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» @RISK Fights Avian Flu in Scientific Computing World
» Palisade User Conference North America Draws Record Global Delegation
» Free Live Webcast: The DecisionTools Suite at Unilever
» Risk and Decision Assessment Training using @RISK, Part II
Winter Simulation Conference
Mines and Money
@RISK Fights Avian Flu in
Palisade User Conference
Sam McLafferty, President, Palisade Corporation noted, “I was impressed with the dynamic energy of this conference. The attendees engaged our staff and the presenters with a vigor I have seldom seen.”
“Overall, I found the conference to be an excellent use of time. The Palisade staff was extremely helpful in answering my questions and educating me on the software. I look forward to working with the new [@RISK 5.0] version.”
“The dinner / networking was an invaluable part of conference!”
“Great experience, will definitely return in the future.”
Free Live Webcast:
Risk Quantification at Suncor
As presented at the 2007 Palisade User Conference by
John G. Zhao, Manager, Project Risk Management Program,
Suncor Energy Service
The significance of risk quantification in the decision-making process reflects the emergence of modern risk management within the project management discipline. Using the DecisionTools Suite of software tools, the integrated process forces decision makers to quantify risk impacts rather than to qualify risk effects on their decisions.
Risk Register, Monte Carlo Simulation, Decision Trees and Force Field Analysis have been integrated to facilitate the decision-making process. This system has been used and applied to Suncor Bitumen Selection Strategy, and the case study proved to be successful. In addition, the result of this case study, along with further research work, may have potential commercial values. If the processes are properly generalized, theorized, and formalized; it will be valuable to Suncor and to any other energy company which desires a proven methodology for their future major capital project selection decisions.
Value at Risk (VAR)
Anybody who owns a portfolio of investments knows there is a great deal of uncertainty about the future worth of the portfolio. The concept of value at risk (VAR) has been used to help describe a portfolio's uncertainty. Simply stated, the value at risk of a portfolio at a future point in time is usually considered to be the fifth percentile of the loss in the portfolio's value at that point in time. In other words, there is considered to be only one chance in 20 that the portfolio's loss will exceed the VAR. To illustrate the idea, suppose a portfolio today is worth $100. We simulate the portfolio's value one year from now and find there is a 5% chance that the portfolio's value will be $80 or less. Then the portfolio's VAR is $20 or 20%. The following example shows how @RISK can be used to measure VAR. The example also demonstrates how buying puts can greatly reduce the risk in a stock. The two outputs represent the range of the percentage gain if we do not buy a put vs. the percentage gain if we do buy a put. The results illustrate there is a greater chance of a big loss if we do not buy the put, although the average return is slightly higher if we do not buy the put.
This example was taken from Chapter 45 of Financial Models using Simulation and Optimization by Wayne Winston, published by Palisade Corporation, where a detailed, step-by-step explanation can be found. It is also explained further in the @RISK User's Guide.
Discounted Cash Flow (DCF)
This example has also been extended to calculate the distribution of bonus payments on the assumption that a bonus is paid whenever the net DCF is larger than a fixed amount (such as 50). It also uses some of the @RISK Statistics functions RiskMean, RiskTarget, and RiskTargetD to work out the average net DCF, the probability that the net DCF is negative and the probability that a bonus is paid.
» Delayed cultivation on set-aside land benefits wildlife
Delaying cultivating or spraying off rotational fallow land for as long as possible are the keys to managing it most successfully for environmental benefit, according to a new decision tree produced by The Farming and Wildlife Advisory Group and The Game and Wildlife Conservation Trust
The team at the University of Southern Denmark developed the technology by first studying children in a playground. They categorized the behavior of children, comparing those who played in a disruptive manner with those who played in a continuous way. When they brought a new set of children to the playground, the neural network they had programmed had learnt to recognize different children's abilities.
In this article, a decision tree process was used to create the best, or optimal, soccer team. The goal was to develop a decision tree to each one of the team’s players.
Credit card companies have turned to sophisticated computer programs called “neural networks” to fight fraud. A computerized neural network needs to undergo a process of “training” to decide whether a transaction is legitimate. One of the great advantages of neural networks is the sheer scope of data they can analyze to discern patterns.
» Online Resource To Help Medical Responders
Plans to counter one of the most menacing threats - radiation contamination by nuclear explosion, "dirty" bomb, or some other device - have been developed. Part of a solution to this challenge is a series of decision-tree algorithms for the nonexpert physician to follow at the scene....
» Urjit R Patel: The Heat is On
» Neonatal Screening for Metabolic
A Monte Carlo simulation was used to assess the incremental cost-effectiveness ratio (ICER) of screening vs. not screening for medium chain acyl-CoA dehydrogenase (MCAD) deficiency, a recessive disease.
Undiscovered oil and gas resources in each of 24 plays (assessment units) within the Alaska Central North Slope were estimated using a deposit simulation analysis. Sizes of oil and gas accumulations were simulated using a Monte Carlo algorithm.
» Improving Yield through Parametric
Random and systematic process variations reduce parametric yields significantly at the leading 65nm and 45nm CMOS process technologies. Local distribution and correlation models of circuit modeling parameters supply statistical circuit analysis engines using Monte Carlo sampling techniques.
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