INFORMATION DYNAMICS: PREMISES, CHALLENGES AND RESULTS

In various fields of contemporary research information and dynamics are becoming the key terms. Theoretic information reasoning is well known in physics, especially in thermodynamics where the relationship between the statistical (or, informational) entropy of the system and its thermodynamical entropy have been studied since a long time. Information theory is especially relevant to data processing and statistical inference. Generally speaking, the apparatus of information theory is applicable to any probabilistic system of observations since whenever we make statistical observations (or design and conduct statistical experiments) we seek information. When the language of information theory (the concepts of entropy, mutual information between random variables and processes, information rate, maximum entropy formalism, information flow, etc.) is used in connection with system dynamics we come to the notion of information dynamics. The objective of this report is to show a potential of the basic information theoretic methodology for the analysis of various problems of system dynamics. In particular, we wish to indicate some challenges and expound our recent results on the maximum information entropy approach to the analysis of stochastic dynamical systems.