Deterministic versus stochastic sensitivity analysis in investment problems: An environmental case study

Deterministic versus stochastic sensitivity analysis in investment problems: An environmental case study

Sensitivity analysis in investment problems is an important tool to determine which factors can jeopardize the future of the investment. Information on the probability distribution of those factors that affect the investment is mostly lacking. In those situations the analysts have two options: (i) apply a method that does not require knowledge of that distribution, or (ii) make assumptions abou...

Varinace-Based Sensitivity Analysis of Deterministic Model

‎The study of many scientific and natural phenomena in laboratory condition is sometimes impossible‎, ‎therefore theire expresed by mathemathical models and simulated by complex computer models (codes)‎. ‎Running a computer model with different inputs is called a computer expriment‎. ‎Statistical issues allocated a wide range of applications for computer expriment to itself‎. ‎In this paper‎, ‎...

Evolutionary Multi-objective Environmental/Economic Dispatch: Stochastic Versus Deterministic Approaches

Due to the environmental concerns that arise from the emissions produced by fossil-fueled electric power plants, the classical economic dispatch, which operates electric power systems so as to minimize only the total fuel cost, can no longer be considered alone. Thus, by environmental dispatch, emissions can be reduced by dispatch of power generation to minimize emissions. The environmental/eco...

Deterministic Versus Stochastic Explanations

Oxygen isotope data for a Pliocene interval from 3.6 to 2.8 million years ago show a mean increase (0.5 per mil) of benthic 6180 at about 3.2 million years ago, whereas planktic 6180 does not increase. This lack of covariance indicates that the event at 3.2 million years did not result in a permanent increase in the ice budget of either the Northern or the Southern Hemisphere. The history of co...

Stochastic versus Deterministic Approaches