Information Technology Reference
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a growing stockpile of passive resources, but instead join the process as secondary
agents (Gell
1998
). Put another way, when culture is seen as a creative system, it
is inclusive of all of these objects, which contribute functionally to the system, just
as the distributed modular mechanisms of the human brain may be required to be
in place for individual creative cognition to function. Similarly, a memetic view of
culture sees ideas, concepts, designs and so on as not just cumulative but effective,
a new form of evolutionary
raison d'être
added to the biological world (Dawkins
1976
). These “memes” are understood as having an emergent teleological function-
ality: brains evolved under selective pressures, and memes were thus unleashed.
But what are memes for? The answer: they are only for memes. Sperber (
2007
)
provides a strong argument for rejecting memes, but promotes the idea of distin-
guishing between a designated function (a function we ascribe to something) and
a teleofunction (a function of something in an evolutionary sense, in service of its
own survival).
Just as neuroscientists care about the behaviour of synapses as much as they
do neurons, a theory of social creativity depends on the functional importance of
both primary and secondary agents. We can view any digital arts tool as a sec-
ondary agent, but arts-based computational creativity holds the promise of intro-
ducing secondary agents that are richly interactive, and as such creatively potent (if
not adaptively creative), encroaching on the territory of primary agents. Arts-based
computational creativity researchers, by definition, study the possibility of artefacts
with agency, and in doing so reveal a gradient of agency rather than a categorical
division.
The
Interactive Genetic Algorithm
(IGA) (Dawkins
1986
), for example, is an
artificial evolutionary system in which a user selectively “breeds” aesthetic artefacts
of some sort (see Takagi
2001
for a survey), or manipulates an evolutionary outcome
via a user-defined fitness function (e.g. Sims
1994
, Bentley
1999a
). The IGA can
only possibly achieve adaptive creativity by being coupled with a human user, in
a generate-and-test cycle. However, it allows the user to explore new patterns or
behaviours beyond those he would have devised using imagination or existing forms
of experimentation (Bentley
1999b
). As such, it is not autonomous, and yet it is
active and participatory, grounded in an external system of value through a human
user.
Researchers in IGAs continue to struggle to find powerful genetic representations
of aesthetic patterns and behaviour that could lead to interesting creative discovery
(e.g. Stanley and Miikkulainen
2004
). But more recently, IGAs have also be used to
couple multiple users together in a shared distributed search (Secretan et al.
2008
).
Whilst an individual approach views IGAs as creative tools that extend individual
cognition, as in the extended mind model, the distributed notion of an IGA embodies
a social view in which no one mind can be seen as the centre of an artificially
extended system. Instead, minds and machines form a heterogeneous network of
interaction, forcing us to view this hybrid human and artificial evolutionary system
on a social level. In this and other areas, arts-based computational creativity is well-
poised to bootstrap its future development on the emergence of social computing,
which presents a training and evaluation environment on the scale of a real human
social system.
