  Communication of information in complex systems can be considered as major
driver of systems evolution. What matters is not the communicated information
by itself but rather the meaning that is supplied to the information. However
informational exchange in a system of heterogenious agents, which code and
decode information with different meaning processing structures, is more
complex than simple input-output model. The structural difference of coding and
decoding algorithms in a system of three or more groups of agents, entertaining
different sets of communication codes,provide a source of additional options
which has an impact on system's dynamics. The mechanisms of meaning and
information processing can be evaluated analytically ion a model framework. The
results show that model predictions acccurately fit empirically observed data
in systems of different origions.
