  We investigate the generation of new concepts from combinations of properties
as an artificial language develops. To do so, we have developed a new framework
for conjunctive concept combination. This framework gives a semantic grounding
to the weighted sum approach to concept combination seen in the literature. We
implement the framework in a multi-agent simulation of language evolution and
show that shared combination weights emerge. The expected value and the
variance of these weights across agents may be predicted from the distribution
of elements in the conceptual space, as determined by the underlying
environment, together with the rate at which agents adopt others' concepts.
When this rate is smaller, the agents are able to converge to weights with
lower variance. However, the time taken to converge to a steady state
distribution of weights is longer.
