DNSS points, also known as Skiba points, arise in optimal control problems that exhibit multiple optimal solutions. A DNSS pointnamed alphabetically after Deckert and Nishimura, Sethi, and Skibais an indifference point in an optimal control problem such that starting from such a point, the problem has more than one different optimal solutions. A good discussion of such points can be found in Grasset al.
Definition
Of particular interest here are discounted infinite horizon optimal control problems that are autonomous. These problems can be formulated as s.t. where is the discount rate, and are the state and control variables, respectively, at time, functions and are assumed to be continuously differentiablewith respect to their arguments and they do not depend explicitly on time, and is the set of feasible controls and it also is explicitly independent of time. Furthermore, it is assumed that the integral converges for any admissible solution. In such a problem with one-dimensional state variable, the initial state is called a DNSS point if the system starting from it exhibits multiple optimal solutions or equilibria. Thus, at least in the neighborhood of, the system moves to one equilibrium for and to another for. In this sense, is an indifference point from which the system could move to either of the two equilibria. For two-dimensional optimal control problems, Grass et al. and Zeiler et al. present examples that exhibit DNSS curves. Some references on the application of DNSS points are Caulkins et al. and Zeiler et al.
History
identified such indifference points for the first time in 1977. Further, Skiba, Sethi, and Deckert and Nishimura explored these indifference points in economic models. The term DNSS points, introduced by Grass et al., recognizes the contributions of these authors. These indifference points have been referred to earlier as Skiba points or DNS points in the literature.
Example
A simple problem exhibiting this behavior is given by and. It is shown in Grass et al. that is a DNSS point for this problem because the optimal path can be either or. Note that for, the optimal path is and for, the optimal path is.
Extensions
For further details and extensions, the reader is referred to Grass et al.