In this paper we argue that many economic dynamical systems naturally become fuzzy due to the uncertain initial conditions and parameters. The Fuzzy Economics is defined as human-centric and closer to the reality Economics with fuzzy-logic based representation of the economic agent's behavior. We use linguistic rule-base and fuzzy differential equations for modeling the economic agents. Using fuzzy agents this paper investigates different problems of fuzzy economics and methods of their solutions, namely, time path and stability of the fuzzy dynamic economic systems, fuzzy decision making in oligopolistic economy, neuron-fuzzy time-series forecasting of macroeconomic processes. The suggested approaches to fuzzy macroeconomic optimization and control and prediction problems are successfully applied to path planning and stability analysis of national economy, economic growth, nonlinear manufacture dynamics and portfolio construction.