ABSTRACT
In deterministic treatments of uncertainties in mathematical models which are dynamical systems, information about the uncertain elements may be of two kinds; the analyst knows some functional properties as well as the possible sizes of the uncertain elements, or author may assume only some of their functional properties. In order to predict or control the behaviour of a system in the 'real' world, be it physical, biological or socio-economic, the system analyst seeks to capture its salient features in an abstraction, a mathematical model. Mathematical model always contains elements which are uncertain. These uncertain elements may be parameters, constant or time-varying, which are unknown or imperfectly known, or they may be unknown or imperfectly known inputs into the system. The author describes a stochastic approach in which information about the uncertain elements as well as about the system behaviour is in statistical terms; loosely speaking, he is content with desirable behaviour on the average.
