@RISK Helps Keep Endangered Fish
The Devils Hole pupfish (Cyprinodon diabolis) is one of the world’s most endangered animals. There is only one wild population living in a single aquifer-fed thermal pool in Nye County, Nevada, and has been perched on the brink of extinction with a population of only 35 to 68 fish in 2013. A major strategy for conserving the pupfish has been to create additional captive populations, but scientists needed to know how to best extract wild pupfish for breeding purposes without unduly accelerating the extinction risk for the population in Devils Hole. Dr. Steven Beissinger, Professor of Conservation Biology at the University of California, Berkeley, constructed a population variability analysis (PVA) using @RISK to better inform this dilemma.
Dr. Beissinger first created models for extinction risk in the wild for the pupfish, which showed that most simulated populations of the pupfish became extinct within 50 years. Median and mean time to extinction were 26 and 27 years, and 17 and 22 years, respectively. Next, to evaluate the effects of different strategies for removing individuals to initiate a captive breeding program on the wild population, Dr. Beissinger modeled the effects of removing different numbers of individuals (0–14) at the start of each simulated year.
Dr. Beissinger then wanted to answer the question of which life-stage should pupfish be harvested. The model showed that removing pupfish eggs had the least effect on the wild population. Indeed, Dr. Beissinger calculated that removing 25 eggs for captive breeding is equivalent to removing a single adult in terms of its influence on population dynamics.
This modeling work has helped to inform decisions made by the U.S. Fish and Wildlife Service to conserve the Devils Hole pupfish, which has now begun to remove pupfish eggs from Devils Hole to start a captive population in its state-of-the-art breeding facility. “The modeling helped everyone to see what some of the trade-offs would be and made the various outcomes more explicit,” says Dr. Beissinger.
Dr. Beissinger uses @RISK for both research and teaching, and finds it to be a valuable tool: “@RISK makes Monte Carlo processes easy for professionals and students to understand,” he says. “The nice thing about it, from my perspective, is that it functions within Excel, which makes visualizing the information a lot easier and more intuitive for people. It makes what would otherwise be a lot of tedious steps a lot easier to do.”
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Excerpted from Evolver Solutions for Business,
by Roy L. Nersesian, published by Palisade
Suppose that you are responsible for production planning at a plant whose product line shows enormous variations in sales. The two key operating questions facing you are what inventory level should trigger a production run and how many should be produced. This example uses Evolver to evaluate two different situations. The first is when an order is placed once the inventory numbers reach a certain trigger point and the other is when there is one order per shipping cycle. RiskOptimizer could also be used for these same two situations. The output under Evolver is discrete whereas the output under RiskOptimizer, while also discrete, is based on probability distributions of costs and stockouts.
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Combining Inputs in a Sensitivity Tornado
Q: How can I aggregate multiple inputs in the tornado graph, so that I see the output's sensitivity to the combination instead of sensitivities to the individual inputs?
If you would like to maybe combine countries in a region, or maybe you want an NPV instead of individual cash flows, or some other situation where you would like to see a combination of sensitities, the RiskMakeInput( ) is the function that will help you do just that. If you already have a formula in your workbook that computes the aggregate you're interested in, just wrap it in a RiskMakeInput( ), like this:
=RiskMakeInput(formula, RiskName("name to appear in tornado") )
As an alternative, you could just create that formula in an empty cell.
RiskMakeInput( ) tells @RISK that its contents should be treated as an @RISK input distribution for purposes of sensitivity analysis, and RiskMakeInput( )'s precedents should be ignored in sensitivity analysis.
» Read the complete answer here
» View other Techniques and Tips here
Estimation of Mixed Traffic Densities in Congested Roads Using Monte Carlo Analysis
by Brian Freeman, Bahram Gharabaghi, and Jesse Thé, EM Magazine, April 2015
The authors use @RISK to create a novel stochastic Monte Carlo method to estimate the number and type of vehicles on a 1-km stretch congested roadway. This technique can then be linked to vehicle emission data to estimate real-time mobile source pollution on roadways due to traffic conditions. The resulting graphs of the @RISK model yielded a power curve that approximates the expected number of cars at each speed, thus giving researchers a fast, convenient tool for better understanding and estimating vehicle numbers in traffic. For Freeman, the benefit of using @RISK for this research was its efficiency. “You can quickly create a model in Excel that would otherwise require a very complicated statistical tool package to set up,” he says. “You can easily create your model without having to become an expert in statistics.”
» Read the full article here
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The DecisionTools Suite is the world’s only integrated set of risk and decision analysis programs. The Suite includes @RISK for Monte Carlo simulation, PrecisionTree for decision trees, and TopRank for “what if” sensitivity analysis. In addition, DecisionTools Suite comes with StatTools for statistical analysis and forecasting, NeuralTools for predictive neural networks, and Evolver and RISKOptimizer for optimization. All programs work together seamlessly, and all integrate completely with Microsoft Excel for ease of use and maximum flexibility.
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