Expert answer:Decision analysis questions
Answer & Explanation:Hello Dear Dr, Henry here is my 2 questions exam ,, fortunatly ,, its same as last year ,, So you can see how it can answer them .. i wish you please to help me to answer it i will provide you with my reference final decision analysis . ANS.docx HBR Dec Traps.pdf
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1. Explain what a Monte Carlo Simulation is and how it is used. Provide an example of a
decision or problem for which a company or organization might use a Monte Carlo. (10
Points)
A Monte Carlo simulation is a process that generates random number inputs for uncertain values, which are then
processed by a mathematical model, so that many scenarios can be evaluated. Results are plotted to provide a probability
distribution of the output. It provides another approach to dealing with uncertainties, which before may not have been
possible using decision tree or influence diagram. The technique is used by professionals in such widely disparate fields as
finance, project management, energy, manufacturing, engineering, applied statistics research and development,
insurance, Design and visuals oil & gas, transportation, Telecommunications and the environment.
The goal of a Monte Carlo simulation is to allow you to generate many possible outcomes which can then be used to
better understand the uncertainty surrounding the problem. Also, it lets you see all the possible outcomes of your
decisions and assess the impact of risk, allowing for better decision making under uncertainty.
The Monte Carlo Simulation will in most cases require the use of a computer, although simple models can be run using
tables or hand held calculators. It performs risk analysis by building models of possible results by substituting a range of
values—a probability distribution—for any factor that has inherent uncertainty. It then calculates results over and over,
each time using a different set of random values from the probability functions. Depending upon the number of
uncertainties and the ranges specified for them, a Monte Carlo simulation could involve thousands of recalculations
before it is complete. Monte Carlo simulation produces distributions of possible outcome values. By using probability
distributions, variables can have different probabilities of different outcomes occurring. Probability distributions are a
much more realistic way of describing uncertainty in variables of a risk analysis. Common probability distributions
include: Normal, lognormal, uniform, PERT, triangular and discrete. “Monte Carlo simulation provides a number of
advantages over deterministic, or “single-point estimate” analysis:
•
Probabilistic Results,
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Graphical Results,
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Sensitivity Analysis,
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Scenario Analysis,
•
Correlation of Inputs”.
In many business situations involve uncertainty in many dimensions for example, variable market demand, unknown plans
of competitors, uncertainty in costs, and many others.
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Monte Carlo simulation usually used in project management, primarily in program and portfolio management when
making capital, budgeting and investment decisions. It assists managers in choosing among different potential
investments and projects. He explained that by replacing estimates of net cash flow for each year with probability
distributions for each factor affecting net cash flow, managers can develop a distribution of possible Net Present Values
(NPV) of an investment instead of a single value. This is helpful when choosing between different capital investment
opportunities that may have similar mean NPV but differing levels of variance in the NPV distribution (Wka k & Ingal l,
2007). When the simulation is complete, we have a large number of results from the model, each based on random input
values. These results are used to describe the likelihood, or probability, of reaching various results in the model. In brief,
the Monte Carlo Process is as follows:
1. Developing a computer algorithm to generate data in specified manner.
2. Developing a strategy or procedure as model.
3. Entering random input.
4. Calculation for the input and getting output.
5. Repeating for process 3 and 4 many times.
6. Analyzing the results in different ways to avoid risks and making better decisions.
Finally, the decision making is the most important part of the company and the riskiest job of the executives because the
bad decision would be critical for the company health. The researchers have identified the series of flaws through which
they make the decision. The uncertainties come from the biases or from the irrational anomalies which make the traps of
dangerous through the invisibility. To make the decision in the positive manner there is a need for analysis of the different
situations such as the risk analysis. Every company has to face the uncertainty, variability and the ambiguity. The Monte
Carlo Simulation is the method which predicts the possible outcomes related to the decision of the problem. In addition
to this, this method helps to assess the risk impact and allow the company to take the best decision under uncertain
circumstances. The researchers and the decision maker use this method in the uncertain situations such as the in the
analysis of the risk in the business or risk related to the people of the country. The companies first of all frame the
question which is the most dangerous step but this is the way which influences the choices the company wants to take.
The company should use the problem through the different perspectives. The company should use the alternative ways
instead of the sticking on one way. According to the researchers the companies should think about the problem and after
that they should consult to others because through this the companies and the management can avoid the situation of
becoming the anchored through their ideas. The organization might use a Monte Carlo while they have to answer the few
basic and the important questions which are based on the nature of the plan, reasoning of suggesting the plan, objectives
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and the goals behind the plan budget and the cost of the plan. So, the management has to make the clear answer of it
because these are the central question for the decision making process or the problem and urge everyone to act
according to it. The management should be opening minded as well as it should seek the information and the opinion
through the different kind of people which can enlarge the connectional frame and let the minds in the different
directions. The companies should be careful and stop to fix the advisers and the consultants because through that the
companies fixed the ways or the options. In the case of the automobile insurance there is a difference of the $ 200 million
in the insurance cost. So, to reduce the cost of the insurance the neighbor countries New Jersey and Pennsylvania made
the similar changes in their laws and the different laws of the countries states the different positions for the company. As
a result, in New Jersey about 80% of drivers chose the limited right to sue, but in Pennsylvania only 25% chose it. Because
of the way it framed the choice, Pennsylvania failed to gain approximately $200 million in expected insurance and
litigation savings. All in all, Monte Carlo simulation provides a much more comprehensive view of what may happen. It
tells you not only what could happen, but how likely it is to happen.
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3. Discuss two common DESCRIPTIVE Theory decision traps and how they might be avoided?
1. Status quo:
Favoring alternatives that perpetuate the existing situation.
Example: A key merger stumbles because the acquiring company avoids imposing a new
management structure on the acquired company.
Avoiding the Trap:
• Ask if the status quo really serves your objectives.
• Ask if you’d choose the status quo if it weren’t the status quo.
• Downplay the effort or cost of switching from the status quo.
2. Confirming evidence:
Seeking information that supports your existing point of view.
Example: A CEO considering canceling a plant expansion asks an acquaintance, who canceled such an
expansion, for advice. She, of course, says to cancel.
Reference
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Palisade Corporation. (2012). About Monte carlo simulation: how monte carlo simulation works. Retrieved
from http://www.palisade.com/risk/monte_carlo_simulation.asp
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Heffernan, R. (2012, July 14). Knowledge is power: Embracing risk analysis. ECT News Network, Retrieved
from http://www.ecommercetimes.com/story/75634.html
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Hammond, J. S., Keeney, R. L. & Raiffa, H. (1998, Sept-Oct). The hidden traps in decision making. Harvard
Business Review, 47(8). Retrieved from http://faculty.washington.edu/janegf/decisionmakin gtraps.html
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Wka k, Y. H. & Ingal l, L. I. (2007, Sept). Exploring monte carlo simulation applications for project
management. Risk Management, Retrieved from http://home.gwu.edu/~kwak/
Monte_Carlo_Kwak_Ingall.pdf
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