1)Reading 11: What is an expected monetary value? How does the EMV contrast to the monetary value? Why is it important for a decision maker to ignore irrelevant data? How sensitive is any decision to changes in probability?

2)Reading 12: If perfect information is never available, why consider it? Cite several examples of how you can positively affect or alter the downside risk associated with any decision.

3)Topic for Discussion:( refer accounting assignment3 file)

Many companies have made decisions to reduce their workforces, close stores, and otherwise shrink their businesses - often because of a decision to outsource.Conduct an Internet search to locate an article about a company that has recently announced significant layoffs, store closings, or other business reductions.

Learning Objective 1 - Distinguish between relevant and irrelevant revenues and costs. Relevant costs are costs which differ among alternatives. Another way to view relevant costs is to identify those which are avoidable, or those which can be eliminated by choosing one alternative over another. Sunk costs and future costs that do not differ among alternatives are not relevant. Sunk costs are costs that have already been incurred. Because sunk costs cannot be avoided, they are not relevant in a decision. Opportunity costs, however, are relevant. Opportunity costs are the benefits forgone by choosing one alternative over another and are relevant costs for decision-making purposes. Decision making is the process of identifying different courses of action and selecting one appropriate to a given situation. Learning Objective 2 - Analyze relevant costs and indicate how they differ under alternative decision scenarios. The many types of short-term tactical decisions that managers make require relevant and timely accounting information. In general, variable costs are relevant in production decisions because they vary with the level of production. Likewise, fixed costs are generally not relevant, because they typically do not change as production changes. However, variable costs can remain the same between two alternatives and thus become irrelevant. Fixed costs can vary between alternatives and thus become relevant. Therefore, it can be misleading to always view variable costs as relevant and fixed costs as irrelevant. Learning Objective 3 - Apply differential analysis to decision scenarios. Relevant costs are also known as differential or incremental costs. Now that you know what costs should be considered when making short-run decisions, let's look at a few types. Changes in plan decisions are basically CVP analysis using differential analysis. Special-order decisions are short-run pricing decisions in which management must decide which sales price is appropriate when customers place orders that are different from those placed in the regular course of business. Special-order decisions are affected by whether the company has excess production capacity and can produce additional units with existing machinery, labor, and facilities. A special order would almost never be accepted if a company does not have excess capacity. If a company does not have excess capacity, it will have to turn away current customers in order to fill a special order, and this may permanently damage the relationship with those customers. The price of a special order must be higher than the additional variable costs plus any opportunity costs incurred in accepting the special order. Even if a special order is profitable from a quantitative perspective, the impact on customer relations should be considered before deciding whether to accept or reject the order. Outsourcing and make-or-buy decisions are short-term decisions to outsource labor or to purchase components used in manufacturing from another company rather than to provide services or produce components internally. An analysis of outsourcing and make-or-buy decisions requires an in-depth analysis of relevant quantitative and qualitative factors and a consideration of the costs and benefits of outsourcing and vertical integration. Vertical integration is accomplished when a company is involved in multiple steps of the value chain. The advantage for the vertically integrated companies is that they are not dependent on suppliers for timely delivery of components. However, vertical integration may not be beneficial when the supplier provides a higher quality part for less cost. To decide whether to make or buy, the total avoidable costs need to be compared with the cost of purchase. If the total avoidable costs are more than the cost of purchase, buying is recommended. On the contrary, if the total avoidable costs are less than the cost of purchase, making is advisable. Before making a decision, qualitative factors like quality of the part, the importance of keeping up with changing technology, and the dependability of the supplier need to be considered. Opportunity costs should also be considered in make-or-buy decisions if resources used in making have alternatives uses. Sell or process further decisions occur when a company can produce different products from the same beginning materials depending on how long production continues. For example, a tree can become logs, lumber, shingles, or sawdust. The key in deciding whether to sell or process further is that all costs incurred up to the point where the decision is made are sunk costs and, therefore, not relevant. The relevant costs in a sell or process further decision are the incremental or additional processing costs. Managers should compare the additional sales revenue that can be earned from processing the product further with the additional processing costs. If the additional revenues from further processing are more than the additional processing costs, further processing is advisable. Learning Objective 4 - Allocate limited resources for purposes of maximizing short-run profit. A resource utilization decision is a short-term decision which requires an analysis of how best to use a resource that is available in limited supply. This constraint is a restriction that occurs when the capacity to manufacture a product or to provide a service is limited in some manner. To maximize profit, managers must focus on the contribution margin provided by each product per unit of limited resource rather than on the profitability of each product. The theory of constraints is a management tool for dealing with constraints. It identifies bottlenecks in the production process. Bottlenecks are production-process steps that limit throughput or the amount of finished goods that result from the production process. Once a bottleneck is identified, management must focus its time and resources on relieving the bottleneck. Reading 11: Performance, Relevant Information & Risk Profiles 1 (File021r reference only) Performance, Relevant Information & Risk Profiles Relevant Monetary Flows: Monetary flows are relevant for comparing alternatives. Relevant monetary flows depend on which alternative is selected. Do not consider the monetary flows that are not relevant. Ignore them. Decisions should be forward looking and reflect the comparative or incremental affect of decisions. Past flows might negatively affect forward looking decisions. However, there are times when fixed cost cannot be dismissed as irrelevant - decision not to produce applies (for example) for a season would make fixed cost relevant. Let's look at an example. Example: *An apple grower purchased his orchard 10 years ago for $20,000. (sunk costs) *Normal cultivating and maintenance costs are $1,000 per year. (fixed costs) **Picking costs are $1,500 per year. **Cost of the new fertilizer is $500. **Revenue: 1. New fertilizer will increase production by 10%. 2. Apples sell for $1.35 per bushel. 3. Average yield is 5,000 bushels. What does he do? The first step is to separate the relevant and irrelevant costs. *Not relevant: Purchase Price of the Orchard: This $20,000 is sunk costs and is not relevant to our current decision. $1,000 Annual Operating Cost: relevant to our current decision. This $1,000 is fixed cost and is not 2 Reading 11: Performance, Relevant Information & Risk Profiles (File021r reference only) For many decisions, such as common pricing decisions, the relevant monetary value is the contribution margin (contribution to reduce fixed costs). This approach accounts for variable costs and revenue, but not fixed costs. **Relevant: $1,500 picking costs. This would not be relevant if the pickers were on salary with no change in the picking costs. However, this is probably not the case. We will consider picking costs are variable thus relevant. $500 fertilizer costs. This is clearly a variable cost and is relevant. Revenue. This is a variable cost since the more yield, the more production, so this is relevant also. Here you must be careful to only calculate the change in the revenue and not the total revenue. From this information we will calculate the expected monetary value (EMV). Revenue from Increased Yield = [10% times 5,000 times $1.35] = $675 (increase) Additional picking costs = [10% times 5,000 times $.30*] = (cost) Relevant Monetary Flows Revenue from Increased Yield Additional Picking Costs Cost of Fertilizer Formula 10% times 5,000 times $1.35 10% times 5,000 times $0.30* Fixed Cost Net Relevant Results $150 Result +$675 -$150 -$500 +$25 *Since we are treating the additional picking cost as variable, we determine the cost per bushel by dividing the cost by the average bushels or $1,500 5,000 = $0.30 per bushel. How do we interpret the result? There is a positive relevant monetary flow; therefore, he should apply the fertilizer. The expected monetary flow is not one that actually occurs, but is one that is useful in helping one make a decision. Positive monetary flows would lead to make a positive decision. Negative monetary flows would lead to Reading 11: Performance, Relevant Information & Risk Profiles 3 (File021r reference only) make a negative decision. The larger the monetary value (positive or negative) the stronger the decision. A monetary value around zero would lead to a decision tending toward neutrality. Expected Monetary Value: Let's look at another example of the concept of expected monetary value. Let's assume we want to play a game. We will flip three coins in sequence. If the results show we have 3 heads in three flips, we win +$20; if 2 heads in three flips, we get +$10; if 1 head in three flips, we lose -$12; and if 0 heads in three flips, we lose -$20. The question is should we play the game? This is also referred to as a mathematical expectation problem. To view it in the easiest manner, we will set up a table of the outcomes and the probabilities associated with the outcomes. Outcome: # of Heads 0 1 2 3 Outcomes Probability [f(X)] (Given) 1/8 3/8 3/8 1/8 1.00 Expected Gain or Loss (X) -$20.00 -$12.00 +$10.00 +$20.00 N/A Expected Monetary Value or the Mathematical Expectation -$2.50 -$4.50 +$3.75 +2.50 Expected Monetary Value Netted is -$0.75 So do you play the game or not? The expected monetary value (mathematical expectation) is negative, so you have an expected loss from the game if you play it many times. Usually statisticians tell you the game should be played up to 1,000 times. While it is true that you can make $10.00 by just getting two heads on any one cycle of three flips of a coin, if you continue to play the game in the long-run you will lose more than you win. From this additional example, you can see that the mathematical expectation is negative (-$0.75). The winnings one might expect (the payoff) are -$20, -$12, +$10 and +$20 (monetary values). These are random variables. Any one of them could occur on any flip of three coins, but when we weigh the random variable with the probability of the outcome then net them, we develop a mathematical expectation of the winnings. The values that could actually occur are monetary values and each is real. The -$0.75 is not real, but a weighted outcome of what we might expect in the long-run. 4 Reading 11: Performance, Relevant Information & Risk Profiles (File021r reference only) Determination of Probabilities for Those Interested [f(x)]: The last column is determined by multiplying the probability times the expected gain or loss, then summing the last column. The probabilities, while given here, would be determined by the rules of independent events where the probabilities are multiplied. For example, if you have three independent events (flipping three coins sequentially), you would have the following outcomes: Indep. Indep. Indep. Event Event Event #3 #2 #1 H (.5) H (.5) H (.5) Multiplication Totals .5*.5*.5 = 0.125 Converting Probabilities to Fractions 3 heads = 1/8 H (.5) H (.5) T (.5) H (.5) T (.5) T(.5) H (.5) T (.5) H (.5) T (.5) H (.5) T (.5) T (.5) H (.5) H (.5) T (.5) T (.5) H (.5) .5*.5*.5 = 0.125 .5*.5*.5 = 0.125 .5*.5*.5 = 0.125 .5*.5*.5 = 0.125 .5*.5*.5 = 0.125 .5*.5*.5 = 0.125 2 heads = 1/8 2 heads = 1/8 2 heads = 1/8 1 heads = 1/8 1 heads = 1/8 1 heads = 1/8 T (.5) T (.5) T (.5) .5*.5*.5 = 0.125 0 heads = 1/8 Combining Like Probabilities 1/8 = 3 Heads 3/8 = 2 Heads 3/8 = 1 Head 1/8 = 0 Heads Common Relevant Flow Mistakes: Accountants: Accountants will look back to report how well the firm has done and may allocate fixed costs to activities. All costs are relevant to the accountant. Decision Making is Different. Not all costs are relevant. If nonrelevant costs are included we may make wrong decisions. The words allocation or charge should be carefully examined. Ignoring Effects Elsewhere in the Company: From our previous example on the apple grower, switching fertilizer may cost a $40 rebate on the old fertilizer. In this case, we would not use the new fertilizer, since using it would mean a net loss ($40 + $25 = -$15). Evaluate the possible erosion of sales elsewhere in the organization Reading 11: Performance, Relevant Information & Risk Profiles 5 (File021r reference only) Does your decision increase costs to other divisions in your organization? Maybe your pickers are salaried and you hire them out to other growers for a fee of $100. The new fertilizer would cause you to become just large enough that they could not take the other job, thus losing $100 in revenues. This would leave you with an even more negative outcome. (-$125) Evaluate Abandonment Costs: If the grower had to change over and use another fertilizer spreader and had to abandon one which cost $250 this cost should also be considered. Flows of Unrelated Activities Should Not be Included: The concept here is one of either bundling or unbundling monetary flows. It is usually best to unbundled the components because bundling can cover up less desirable inclusions. Don't combine alternatives that are separable. Influence diagrams are useful in determining relevant monetary flows and relevant monetary flows may aid in building influence diagrams. You were introduced to the concept on influence diagrams in a previous reading, but if there are other possible influences on the outcome that are complicated by some of the issues outlined just above, you may want to draw that diagram as an aid in understanding the inter-relationships. Let's look at a couple of possibilities as we make decisions. First we will evaluate decisions where there is no uncertainty then we will evaluate decisions where there is uncertainty. Evaluating Alternatives Under No Uncertainty & Uncertainty: No Uncertainty (Certainty): If we have a single monetary value and no uncertainty, there are few problems in establishing the monetary flows. For example, we may easily decide we want to maximize our marginal contribution or profit or we may want to decrease our cost (minimize). One of these two outcomes is almost always under consideration in making decisions. Our actual decision may be tempered by nonquantitative issues, but the quantifiable should be examined first. In conditions of certainty this is no difficulty in the decision we will make. However, most of the time we are faced with uncertainty thus our decision opportunities will be clouded. Reading 11: Performance, Relevant Information & Risk Profiles 6 (File021r reference only) Few Potential Outcomes: We will look at two litigation examples. In the first example the probability of suing and winning is rather low (25% Case). In the second we will change the probability of winning to a much higher percentage (70% Case). The 25% Case: We will use the decision tree approach (Treeage software). The tree is shown below. The payoff numbers to the far right are given for each path. Just accept them for now. They are the relevant payoffs for each branch of the decision tree. These values are the real money damage payments (or costs) less or including any cost of litigation. They are referred to as monetary values (MV). As the process is developed, notice that at various stages probabilities have been included. They have been developed by using the subjective approach, which as you recall is a best estimate or best, educated guess approach. Often this is a \"gut\" feel the attorney will have about the possible outcome of the case. As you examine the results, you will notice that this is the entire sample space, which is all of the possible outcomes of the decisions you are facing. In other words, if I look at all of the legal possibilities associated with this case I can only experience these outcomes. Since this is the sample space the sum of the probabilities must equal one. Reading 11: Performance, Relevant Information & Risk Profiles 7 (File021r reference only) How do we read the Decision Tree? Let's examine one of the branches. If we sue and lose then appeal and lose, the monetary value (cost) will be a negative $90,000 in real dollars lost. Monetary values are estimates of value which you expect to really occur unlike expected monetary values, which you do not expect to occur. The expected monetary values are weighted averages of the probability of occurrence times the monetary value. Lose Branch is the Simplest to Understand: Sue and Lose then Appeal and Win We net +90,000. Sue and Lose then Don't Appeal We net - 40,000. So what is the best decision path, you might be asking. This is determined by something called the rollback. This process rolls back each monetary value as weighted by the probability of that monetary value occurring. The result is we have a path flowing from the right to the left (reverse order) which develops an expected monetary value at intermediate points. We can essentially ignore those intermediate points. What we want is the overall expected monetary value of the entire decision tree. This is found at the beginning point of the decision tree. If this value (EMV) is positive, we have an indicator of a positive decision to Reading 11: Performance, Relevant Information & Risk Profiles 8 (File021r reference only) sue. If this value is negative or very small, then the decision might be not to sue regardless of the emotion involved. The entire decision rollback is presented below. From this analysis, the best path is to sue. Notice the double bars on the \"Don't Sue\" path. This means this path is not the preferred one. The expected monetary value of the entire decision tree is +$1,200.00, which is a positive value. Positive values support making a positive decision. However, in this instance this is a small EMV and the decision should be further examined. The EMV is the expected gain or loss is for the entire sample space. Said differently, the expected gain is the mathematical expectation or the expected value of the random variable, which is called the EMV (Expected Monetary Value). Wait a minute, you say, speak English. Okay, I reply. This is the weighted mean of the entire sample space or distribution. It is weighted by the probabilities of an outcome. At each decision stage the probability of the outcome is applied against the outcome. These are then summed in reverse order to arrive at an overall expected monetary value for the one decision facing us - sue or don't sue (two alternatives). 9 Reading 11: Performance, Relevant Information & Risk Profiles (File021r reference only) Will we ever achieve the $1,200? The short answer is \"No\". The monetary values are all achievable based on the information we now know, but the EMV is a guide to helping us make a decision to take a particular path in our decision making process. Risk Profile: Let's look at a Risk Profile. Notice that the probability is shown on the Yaxis and the monetary value is shown on the X-axis. For those among us who like visual learning, you can readily see the risk profile is negative ($90,000) for the path which yields the highest probability of occurrence (49%). Litigation Risk Profile - 25% Case 0.6 0.5 0.49 Probability 0.4 0.3 0.21 0.2 0.15 0.08 0.1 0.05 0.02 0 -100 -80 -60 -40 -20 0 20 40 60 80 100 120 140 Monetary Value The negative $90,000 is the monetary value. The overall tree reflects a positive Expected Monetary Value (EMV) of $1,200, but this is not a real value. The EMV is the sum of the probability of the monetary value path under consideration times the Monetary Value (MV). The path with the greatest positive monetary value ($140,000) is the path of sue - win - no appeal, but the probability of this occurrence is only 15%. This means the greatest MV has only a 15% chance of occurring (not good). Reading 11: Performance, Relevant Information & Risk Profiles 10 (File021r reference only) Notice the Monetary Value (MV) is different than the Expected Monetary Value (EMV). The MV occurs. The EMV is a directional and strength indicator. Directing your attention once again to the overall 25% litigation case, you may evaluate some other facts. The upside is greater than the downside. +140,000 and -90,000. This may make this an attractive opportunity, but the path winning the upside has only a 15% chance of being successful. The probabilities of each path are shown to the far right of the payoff for each individual path in the decision tree. The most likely outcome is -90,000, which has a probability of 0.49 (49%). This is not attractive. The negative probabilities (0.02 + 0.49 + 0.05 = 0.56) are more likely than the positive probabilities (0.44). This is not attractive. The EMV is positive for the entire decision tree. (0.08 times 100,000) + (0.02 times -80,000) + (0.15 times 140,000) + (0.21 times 90,000) + (0.49 times -90,000) + (0.05 times - 20,000) = +1,200 +8,000 -1,600 + 21,000 +18,900 - 44,100 - 1,000 = + 47,900 46,700 = + 1,200 This is attractive. IF and ONLY IF, we base our decision on the EMV alone, we sue. However, we may want to consider several other non-quantifiable possibilities before we use EMV alone. What is the asset value of our firm relative to the decision? We want to weigh the largest potential loss versus our assets to determine if it makes economic sense to sue. What about their firm? Do they have \"deeper pockets\" than we do thus leaving us open to a cash war? What is the impact on the stockholders of our firm? What is the impact on short-term or long-term opportunities? Reading 11: Performance, Relevant Information & Risk Profiles 11 (File021r reference only) Will we want to tie up our resources and time on this suit? The EMV is an aid in making your decision: In summary, if risk is not a major concern because the stakes are small or the assets of the firm are huge in comparison, then using the EMV alone is quite logical. If you use the decision tree in making your calculation, the process is known as \"folding back (rolling back) the tree.\" The number near each node represents the EMV of the remainder of the tree, assuming any decision taken subsequently will optimize the EMV. The EMV will be shown at the beginning of the decision tree rather than the end of the decision tree, although a similar weighted average can be determined at the end of the tree. We will not go through these calculations at this point, but for the more adventurous you would simply multiply the final probability times the monetary value, then sum them to achieve the same $1,200. The +1,200 is the EMV or the expected value of the entire opportunity. Under these expected gains, it is highly unlikely that you would sue even though the EMV is positive. Using it alone your decision should be to sue, but often the strength of the decision is not sufficient to follow the EMV blindly. The 70% Case: Let's change two important probabilities and leave the rest of the decision tree the same. Let's assume the probability of winning is now 70% and the probability of losing is 25% with a 5% chance of the suit being thrown out. To answer this we will use the decision tree and revised the initial probabilities. The new decision tree is shown below. 12 Reading 11: Performance, Relevant Information & Risk Profiles (File021r reference only) The key to suing is usually having a much higher EMV. The second example, where the initial probability was changed from 25% to 70% for winning, yields a much higher EMV ($66,720). So is suing a better option in the 70% case? In the 70% case example, suing is a much better option. The probabilities yield a much higher EMV, thus our decision to sue is stronger. The revised risk profile for the 70% case shows a different picture. Litigation Risk Profile - 70% Case 0.45 0.42 0.4 0.35 0.25 0.224 0.2 0.175 0.15 0.1 0.075 0.056 0.05 0.05 Monetary Value 14 0 12 0 10 0 80 60 40 20 0 -2 0 -4 0 -6 0 -8 0 0 -1 00 Probability 0.3 Reading 11: Performance, Relevant Information & Risk Profiles 13 (File021r reference only) Compare the risk profile for the 25% case and the 70% case. As you can see the probabilities have shifted considerably to the positive side in the 70% case. But wait a minute: Are the options of suing or not suing the only options? Let's say we do decide to sue, what other option might be available to us? Using the EMV of $66,720, if someone offered you $10,000 to take over your position in the suit would you take it and walk away? How about $25,000? How about $50,000? Remember you are selling the most positive path which yields at best a $140,000 MV. The point is there may be other options which open up if the EMV is large enough to attract other \"investors\" or risk takers who are not averse to risk. In the second litigation example (70% case), the model (decision tree) probability of winning shifts to 71.9% (0.224+0.420+0.075) and the probability of losing is 28.1% (0.056+0.175+0.050). Here the decision is much stronger to sue. These values are determined by taking the probability of each leg to win and each leg to lose and summing similar probabilities. If you match this against the probabilities of winning and losing in the 25% cases you have a combined probability of losing of 56% (0.05 + 0.49 + 0.02) and of winning of 44% (0.08+0.15+0.21). I don't know about you, but I much prefer the probability of winning of 71.9% rather than 44%. 14 Reading 11: Performance, Relevant Information & Risk Profiles (File021r reference only) Litigation Risk Profile - 70% Case 0.45 0.42 0.4 0.35 Probability 0.3 0.25 0.224 0.2 0.175 0.15 0.1 0.075 0.056 0.05 0.05 14 0 12 0 10 0 80 60 40 20 0 -2 0 -4 0 -6 0 -1 00 -8 0 0 Monetary Value Litigation Risk Profile - 25% Case 0.6 0.5 0.49 Probability 0.4 0.3 0.21 0.2 0.15 0.08 0.1 0.05 0.02 0 -100 -80 -60 -40 -20 0 20 40 Monetary Value 60 80 100 120 140 1 Reading 12: Risk Management (File022r reference only) Risk Management Understanding the risk of alternatives will help us make better decisions. We need to learn how to create value and reduce risks. We need to make the quality of decisions improve by controlling factors that increase the monetary value. Value of Information: The higher the monetary value the better the alternative. Good but imperfect information is usually all that is available to us. What sort of decisions do you think we would make if we knew perfect information? A better decision is the obvious reply. Example: Vickie has a decision to make. Vickie has current investments that yield $100,000 if she does nothing. There is an opportunity to enter a new venture operating a cable company with her friends. If the new venture obtains approval and a license to operate the new cable company she makes $200,000. If they do not get the approval or license, she will only get one-half of her current investment yield or $50,000. She plans to analyze her opportunity based on the use of a decision tree using the best information available to her. She believes the company has a 60% chance of success. 1.Vickie's Decision with No Information (Base Case) Reading 12: Risk Management 2 (File022r reference only) 2.Vickie's Decision with No Information Rolled Back (Base Case) The $140,000 is the EMV (Expected Monetary Value) without any additional information. This is the base case decision tree. Based on this approach, since this is a positive value and she should make the investment. However, let's complicate the alternatives a bit more since Vickie believes she needs to know how her decision might be affected if she knew for certain she would receive approval of the license. Let's expand our alternatives by expanding the information we have available to make the decision. Vickie knows a firm who specializes in aiding businesses in obtaining the sort of license the new venture will require. She can purchase accurate information about whether or not the new venture will receive approval of the license or not. That information will prove valuable to Vickie as she considers making her investment decision. The question in Vickie's mind is how much is information going to cost and will it be worth it to incur that cost? She needs perfect information, but at what price? She needs the Expected Value of Perfect Information (EVPI). Notice the word \"of\" here. 3. EVPI - the Expected Value of Perfect Information To determine this value (which is the maximum she can pay to the firm for the information), we will take the difference between the following two rolled back values. EMV with Perfect Information less EMV without Perfect Information (or with no information) = EVPI. Notice the word \"with\" here. The key to developing a decision tree for perfect information is dependent on the probabilities assigned to success and failure of the new venture after the license 3 Reading 12: Risk Management (File022r reference only) has been approved or after the license has been disapproved. Perfect information implies that the probability of success is 1.0 (EVPI) as is the probability of failure equal to 1.0. The EMV with perfect information (EVPI) must have a final probability of 1.0 and is defined as success and is shown on one leg of the tree. The corollary is also true, the EMV without perfect information must have a final probability of 1.0 and is defined as failure. This is shown as one leg of the tree. This concept is called clairvoyance. First, Vickie must consider her decision based on the assumption she has the perfect information available to her. The choices must be made between License approval and success in the new venture License disapproval and failure in the new venture Two more decision trees are necessary - Perfect Information Decision Tree and an Imperfect Information Decision Tree. We will look at them one at a time. 4.Perfect Information Information) Decision Tree (Expected Value with Perfect To help Vickie make her decision, we will expand the previous decision tree to include additional information. The approval or disapproval of the license now becomes the hinge on which our future decisions are based. The probability of approval still remains at 60%. The Reading 12: Risk Management 4 (File022r reference only) probability of disapproval still remains at 40%. However, notice that if approval occurs, the probability of success in the new venture is 1.0. Notice that if we do not receive the approval (disapproval), the probability of failure of the new venture is 1.0. This allows us to determine the expected monetary value if we have perfect information. Combining these two gives us a picture of EVPI or $160,000 as shown in the rollback below. In this decision tree, if we move forward with the new venture, Vickie will base her future decision options on receiving the approval or disapproval of the license. To recap: The rollback shows we have an expected monetary value (EMV) with perfect information which is $160,000. With perfect information, we have a much stronger case for making the decision to launch the new venture. 5. How Much Can Vickie Pay for Perfect Information? The EMV (Expected Monetary Value) for the perfect information decision tree is $160,000 based on perfect information. Notice that this value (as must all values in our decision) must fall between $100,000 and $200,000. To recap, she makes $100,000 if she does not make the decision to invest in the new venture. The maximum monetary value is $200,000 if she makes the decision to begin the new venture, receives the license and is successful. These two values are the high and the low monetary values, thus are the outside limits of the entire decision tree. The expected value with perfect information is larger than the EMV without information ($140,000), which is the Base Case decision tree. It must always be higher, because we have strengthened the information available. This is 5 Reading 12: Risk Management (File022r reference only) the maximum expected value with perfect information. The calculation of the EVPI (Expected Value of Perfect Information) is the difference between these two values. EMV with Perfect Information Less: EMV with no Information (Base Case) $160,000 -$140,000 Equals EVPI (Expected Value Of Perfect Information) $20,000 What this tells us is that Vicky can pay as much as $20,000 for gathering the information necessary to make her decision. This is the most she could pay. She would hope the amount would be considerably less, but there is up to $20,000 in value to be gained by resolving the uncertainty. However, we are not yet though with Vickie's alternatives. \"Oh no\