Evaluation Method for Strategic Investments

Real Options Analysis (ROA) has become a complimentary tool for engineering economics. It has become popular due to the limitations of conventional engineering valuation methods; specifically, the assumptions of uncertainty. Industry is seeking to quantify the value of engineering investments with uncertainty. One problem with conventional tools are that they may assume that cash flows are certain, therefore minimizing the possibility of the uncertainty of future values. Real options analysis provides a solution to this problem, but has been used sparingly by practitioners. This paper seeks to provide a new model, referred to as the Beta Distribution Real Options Pricing Model (BDROP), which addresses these limitations and can be easily used by practitioners. The positive attributes of this new model include unconstrained market assumptions, robust representation of the underlying asset‟s uncertainty, and an uncomplicated methodology. This research demonstrates the use of the model to evaluate the use of automation for inventory control.

Accounting for uncertainty in ecological analysis: the strengths and limitations of hierarchical statistical modeling

Analyses of ecological data should account for the uncertainty in the process(es) that generated the data. However, accounting for these uncertainties is a difficult task, since ecology is known for its complexity. Measurement and/or process errors are often the only sources of uncertainty modeled when addressing complex ecological problems, yet analyses should also account for uncertainty in sampling design, in model specification, in parameters governing the specified model, and in initial and boundary conditions. Only then can we be confident in the scientific inferences and forecasts made from an analysis. Probability and statistics provide a framework that accounts for multiple sources of uncertainty. Given the complexities of ecological studies, the hierarchical statistical model is an invaluable tool. This approach is not new in ecology, and there are many examples (both Bayesian and non-Bayesian) in the literature illustrating the benefits of this approach. In this article, we provide a baseline for concepts, notation, and methods, from which discussion on hierarchical statistical modeling in ecology can proceed. We have also planted some seeds for discussion and tried to show where the practical difficulties lie. Our thesis is that hierarchical statistical modeling is a powerful way of approaching ecological analysis in the presence of inevitable but quantifiable uncertainties, even if practical issues sometimes require pragmatic compromises.

Macroecology of Caribbean Bats: Effects of Area, Elevation, Latitude, and Hurricane-Induced Disturbance

Understanding the geographic and environmental characteristics of islands that affect aspects of biodiversity is a major theme in ecology (Begon et al. 2006; Krebs 2001) and biogeography (Cox and Moore 2000; Drakare et al. 2006; Lomolino et al. 2006). Such understanding has become particularly relevant over the past century because human activities on continents have fragmented natural landscapes, often creating islands of isolated habitat dispersed within a sea of land uses that include agriculture, forestry, and various degrees of urban and suburban development. The increasingly fragmented or islandlike structure of mainland habitats has critical ramifications to conservation biology, as it provides insights regarding the mechanisms leading to species persistence and loss. Consequently, the study of patterns and mechanisms associated with island biodiversity is of interest in its own right (Whittaker 1998; Williamson 1981), and may provide critical insights into mainland phenomena that otherwise could not be studied because of ethical, financial, or logistical considerations involved with the execution of large-scale manipulative experiments.