Optimization In Economics And Finance

Many optimization questions arise in economics and finance; an important example of this is the society`s choice of the optimum state of the economy (the social choice problem). Optimization in Economics and Finance extends and improves the usual optimization techniques, in a form that may be adopted for modeling social choice problems. Problems discussed include eg., when is an optimum reached; when is it unique; relaxation of the conventional convex (or concave) assumptions on an economic model; associated mathematical concepts such as invex and quasimax; multiobjectve optimal control models, and related computational methods and programs. These techniques are applied to economic growth models (including small stochastic perturbations), finance and financial investment models (and the interaction between financial and production variables,) modeling sustainability over long time horizons, boundary (transversality) conditions, and models with several conflicting objectives. Although the applications are general and illustrative, the models in this book provide examples of possible models for a society`s social choice for an allocation that maximizes welfare and utilization of resources. As well as using existing computer programs for optimization of models, a new computer program, named SCOM, is presented in this book for computing social models by optimal control. ``Craven and Islam have produced an excellent survey of recent applications of optimization to economics (especially growth theory) and finance. The exposition is clear, with good notation and a judicious mix of rigor and heuristic argument. As a bonus, they present a new SCOM program to compute solutions to social choice models. This book will surely prove to be a boon to the student, especially those who wish to learn about optimal growth under different conditions and assumptions. The text covers a wide range of optimization models in economics and finance, including non-linear programming, dynamic optimization, stochastic control and dynamic vector optimization models. The models are applicable to decision-making, forecasting, simulation, sensitivity analysis and planning.``