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Submitted by BobScratchems on Tue, 2012-05-22 23:05
due on Fri, 2012-05-25 23:00
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Assignment 2

Decision trees are models which allow you to both visualize and quantify a range of possible outcomes when faced with complex choices. These models incorporate the timing and estimated probability of outcomes along branches on a tree to help you identify the most promising path forward.

Review the following:

Real Options and the Value of Information

Did you know that roughly 60 percent of new restaurant businesses fail within the first three years of operation (Abrams, 2004)?

Suppose you have a close friend who is employed in a high-paying position in the banking industry with tremendous potential for her professional and financial growth. However, your friend wants to leave this position and start a little restaurant. It is your job to help your friend make a sound decision. What do you do?

Often, the valuations on which decisions are based require the input of information neither easily deduced nor accurately available. There are so many options and so little time. The values of these options can be clouded in uncertainty. The likelihood of each outcome—both those that are dependent and those that are independent—is shrouded in a variety of likely scenarios.

Like your friend, you must assess the value of certain options, including those choices foregone. If your friend, the restaurateur, leaves her job, what is the income she has given up? What is the probability that her business will flourish for a year or two years? What is the likelihood today that she will be in business three years from today (you would immediately think 40 percent)? What if she creates a great restaurant that is widely acclaimed, but the market, well beyond her control, suddenly crashes? How might you have incorporated that information in your forecast of probabilities?

As you can see, decision points combine with scenarios, including events beyond the chooser’s control, to increase the complexity of choosing. Fortunately you have a tool, scenario analysis, which works in conjunction with decision trees where multiple outcomes and the likelihood of those outcomes can be evaluated in light of an uncertain future and the need for a choice today. As much as you need to rationalize your choices and incorporate information accurately and reasonably, you must also learn to forecast reasonably, and identify those biases that undermine your choices.


Assume the following facts as well as the above information:







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Assignment Details






Write a 3–4-page paper in Word format describing your decision making process, conclusions, and recommendations for Jennifer. Apply APA standards to citation of sources. Use the following file naming convention: LastnameFirstInitial_M4_A2.doc.

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Assignment 2 case solution Jennifer Decision Tree

body preview (6 words)

xxxxxxxxxx x xxxx xxxxxxxx xxxxxxxx Decision Tree

file1.doc preview (1248 words)

Running Head: DECISION TREE 1 DECISION TREE � PAGE xx xxxxxxxxxxx �2�

xxxxxxxxxx xx xxxxxxx xx x xxxxx Award



Case xxxxxxxxxxxxx

Jennifer is a xxxxxx xxxx xxxxxxxxxx xxxxxxxxx for professional xxx xxxxxxxxx xxxxxx in xxx xxxxxxx xxxxxxx However, xxx has an xxxxxxxxxxxxxxx streak and xxxxx to open x xxxxxxxxxx xxxxxxx of her xxxx skills. It’s x crucial decision that requires thorough xxxxxxxx xx the xxxxxx xxxxxxxxx xx xxx xxxxxxxxxxx especially with xxx xxxxxxxx average of xx percent of new xxxxxxxxxxx closing xxxxxx xxxxxxx xxxxx years xx xxxxxxxxxx (Abrams, 2004). xx xxxxxxxx would have xx xxxxx xxx xxxxxxxxx xxx xxx xxxxxxxxx xxx xxx restaurant, this paper analyses both xxx xxxxxxx that xx to xxxxxxxx her xxx or to start xxx restaurant.

Decision Making xxxxxxxx

Decision tree is an effective xxxx for xxxxxxxx xxxxxx especially for those projects xx which xxxx is sequential and not discrete xxxüxxx & Küxxx 2009). xxxx xxxx also

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file2.xlsx preview (3240 words)


xxxx xxxxxxx xxx the Value xx Information
xxx you know xxxx xxxxxxx xx xxxxxxx xx xxx restaurant xxxxxxxxxx fail xxxxxx xxx first three years of operation (Abrams, xxxxxx
xxxxxxx you xxxx x xxxxx xxxxxx xxx is xxxxxxxx xx x xxxx xxxxxx position in xxx xxxxxxx industry xxxx xxxxxxxxxx potential xxx both professional xxx xxxxxxxxx xxxxxxx Nevertheless, your xxxxxx xxxxx to xxxxx and xxxxx x xxxxxx restaurant. xx xx your xxx to xxxx xxxx xxxxxx xxxx x xxxxx decision. What do you xxx
xxxxx the xxxxxxxxxx on which decisions xxx based require xxx input xx xxxxxxxxxxx xxxxxxx xxxxxx deduced xxx accurately xxxxxxxxxx xxxxx xxx so many options and xx xxxxxx time. The values xx xxxxx options can be xxxxxxx in xxxxxxxxxxxx The likelihood xx xxxx outcome—xxxx those xxxx xxx xxxxxxxxx xxx xxxxx xxxx xxx xxxxxxxxxxx—is shrouded xx a xxxxxxx xx likely scenarios.
Like your friend, you must xxxxxx the xxxxx of xxxxxxx options, including those choices xxxxxxxxx If your xxxxxxx xxx

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