This compact and original exposition of optimal control theory and applications is designed for graduate and advanced undergraduate students in economics. It presents a new elementary yet rigorous proof of the maximum principle and a new way of applying the principle that will enable students to solve any one-dimensional problem routinely. Its unified framework illuminates many famous economic examples and models.

This work also emphasizes the connection between optimal control theory and the classical themes of capital theory. It offers a fresh approach to fundamental questions such as: What is income? How should it be measured? What is its relation to wealth?

The book will be valuable to students who want to formulate and solve dynamic allocation problems. It will also be of interest to any economist who wants to understand results of the latest research on the relationship between comprehensive income accounting and wealth or welfare.

This impressive collection features Richard Herrnstein's most important and original contributions to the social and behavioral sciences--his papers on choice behavior in animals and humans and on his discovery and elucidation of a general principle of choice called the matching law.

In recent years, the most popular theory of choice behavior has been rational choice theory. Developed and elaborated by economists over the past hundred years, it claims that individuals make choices in such a way as to maximize their well-being or utility under whatever constraints they face; that is, people make the best of their situations. Rational choice theory holds undisputed sway in economics, and has become an important explanatory framework in political science, sociology, and psychology. Nevertheless, its empirical support is thin.

The matching law is perhaps the most important competing explanatory account of choice behavior. It views choice not as a single event or an internal process of the organism but as a rate of observable events over time. It states that instead of maximizing utility, the organism allocates its behavior over various activities in exact proportion to the value derived from each activity. It differs subtly but significantly from rational choice theory in its predictions of how people exert self-control, for example, how they decide whether to forgo immediate pleasures for larger but delayed rewards. It provides, through the primrose path hypothesis, a powerful explanation of alcohol and narcotic addiction. It can also be used to explain biological phenomena, such as genetic selection and foraging behavior, as well as economic decision making.

Nonlinear Optimization in Electrical Engineering with Applications in MATLAB® provides an introductory course on nonlinear optimization in electrical engineering, with a focus on applications such as the design of electric, microwave, and photonic circuits, wireless communications, and digital filter design.

This book gives an exposition of recently developed approximate dynamic programming (ADP) techniques for decision and control in human engineered systems. ADP is a reinforcement machine learning technique that is motivated by learning mechanisms in biological and animal systems. It is connected from a theoretical point of view with both adaptive control and optimal control methods. The book shows how ADP can be used to design a family of adaptive optimal control algorithms that converge in real-time to optimal control solutions by measuring data along the system trajectories. Generally, in the current literature adaptive controllers and optimal controllers are two distinct methods for the design of automatic control systems. Traditional adaptive controllers learn online in real time how to control systems, but do not yield optimal performance. On the other hand, traditional optimal controllers must be designed offline using full knowledge of the systems dynamics. It is also shown how to use ADP methods to solve multi-player differential games online. Differential games have been shown to be important in H-infinity robust control for disturbance rejection, and in coordinating activities among multiple agents in networked teams. The focus of this book is on continuous-time systems, whose dynamical models can be derived directly from physical principles based on Hamiltonian or Lagrangian dynamics.