Communication of information in systems of heterogeneous agents and systems’ dynamics

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 heterogeneous agents, i.e. agents which code and decode information with different meaning processing structures, or, in other words, which entertain different sets of communication codes, is more complex than simple input–output model. The structural differences 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 in a framework of the model, based on the use of a non-linear evolutionary equation. The results suggest that model predictions significantly correlate with empirically observed data in systems of different origins.