Introduction to Decision Analysis
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1 Session # Page Decisions Under Certainty State of nature is certain (one state) Select decision that yields the highest return Examples: Product Mix Diet Problem Distribution Scheduling Decisions Under Uncertainty (or Risk) State of nature is uncertain (several possible states) Examples: Drilling for Oil Developing a New Product Newsvendor Problem Producing a Movie
2 Session # Page Oil Drilling Problem Consider the problem faced by an oil company that is trying to decide whether to drill an exploratory oil well on a given site. Drilling costs $,. If oil is found, it is worth $,. If the well is dry, it is worth nothing. However, the $, cost of drilling is incurred, regardless of the outcome of the drilling. Decision State of Nature Payoff Table Which decision is best? Optimist : Pessimist : Second-Guesser : Joe Average :
3 Session # Page Bayes Decision Rule Suppose that the oil company estimates that the probability that the site is Wet is %. Payoff Table and Probabilities: State of Nature Decision Wet Dry Drill - Do not drill Prior Probability.. All payoffs are in thousands of dollars Expected value of payoff (Drill) = Expected value of payoff (Do not drill) = Features of Bayes Decision Rule Accounts not only for the set of outcomes, but also their probabilities. Represents the average monetary outcome if the situation were repeated indefinitely. Can handle complicated situations involving multiple and related risks.
4 Session # Page Using a Decision Tree to Analyze Oil Drilling Problem Payoff Table and Probabilities: State of Nature Decision Wet Dry Drill - Do not drill Prior Probability.. All payoffs are in thousands of dollars Decision Tree: Folding back: At each event node (circle): calculate expected value (SUMPRODUCT of payoffs and probabilities for each branch). At each decision node (square): choose best branch (maximum value).
5 Session # Page Using TreePlan to Analyze Oil Drilling Problem. Choose Decision Tree under the Tools menu.. Click on New Tree and it will draw a default tree with a single decision node and two branches, as shown below. A B C D E F G Decision Decision. Label each branch. Replace Decision with Drill (cell D). Replace Decision with Do not drill (cell D).. To replace the terminal node of the drill branch with an event node, click on the terminal node (cell F) and then choose Decision Tree under the Tools menu. Click on Change to event node, choose two branches, then click OK. TreePlan draws the tree below. A B C D E F G H I J K. Event Drill. Event Do not drill. Change the labels Event and Event to Wet and Dry, respectively.. Change the default probabilities (cells H and H) from. and. to the correct values of. and... Enter the partial payoffs under each branch: (-) for Drill (D), for Do not Drill (D), for Wet (H), and for Dry (H). The terminal value cash flows are calculated automatically from the partial cash flows.
6 Session # Page Final Decision Tree A B C D E F G H I J K. Wet Drill -. Dry - Do not drill - Features of TreePlan Terminal values (payoff) are calculated automatically from the partial payoffs (K=D+H, K=D+H, K=D). Alternatively, they can be entered directly (in which case the partial payoffs are ignored). Foldback values are calculated automatically (I=K, I=K, E=H*I+H*I, E=K, A=Max(D,D)). Optimal decisions are indicated inside decision node squares (labeled by branch number from top to bottom, e.g., branch # = Drill, branch # = Do not drill). Changes in the tree can be made by clicking on a node, and choosing Decision Tree under the Tools menu (change type of node, # of branches, etc.) Clicking Options in the Decision Tree dialogue box allows the choice of Maximize Profit or Minimize Cost.
7 Session # Page Making Sequential Decisions Consider a pharmaceutical company that is considering developing an anticlotting drug. They are considering two approaches. A biochemical approach would require less R&D and would be more likely to meet with at least some success. Some, however, are pushing for a more radical, biogenetic approach. The R&D would be higher, and the probability of success lower. However, if a biogenetic approach were to succeed, they would likely capture a much larger portion of the market, and generate much more profit. Some initial data estimates are given below. R&D Choice Investment Outcomes Profit (excluding R&D) Probability $ million Large success $ million. Small success $ million. $ million Success $ million. Failure $ million. A B C D E F G H I J K. Large Success -. Small Success. Success -. Failure - - All monetary amounts in millions of dollars.
8 Session # Page Simultaneous Development A B C D E F G H I J K L M N O P Q R S. Biogen (S). Biochem (LS). Biogen(F) Simultaneous... Biogen (S). Biochem (SS). Biogen(F) All monetary amounts in millions of dollars.
9 Session # Page First A B C D E F G H I J K L M N O P Q R S T U V W. Large Success. Success Pursue -. Failure First. -. Biogentic. Small Success. Success Pursue -. Failure Biogentic All monetary amounts in millions of dollars.
10 Session # Page First A B C D E F G H I J K L M N O P Q R S T U V W. Large Success Pursue -.. Small Success Success First.. -. Large Success Pursue -.. Small Success Failure - - All monetary amounts in millions of dollars.
11 Session # Page Whole Decision Tree A B C D E F G H I J K L M N O P Q R S T U V W. Success Large. Success Pursue -. Failure -.. Success Small. Success Pursue -. Failure. Success Large Pursue -. Success. Small Success. Success -. Large. Pursue -. Success. Small Failure - -. Biogen (S). (LS) Biochem. Biogen (F) Simultaneous.. Biogen (S). (SS) Biochem. Biogen (F) Don't Invest
12 Session # Page Decision Support System A B C D E Data BC Investment BC Large Success Profit BC Small Success Profit BC Large Success Probability. BG Investment BG Success Profit BG Failure Profit BG Probability of Success. Results Action: Expected Payoff (millions): $. First If Success Then Commercialize If Failure Then Pursue Data cells in decision tree spreadsheet (partial payoffs, probabilities) refer to data cells in front end spreadsheet. Results in front end spreadsheet refer to result cells in decision tree spreadsheet (decision node branch # s, payoff values) B C =IF(Tree!B=," First",IF(Tree!B=," Action: First",IF(Tree!B=,"Simultaneous","Don't =IF(Tree!B=,IF(Tree!J=," If Large Success Invest"))) then Commercialize "," If Large Success then Pursue "),IF(Tree!B=,IF(Tree!J=," If Success Then Pursue "," If Success Then Commercialize "))) "," If Small Success then Pursue "),IF(Tree!B=,IF(Tree!J=," If Failure Then Pursue "," If Failure Then Don't Pursue "))) Expected Payoff (millions): =Tree!A
13 Session # Page Using Data Tables to Plot Payoff vs. Probability of BG Success A B C D E F G H Data BC Investment BG Probability Expected Payoff BC Large Success Profit of Success ($millions) BC Small Success Profit. BC Large Success Probability... BG Investment.. BG Success Profit.. BG Failure Profit.. BG Probability of Success..... Results.. Action: First.. If Success Then Commercialize.. If Failure Then Pursue.. Expected Payoff (millions): $... Expected Payoff ($millions) BG Probability of Success H =C A Data Table can be used to generate Payoff vs. BG Success Probability. In the first line of the table (H), put an equation referring to the output cell of interest (in this case, =C for expected payoff). In the first column of the table (G:G), enter the data for the input cells (in this case, the probabilities, ranging from to ). Select the whole table (G:H), and then choose Table from the Data menu. Specify the column input cell as the input cell in the spreadsheet that will be changing (as represented by the data in the first column of the table). In this case, this is the BG probability of success, in cell C.
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