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.

翻译：复杂系统中的信息通信可被视为系统演化的主要驱动力。重要的并非信息本身，而是被赋予信息的含义。然而，在由具有不同含义处理结构的智能体对信息进行编码和解码的异构智能体系统中，信息交换比简单的输入-输出模型更为复杂。在包含三组或更多组智能体的系统中，这些智能体采用不同的通信编码集，其编码与解码算法的结构差异提供了额外的选择来源，进而对系统动力学产生影响。含义与信息处理的机制可在模型框架内进行解析性评估。结果表明，模型预测能够准确拟合来自不同起源系统的经验观测数据。