitions to (previously unobserved) attractors may then be identified with ``novelty''. But the randomness is not in itself the source of novelty. It is merely helping to make manifest the variety in the potential attractors of the underlying deterministic dynamics; which is to say, establishing a long term probability distribution over the occupancy of these attractors. In Karl Popper's words, we must be inclined to conclude that ``...indeterminism is not enough'' (Popper, 1973).
Of course, arguments for and against novelty-creation in existing ALife systems can go on forever. In order to avoid lengthy and unfruitful discussions, we may suggest an additional, albeit very informal, criterion: that novelty should be produced perpetually. In practice perpetual novelty creation does not need to mean eternal novelty creation, but only that the macroscopic behaviour does not quickly settle on one or a few attractors, but goes on exhibiting novelty for much longer than the typical simulation-time.
In the following sections we will look at the issue of novelty more concretely by considering two specific categories of ABM, featuring what we shall call closed versus open agents. There is not a sharp demarcation between these: but, roughly speaking, closed agents have a relatively small variety of pre-conceived (and pre-programmed) behaviours; whereas open agents can explore an indefinitely large variety of behaviours, which are not pre-conceived by the designer.
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