  Barring swarm robotics, a substantial share of current machine-human and
machine-machine learning and interaction mechanisms are being developed and fed
by results of agent-based computer simulations, game-theoretic models, or
robotic experiments based on a dyadic communication pattern. Yet, in real life,
humans no less frequently communicate in groups, and gain knowledge and take
decisions basing on information cumulatively gleaned from more than one single
source. These properties should be taken into consideration in the design of
autonomous artificial cognitive systems construed to interact with learn from
more than one contact or 'neighbour'. To this end, significant practical import
can be gleaned from research applying strict science methodology to human and
social phenomena, e.g. to discovery of realistic creativity potential spans, or
the 'exposure thresholds' after which new information could be accepted by a
cognitive agent. The results will be presented of a project analysing the
social propagation of neologisms in a microblogging service. From local,
low-level interactions and information flows between agents inventing and
imitating discrete lexemes we aim to describe the processes of the emergence of
more global systemic order and dynamics, using the latest methods of complexity
science. Whether in order to mimic them, or to 'enhance' them, parameters
gleaned from complexity science approaches to humans' social and humanistic
behaviour should subsequently be incorporated as points of reference in the
field of robotics and human-machine interaction.
