### Create a StudySoup account

#### Be part of our community, it's free to join!

Already have a StudySoup account? Login here

# Class Note for EMSE 269 with Professor Dorp at GW (9)

### View Full Document

## 21

## 0

## Popular in Course

## Popular in Department

This 58 page Class Notes was uploaded by an elite notetaker on Saturday February 7, 2015. The Class Notes belongs to a course at George Washington University taught by a professor in Fall. Since its upload, it has received 21 views.

## Reviews for Class Note for EMSE 269 with Professor Dorp at GW (9)

### What is Karma?

#### Karma is the currency of StudySoup.

#### You can buy or earn more Karma at anytime and redeem it for class notes, study guides, flashcards, and more!

Date Created: 02/07/15

Makin ar 1 Decisions R T Clemen T Reilly Draft Versmn 1 Chapter 4 Making Choices Making Hard Decisions R r Ciemen i ReHW Chapter 4 Making Choices Slide 1 of 58 Le meNmesbv m vanDamandYA Mazzucm copvmwmuus millvwwvseasngedul39vdamn bvevvu Texaco Versus Pennzoil In early 1984 Pennzoil and Getty Oil agreed to the terms of a merger But before any formal documents could be signed Texaco offered Getty a substantially better price and Gordon Getty who controlled mos of the Getty Stock reneged on the Pennzoil deal and sold to Texaco Naturally Pennzoil felt as if it had been dealt with unfairly and immediately files a lawsuit against Texaco alleging that Texaco had interfered illegally in the Pennzoil Getty negotiations Pennzoil won the case in late 1985 it was awarded 111 billion the largest judgment ever in the United States A Texas appeal court reduced the judgement to 2 billion but interest and penalties drove the total back up to 103 billion James Kinnear Texaco s Chief executive officer had said that Texaco would file for bankruptcy if Pennzoil obtained court permission to secure the judgment by filing liens against Texaco s assets Draft Version 1 39 Making Hard Decisions Chapter 4 Making Choices Slide 2 of 58 R r cremenr Reilly Le ureNmesby m vanDamandiA Mazzuchr conchHrezuus mplvwwvseasngeduldavplvl hyevvu Texaco Versus Pennzoil Continued Draft Version 1 Furthermore Kinnear had promised to fight the case all the way to the US Supreme Court if necessary arguing in part that Pennzoil had not followed Security and Exchange Commission regulations in its negotiations with Getty In April 1987 just before Pennzoil began to file liens Texaco offered to Penzoil 2 billion dollars to settle the entire case Hugh Liedtke chairman of Pennzoil indicated that his advisors were telling him that a settlement between 3 billion and 5 billion would be fair What should Hugh Liedtke do 1 Accept 2 Billion 2 Refuse 2 Billion and counter offer 5 Billion Making Hard Decisions Chapter 4 Making Choices Slide 3 of 58 R r Clemenll My Le meNmesby m vanDamandlA Mazzuchi mplvwwvseasngeduldamlvl copvmew e zuus byGWU Texaco Versus Pennzoil DeciSIon Tree Max Semement Amount BHhon Accept2 Bunon 9 2 Texaco Accepts 5 BHMOH 5 H h g 10 3 Counteroffer Texaco Refuses Fwd Com Medmm 5 5 BHhon counteroffer V Dec g o LOW 0 H h g 10 3 Refuse was Com Medium 5 Decisio a E Texaco 0 g Counterr g offers 3 BHhon Accept 3 Buhon 3 Making Hard Decisions Chapter 4 Making Choices Slide 4 of 58 r C emen i Remy Le uveNmesbv m mommys Mazzucm covvmewezuus mPlwwwseasngedul39vdamn bvevvu Texaco Versus Pennzoil Continued Given tough negotiation positions ofthe two executives their could be an even chance 50 that Texaco will refuse to negotiate further Liedtke and advisor gure that it is twice as likely that Texaco would counter offer 3 billion than accepting the 5 billion Hence because there is a 50 of refusal there must be a 33 chance of a Texaco counter offer and a 17 chance of Texaco accepting 5 billion What are the probabilities of the final court decision Liedtke admitted that Pennzoil could lose the case Thus there is a significant possibility the outcome would be zero It s probability is assessed at 30 Given the strength ofthe Pennzoil case it is also possible that the court will upheld the judgment as it stands It s probability is assessed at 20 Finally the possibility exists that the judgment could be reduced somewhat to 5 billion Thus there must be a chance of 50 of this happening Draft Version 1 39 Making Hard Decisions Chapter 4 Making Choices Slide 5 of 58 R r Clemenl Reilly Le uveNmesby m vanDamandlA Mazzuchl conRlGHrezuus mplvwwvseasngeduldavplvl bvcvvu Texaco Versus Pennzoil Continued Given tough negotiation positions ofthe two executives it could be an even chance 50 that Texaco will refuse to negotiate further Liedtke and advisor gures that it is twice as likely that Texaco would counter offer 3 billion than accepting the 5 billion Hence because there is a 50 of refusal there must be a 33 chance of a Texaco counter offer and a 17 chance of Texaco accepting 5 billion What are the probabilities of the final court decision Liedtke admitted that Pennzoil could lose the case Thus there is a significant possibility the outcome would be zero It s probability is assessed at 30 Given the strength ofthe Pennzoil case it is also possible that the court will upheld the judgment as it stands It s probability is assessed at 20 Finally the possibility exists that the judgment could be reduced somewhat to 5 billion Thus there must be a chance of 50 of this happening Draft Version 1 39 Making Hard Decisions Chapter 4 Making Choices Slide 6 of 58 R r Clemenl Reilly Le uveNmesbv m vanDamandlA Mazzuchl conRlGHrezuus mplvwwvseasngeduldavplvl bvcvvu Texaco Versus Pennzoil Decision Tree Max Semement Amount Accept 2 BHHOH B HTSH Texaco Accepts 5 BTHTon o 17 HT h 020 9 103 Texaco Refuses o 50 PM Com Medmm 0 so 5 DecTsTon 5 BTHTon Counteroffer LOW 0 30 HT h 0 20 g m 3 Refuse FmaT Com MedTum 0 50 5 DecTsTon 2 E Texaco 0 g Counterr 0 33 f offers 3 BTHTon Q Accept 3 BTHTon 3 Making Hard Decisions Chapter 4 Making Choices Slide 7 of 58 x v Ciemen i Remy Le uveNmeshv m mommya Mazzucm conRTGHTozuus Niplvwwvseasngedul39vdamivi bvevvu Decision Tree and Expected Monetary Value EMV When objective is measured in dollars 1 First Suggestion Solve decision problem by choosing that alternative that maximizes the EMV Expected value of discrete random variable Y EYY Zyi PrY yi Zyi pi i1 i1 Draft Version 1 Making Hard Decisions Chapter 4 Making Choices Slide 8 of 58 r Clemen l Reilly Le uveNmesbv J m Mazzuchi conRchrezuus vaWU Ad oublerisk dillema Draft Version 1 y PrYy y PrYy M 2400 02 480 Win 020 100 08 O80 EMV 4 25 24 400 EMV Trade Ticket T EMV 45 31 0 Win 045 10 I EMV 45 10 Keep Ticket 0 y PrYy yPrYy Lose 055 0 1000 045 450 0 000 055 000 450 EMV Interpretation EMV Playing the same lottery a lot oftimes will result over time in an average payoff equal to the EMV Making Hard Decisions R r clemenr ReilW Chapter 4 Making Choices Le ureNmesbv m mommy4 Mazzuchi mtplvwwvseasngeduldamlv Slide 9 of 58 conRlGHr 2005 hvGWU Texaco Versus Pennzoil Decision Tree M ax Semem em Amount Accept 2 Bthon Btg on Texaco Accepts 5 Bthon 0 t7 5 Ht h o 20 g 10 3 Counteroffer Texaco Refuses o 50 PM Com Mew 0 50 5 5 Bthon Counteroffer Demo LOW 0 30 o Htgh o 20 3 23235 lt0 33gt E offers 3 Bthon g Accept 3 Bthon 3 Lquot Q Solve tree using EMV by folding back the tree Making Hard Decisions Chapter 4 Making Choices Slide 10 of 58 Y Ciemen i Remy Le uveNmesbv m vanDamandYA Mazzucm comtcwezoos Niplvwwvseasngedul39vdavmvi hvcvvu Decision Tree and Expected Monetary Value EMV Step 1 Calculate EMV of court decision uncertainty node V High 020 EMV 456 Final Court Decision Medium 050 Low 030 Step1 Y PFYY YF FYY 10300 02 206 g 5000 05 250 2 0000 03 000 5 4 56 EMV 39 Making Hard Decisions Chapter 4 Making Choices Slide 11 of 58 R r clemem ReilW Le uveNmesbv m vanDamandlA Menu2m conRlGHrezuus mPlwwseasngeduldamlv bvevvu Decision Tree and Expected Monetary Value EMV Draft Version 1 Step 2 Evaluate decision regarding Texaco s counter offer EMV 456 High 020 103 Refuse Final Court MSdium 050 Decision EMV 456 5 Low 030 Accept 3 Billion 39 Making Hard Decisions Chapter 4 Making Choices rammest m vanDavpandlA Mazzucm Niplvwwvseasngedul39vdamlv Slide 12 of 58 copvmew e zuus vaWU R r clemen r ReilW Decision Tree and Expected Monetary Value EMV Step 3 Calculate EMV Texaco s reaction uncertainty node V WWW WPWW 5 000 0 17 0 85 4 560 0 5 2 28 Accept 2 BIIIIon 2 4 560 0 33 1 50 4 3 EMV Texaco Accepts 5 Billion 017 5 EMV 463 EMV Texaco Re Jses 050 Countero er 5 Billion EMV Texaco 43956 Counter offers 3 Billion 033 Draft Version 1 Making Hard Decisions Chapter 4 Making Choices Slide 13 of 58 R r clemen r Reilly Le ureNmesbv m vanDamandTA Mazzucm conRiGHrezuus mpwmvseasngeduldamlv bvevvu Decision Tree and Expected Monetary Value EMV Step 4 Evaluate the immediate decision Max Result Accept 2 Billion EMV 463 EMV 463 Counteroffer 0 5 Billion Optimal decision Counteroffer 5 Billion Optimal decision strategy Counteroffer 5 Billion and if Texaco counteroffers 3 Billion then refuse this counteroffer Draft Version 1 39 Making Hard Decisions Chapter 4 Making Choices Slide 14 of 58 R r Clemen f Reilly Le uveNmesbv m vanDamande Mazzuchi conRiGHrezuus mplwmvseasngeduldavplv bvevvu Folding back the Decision Tree from right to left using EMV Draft Version 1 Max Result Accept 2 Billion EMV 2 463 Texaco Accepts 5 Billion 017 EMV EMV High 020 10 3 453 456 39 Counteroffer Texaco Re Jses 050 Final Court Medium 050 5 5 Billion Countero er Decision Low 030 0 EMV 456 High 020 103 Final Court Medium 050 eclslon EMV 456 5 Low 030 Texaco 0 Counter 03933 offers 3 Billion Accept 3 Billion 3 Making Hard Decisions Chapter 4 Making Choices Slide 15 of 58 r clemem Reilly Le uveNmesbv m vanDamandlA Mazzucm copvmew e 2005 mm wwwseas ng 200mm bv cvvu Definitions Decision Path and Strategy Definition decision path A path starting at the left most node up to the values at the end of a branch by selecting one alternative from decision nodes or by following one outcome from uncertainty nodes Represents a possible future scenario Definition decision strategy The collection of decision paths connected to one branch of the immediate decision by selecting one alternative from each decision node along these paths Represents specifying at every decision in the decision problem what we would do if we get to that decision we may not get there due to outcome of previous uncertainty nodes Optimal decision strategy That decision strategy which results in the highest EMV if we maximize profit and the lowest EMV if we minimize cost Draft Version 1 a Making Hard Decisions Chapter 4 Making Choices Slide 16 of 58 a r Clememl Reilly Le ureNmeshv JR vanDamandlA Mazzuchi conRiGHrezuus Niplvwwvseasngeduldamlvl hvevvu Counting Strategies How many decision strategies in Example 1 a How many decision strategies in Example 2 Example 1 N 2 a 2 g 4 gtlt g LIJ ii a a 3 Making Hard Decisions Chapter 4 Making Choices Slide 17 of 58 R i element MW Lawyengst m MWA Mum conRleHvezuus MpiiweBSEWedudaypyi WW Counting Strategies How many decision strategies in Example 3 Example 3 39 E E g a a 6 Making Hard Decisions Chapter 4 Making Choices Slide 18 of 58 R r clemen r RelllV Le meNmesbv m vanDamandlA Mazzucm copvmwmuus Niplvwwwseasngedul39vdamlvl hvevvu Counting Strategies How many decision strategies in Example 1 E Strategy 1 239 Strategy 2 a St t 3 5 ra egy How many decision strategies in Example 2 N Strategy 1 g 1 Strategy 2 1 1 a a E 0 Strategy 3 00 g E 1 Strategy 4 l0 E 0 Strategy 5 01 Making Hard Decisions Chapter 4 Making Choices Slide 19 of 58 h R r clemen r Rellly Le meNmesbv J M zzzz cl conRlGHrezuus MPIvwwv y bv ewu Counting Strategies How many decision strategies in Example 3 Strategy 1 Strategy 2 111 1 Strategy 3 001 Strategy 4 101 Strategy 5 011 Strategy 6 110 1 0 Strategy 7 000 Strategy 8 100 Strategy 9 010 Example 3 Draft Versmn 1 3 Making Hard Decisions Chapter 4 Making Choices Slide 20 of 58 R y mm W Lawyengst H mm Mum covvmewezuus WW Decision Strategies TexacoPennzoil Case How many decision strategies do we have in the Texaco Penzoil decision tree First strategy Accept 2 billion Accept 2 Billion Draft Version 1 39 Making Hard Decisions Chapter 4 M ing Choices Slide 21 of 58 Lena R r Clem r Reilly W m conRchrezuus hvGWU Draft Version 1 Decision Strategies TexacoPennzoil Case Second strategy Counter 5 billion and if Texaco counter offers 3 billion refuse this counteroffer of 3 Billion Texaco Accepts 5 Billion 017 High 020 103 Texaco Refuses 050 Final Court Medium 050 5 DecIsIon Counteroffer Low 0 30 39 0 O o a 2 9g 2 5 Billion High 020 103 Re Jse Fina Court Medium 050 5 Decision Low 030 Texaco 0 Counter 03933 offers 3 Billion 39 Making Hard Decisions Chapter 4 Making Choices Slide 22 of 58 R r clemem Reilly Le ureNmesbv m vanDamandTA Mamcm copvmew 2005 mm lvwwvseas ng 200mm bv cvvu Decision Strategies TexacoPennzoil Case Third strategy Counter 5 billion and if Texaco counter offers 3 billion accept this counteroffer of 3 Billion Texaco Accepts 5 Billion 017 High 020 103 Counteroffer Texaco Refuses 050 AKFinal Court Medium 050 5 5 Billion 39 Counteroffer Deuswn Low0 30 I 0 Texaco 03933 Counter offers 3 Billion Accept 3 Billion 3 Draft Version 1 39 Making Hard Decisions Chapter 4 Making Choices Slide 23 of 58 R r clemen r Reilly Le ureNmesbv m vanDamandTA Mamcm conRchrezuus Niplvwwvseasngedul39vdamn bvcvvu Risk Profiles and Cumulative Risk Profiles RISK PROFILES Graph that shows probabilities for each of the possible outcomes given a particular decision strategy Note Risk Profile is a probability mass function for the discrete random variable Y representing the outcomes for the given decision strategy CUMMULATIVE RISK PROFILES Graphs that shows cumulative probabilities associated with a risk profile Note Cumulative risk pro le is a cumulative distribution function for the discrete random variable Y representing the outcomes for the given decision strategy Draft Version 1 39 Making Hard Decisions Chapter 4 Making Choices Slide 24 of 58 R r Clemen l Reilly Le meNmesbv m vanDamandlA Mamcm copvmewezuus mplwmvseasngeduldavplvl bvcvvu Risk Profiles Draft Version 1 First strategy Accept 2 billion Accept 2 Billion Outcomex Billion PrOutcom elD RI lt Pro le 2 1 DquotAccept2 Ellllonquot 1 9 u a 5 u E s 5 m r l u 2 u 4 2 5 a M Outcome sannom Making Hard Decisions Chapter 4 Making Choices Slide 25 of 58 R r Ciemen l REHW Le uveNmesbv m vanDamandlA Mamcm copvmew zuus mp lvwwvseas ng eduldavmn bv ewu Risk Profiles Second strategy Counter 5 billion and if Texaco counter offers 3 billion refuse this counteroffer of 3 Billion calculatlon Prob Texaco Accepts 5 Biiiion 0 i7 5 El 17 E17E Hi n 020 10 3 El 5E7 El 2E E1EE Finai Counteroff Texaco Refuses o 50 Medmm 0 50 5 D Em an D m 5 Biiiion counteroffer Decision Low 0 30 0 Batman o iso to 3 n BTU 2n n DEE Medium 0 50 5 usamsu u iss a E Decision g Texaco LOW 0 30 0 n aim in n ma g Counter 5 offers 3 Biiiion Tm mun 3 Making Hard Decisions Chapter 4 Making Choices Slide 26 of 58 R i om Rein ieuuieuuessi is New mun conRiGHiozuus MPIvwwvseasngeduldamiv hvGWu Risk Profiles Draft Version 1 39 Making Hard Decisions Second strategy Counter 5 billion and if Texaco counter offers 3 billion refuse this counteroffer of 3 Billion Outcome x Billion Calculation PrOutcome D 0 01500099 0249 5 017002500165 0585 103 01000066 0166 1 000 Ri Pro le DquotCountef 5 Billion refuse counter offer of3 Bquot quoton if givenquot PrtOutcomel D Outcome Billion Chapter 4 Making Choices Le uveNmeshv m venDavpandiA Menu2m mpvwvwseasngedudavmn Slide 27 of 58 copvmew zuus vaWU R r memem ReiiW Risk Profiles Third strategy Counter 5 billion and if Texaco counter offers 3 billion accept this counteroffer of 3 Billion Calculatlon Prob Texaco Acce t5 5 BiHion 017 5 n17 n 17m 103 DEU UZU UWUU Finai Counterof Texaco Refuses 0 50 M dmm 0 0 61 BiHion Counteroffer 5 Dan man man Decision LOW 030 Texaco 0 33 0 n sn n an n 15D Counterr g offers 3 BHMOH E Acce l 3 BHMOH 3 D 33 D 33D 1 Total 1 mm Lquot Q 3 Making Hard Decisions Chapter 4 Making Choices Slide 28 of 58 R i mm W Lawyengst m 0mm mm conRieHrezuus MilWeaswwwi WW Risk Profiles Draft Version 1 Third strategy Counter 5 billion and if Texaco counter offers 3 billion accept this counteroffer of 3 Billion Outcome x Billion Calculation PrOutcome D 0 015 015 3 033 033 5 01700250 042 103 01 01 1000 Risk Pro le DquotCounller 5 Billion Accept Counter Olrer of 3 Billion if givenquot 1 g 08 7 06 7 5 04 E 0 2 39 010 0 I 1 2 5 8 1 1 Ouocome Billion Making Hard Decisions Chapter 4 Making Choices Slide 29 of 58 R r clenenr Reilly Le uveNmesbv m venDamendlA Mezzuchl conRloHl e was mp WNWsees ng ewwavpm bv ewu Cumulative Risk Profiles First strategy Accept 2 billion RIskPro le DquotAccept 2 Ellllonquot Outcome X Billion PrOutcomeD 1 D u a 2 1 E o u a s 5 m r l u 2 u 71 2 5 a M outcome sannom Outcome X Billion PrOutcome S XD quot39quotquot39 V R39s quotWe 2 1 DquotAccept2 Ellllonquot 1 a X El E v E n 6 v 8 S 3 A E r u 2 W 77777777777777777777777777777777777 H u L 2 n o g 4 1 a 5 7 a M Outcome SEIIIIOrlj Making Hard Decisions Chapter 4 Making Choices Slide 30 of 58 r Clemen l Remy Le uveNmesbv m vanDavpandYA Mazzucm covlecHrezuus Niplvwwvseasngedul39vdamn bvcvvu Cumulative Risk Profiles Draft Version 1 Second strategy Counter 5 billion and if Texaco counter offers 3 billion Rlsk Proflle Dquotcounter5 annon refuse courmer offerofxf annon Ifglvenquot l r 777777777777777777777777777777777777 w refuse thls counteroffer of 3 BIIIIon g D E r V a 503 e 3 904 r r Outcomex Billi0n PrOutcomeD D Z r 0 0249 D o166 5 0585 1 2 5 0 M 103 0166 oumome SEllllonj Outcome X Billion PrOutcome S XD cumulatlve Rlsk Froflle Dquotcourmer5 Elmoquot refuse counter offerofxf Ellllon If glverlquot E 0 0249 g 5 0249 0585 0834 g 103 083401661 g E l 2 5 E M Outcome sannom Making Hard Decisions Chapter 4 Making Choices Slide 31 of 58 r cfemenr Reilly rammest m vanDamandlA Menu2m covleeHrezuus mpiwwseasngeduldavmvl bvevvu Cumulative Risk Profiles Third strategy Counter 5 billion RI lt Pro le rquotcounterss Ellllorl Acceptcounter and if Texaco counter offers 3 billion m quot quotquot quot quot accept this counteroffer of 3 Billion E l quot quot a u a E Outcomex Billion PrOutcom elD g D B o 015 g D 4 3 033 D 1 5 03942 D l 2 a ll 10393 0391 outcome SEIIIIonl cumulative RI lt Pro le quotcounter 5 Ellllon accept counter offer of Ellllon If glverlquot Outcome X Billion PrOutcome S XD 1 0 015 2 n E 3 o1503304a gm 5 048 042 090 g c 103 090o101 g g U2 g El 1 71 2 5 E M g outcome SEIIIIonl Making Hard Decisions Chapter 4 Making Choices Slide 32 of 58 r Clemen Rellly reutcumesw m vanDavpanulA Menu2m conRleHrezuus mPlwwseasngeduidavplvi hvevvu Deterministic Domlnance Original Tree nus nnnn 2nnn Accepi 32 BMW neuan mas Yexaca mm 35 Buhan on was Cmmlem ev 5 ammn Yemca emsescaumem ev ms 1nJnn n1ss am a E n1nn g nnnn a 33 quotEmquot Lu Assn Q nnnn am am Making Hard Decisions Chapter 4 Making Choices Slide 33 of 58 R v Ciemen i Remy Le uveNmesbv m vanDavpandiA Mazzucm covvmew 2uus Niplvwwvseasngedul39vdamn bvevvu Deterministic Domlnance Modified Tree Ancemsz han L5 nnnn 2nnn neclslnn 5251 Yemen Amem 5 Buhan um 5nnn Caumem ev 35 mm on 5251 m hAv avd quot1 m Om 5 Medium Award quot2 5m n15n 25nn Medium Award quot155 E mquot a 5 22 gt 33 n I Yemen Caumem evs 3 ammn EDInquot Q nnnn 3n mu Making Hard Decisions Chapter 4 Making Choices Slide 34 of 58 R v Ciemen i Remy Le uveNmesbv m mommy5 Mazzucm covvmew 2uus mpvwwvseasngeduldamyv bvevvu Draft Version 1 Deterministic Dominance Based on EMV analysis we still choose the alternative Counteroffer 5 Billion Max Result Accept 2 Billion EMV 526 EMV 526 Countero er 0 5 Billion Could we have made a decision here without an EMV analysis 39 Making Hard Decisions Chapter 4 Making Choices Le uveNmesbv m vanDavpandlA Mazzucm mpivwwvseasngedudavpyv Slide 35 of 58 copvmew zuus vaWU R r clemen r RellW Deterministic Dominance Formal Definition Deterministic Dominance If the worst outcome of Alternative B is at least as good as that of the best outcome of Alternative A then Alternative B deterministically dominates Alternative A Deterministic dominance may also be concluded by drawing cumulative risk pro es and using the de nition Definition Range of a Cumulative Risk Profile LU where L Smallest 0 point in Cumulative Risk Pro le and U Largest 100 point in Cumulative Risk Profile Draft Version 1 E Making Hard Decisions Chapter 4 Making Choices Slide 36 of 58 i a r Clememl Reilly Le uveNmesbv 1 YA M zzzz ai conRicHrezuus Niplvwwv m r hvevvu Deterministic Dominance Draft Version 1 Deterministic dominance via cumulative risk profiles Step 1 Draw cumulative risk profiles in one graph Step 2 Determine range for each risk profile Step 3 If ranges are disjoint or their intersections contain a single point Cumulative Risk Pro les Revised TexacoPenzoil Case PrOutcome S x Outcome Billion Accept 2 Billion Counteroifer 5 Billion and Re ise 3 Billion Range 1 2 Range 2 25103 Ranges 1 and 2 are disjoint The Objective is Max Result hence Green CRP deterministically dominates the Red one Making Hard Decisions R r Clemen i ReilW Chapter 4 Making Choices Le ureNmesbv m vanDamandiA Mazzuchi MPIWwwseasngedul39vdamm Slide 37 of 58 conRicHr 2uus vaWU Stochastic Dominance Example 1 Firm A Original Tree Accept 2 Swan 5 quot39quot 2nnn neuan mas p enzerYexaca Yexaca mm 35 Buhan caumgmnms ammn 39 muss 1n1nn n1ss am a E n1nn g nnnn a quotEmquot Lu Assn Q nnnn am am Making Hard Decisions Chapter 4 Making Choices Slide 38 of 58 R v Ciemen i Remy Le uveNmesbv m vanDavpandYA Mazzucm conRichozuus Niplvwwvseasngedul39vdamn bvcvvu Stochastic Dominance Example 1 Firm B Modified Tree nus nnnn 2nnn AccepiSZBiHmn neclslnn ma p enmiiiriexaca Yemen Accept 5 Buhan um 5nnn Caumem ev 35 mm 39 on m a High mm n1nn Yexaca Refuses Cmmlem ev nnss 1n1nn n1ss 52nn a E mu g nnnn a quotEMquot 5 mm D nnnn an inn Making Hard Decisions Chapter 4 Making Choices Slide 39 of 58 R v Ciemen i Remy Le uveNmesbv m vanDavpandYA Mazzucm conRichozuus Niplvwwvseasngedul39vdamn bvcvvu Stochastic Dominance Example 1 Based on EMV analysis we still choose the alternative Firm B Max Result EMV 463 Firm A EMV 472 EMV 472 O Could we have made a decision here without an EMV analysis Draft Version 1 39 Making Hard Decisions Chapter 4 Making Choices Slide 40 of 58 R r Clemen l Reilly Le ureNmesbv m vanDamandlA Mazzucm copvmewezuus mplsteasngedudamlvl bvcvvu Stochastic Dominance Example 1 Optimal Cumulative risk profiles in Firm Aquot Tree and Firm Bquot Tree Cumulative Risk Pro les Firm A and Firm B 3 VI I E c U S 9 l n 1 2 5 8 11 E g Outcome Billion gt E Firm A Firm B 6 Making Hard Decisions Chapter 4 Making Choices Slide 41 of 58 r Clemen l Reilly Le uveNmeshv m vanDavpandYA Menu2m conRicHrezuus Niplvwwvseasngedul39vdamn bvcvvu Stochastic Dominance Example 1 Cumulative Ri Pro les Firm A and Firm B Note that for all possible values of x PrOutcome S x Firm B S PrOutcome S x Firm A Pr0utcomes x or equivalently r1 2 5 3 11 Outcome Billion PrOutcome 2 x Firm B 2 PrOutcome 2 x Firm A Firm A Firm B Hence the chances of winning with Firm B are always better than that of Firm A Draft Version 1 Conclusion Firm B stochastically dominates Firm A 39 Making Hard Decisions Chapter 4 Making Choices Slide 42 of 58 R v Clemen l Reilly Le uveNmeshv m vanDavpandYA Mazzucm conRichezuus mplvwwvseasngedudavmvl bvcvvu Stochastic Dominance Example 2 Firm A Original Tree Accept 2 Swan 5 quot39quot 2nnn neuan mas p enzerYexaca Yexaca mm 35 Buhan caumgmnms ammn 39 muss 1n1nn n1ss am a E n1nn g nnnn a quotEmquot Lu Assn Q nnnn 3 n ma Making Hard Decisions Chapter 4 Making Choices Slide 43 of 58 R v Ciemen i Remy Le uveNmesbv m vanDavpandYA Mazzucm conRichozuus Niplvwwvseasngedul39vdamn bvcvvu Stochastic Dominance Example 2 Firm C Modified Tree nus nnnn 2nnn AccepiSZBiHmn neclslnn mus p enmiiiriexaca Yemen Accept 5 Buhan um 5nnn caumgyangyss sum 39 on mans n15n 1n1nn High mm on 52 Yexaca Refuses Cmmlem ev Medium Award Hi h Amid 391 m 52 Medium Award 391 52nn nma 3 nnnn E 33 quotEmquot g 52m nnnn Lquot 3 u annn Q Making Hard Decisions Chapter 4 Making Choices Slide 44 of 58 R v Ciemen i Remy Le uveNmesbv m vanDavpandYA Mazzucm conRichozuus Niplvwwvseasngedul39vdamn bvcvvu Stochastic Dominance Example 2 Based on EMV analysis we still choose the alternative Firm C Max Result EMV 463 Firm A EMV 600 EMV 600 O Could we have made a decision here without an EMV analysis Draft Version 1 39 Making Hard Decisions Chapter 4 Making Choices Slide 45 of 58 R r Clemen l Reilly Le ureNmesbv m vanDamandlA Mazzucm copvmewezuus mplsteasngedudamlvl bvcvvu Stochastic Dominance Example 2 Optimal Cumulative risk profiles in Firm Aquot Tree and Firm Cquot Tree Cumulative Risk Pro les Firm A and Firm C 3 VI I E o E 1 9 l n F 1 2 5 a 11 g Outcome Billion gt g Firm A Firm C 6 Making Hard Decisions Chapter 4 Making Choices Slide 46 of 58 r Clemen l Reilly Le uveNmeshv m vanDavpandYA Menu2m conRicHrezuus Niplvwwvseasngedul39vdamn bvcvvu Stochastic Dominance Example 2 Cumulative Ri Pro les Firm A and Firm C Note that for all possible values of x PrOutcome S x Firm C S PrOutcome S x Firm A x S 5353 was PrlOutcome o o 39N 39s or equivalently 71 2 5 8 11 PrOutcome 2 x Firm C 2 mm mmquot PrOutcome 2 x Firm A Firm A Firm C Hence the chances of winning with Firm C are always better than that of Firm A Draft Version 1 Conclusion Firm C stochastically dominates Firm A 39 Making Hard Decisions Chapter 4 Making Choices Slide 47 of 58 R v Clemen l Reilly Le uveNmeshv m vanDavpandYA Mazzucm conRichezuus mplvwvwseasngeduldamyvl bvcvvu Stochastic Dominance Examples 1 amp 2 cumulative Rlsk Pro les Flrm A and Flml a Commonality CRP plots i VI DE Cumulative risk profiles in Us both plots do not cross El The CRP that is toward the quot 2 right and below F quot A F quot E stochastically dominates swimRemiesmmtmhmc i The objective in both plots E is to Maximize the Result 33m E E 2 What ifthe objective is 2 s a i Minimize the Result W 3 Making Hard Decisions Chapter 4 Making Choices Slide 48 of 58 R r Clememl Reilly Le uveNmeshv m vanDamandlA Mazzuchi COPVRiGHY 2EIEIB Niplvwwvseasngeduldamiv hvevvu Making Decisions with Multiple Objectives Two Objectives Making Money Measured in Having Fun Begs Measured on Constructed attribute scale see page 138 Best5 Good4 Midde3 Bad2 Worst 1 Draft Version 1 Making Hard Decisions Chapter 4 Making Choices Slide 49 of 58 h r Ciemerv i Remy Le meNmesbv J m M zzzz cr copvmewezuus Niplvwwv m y bvcvvu Making Decisions with Multiple Objectives Consequences 39l Salary Fun Level 5 010 260000 5 260000 4 Forest Job 260000 3 260000 2 1 005 260000 1 hours perweek 273000 3 2 g 39 39T W J b 34 hours 050 e 232050 3 5 204750 3 Making Hard Decisions Chapter 4 Making Choices Slide 50 of 58 v ClemenJ Reilly Le uveNmeshv m vanDavpandlA Mazzucm covmewmuua Niplvwwvseasngedul39vdamn bvevvu Analysis Salary Objective Draft Version 1 Forest Job lnTown Job Salary Prob SalaryProb Prob SalaryProb 204750 015 30713 232050 050 116025 260000 100 260000 273000 035 95550 ESalary 260000 ESalary 242288 Concluswn Forest Job preferred Over InTown job CRP s cross Hence No Stochastic Dominance Pr0utcomes x 2000 2200 2400 2600 2800 3000 ForeSl Job Salary rrTown Job Making Hard Decisions r Harmful REHW Chapter 4 Making Choices Le meNmesbv m vanDamandlA Mazzucm mp lvwwvseas ng 200mm Slide 51 of 58 copvmwezuus vaWU Fun Level Objective Forest Job lnTown Job Outcome Fun Level Prob Fun LevelProb Prob Fun LevelProb 5BEST 10000 010 100 4 GOOD 9000 025 225 3 MIDDLE 6000 040 240 100 6000 2 BAD 2500 020 50 1 WORST 000 005 00 EFun Level 615 EFun Level 6000 Conclusion 1 Forest Job preferred a v 08 Over InTown ob g o 06 8 y g 04 CRP 5 cross Hence E 02 No Stochastic 0 g Dominance 0 20 40 60 80 100 120 E Fun Level E Foresl Job lanown Job Making Hard Decisions Chapter 4 Making Choices Slide 52 of 58 r CiemenJ Remy Le uveNmesbv m vanDavpandYA Mazzucm covvRicHrezuus Niplvwwvseasngedul39vdamn bvcvvu Multiple Objective Analysis It is clear from both objective analyses that the ForestJob is the strongly preferred although neither Stochastic nor Deterministic Dominance can be observed in them Careful as you are in your decisions you decide to tradeoffthe salary objective and having fun objective in a multiple objective analysis Before tradeoff analysis can be conducted both objectives have to be measured on a comparable scale 2 t gt ti Lquot o Making Hard Decisions Chapter 4 Making Choices Slide 53 of 58 r Ciemen Remy Le uveNmeshv J m Mazzuchi conRicHrezuus hvGWU Multiple Objective Analysis Construct 01 Scale Having Fun Objective already has a 01 scale Transformed to 0 1 scale or 0 100 scale Set Best100 Worst0 Determine intermediate values Having Fun objective Best100 Good90 Middle60 Bad25 Worst 0 Construct 01 scale for Salary Objective 273000100 2047500 Intermediate dollar amount X X 204750 2730 204750 Draft Version 1 100 39 Making Hard Decisions Chapter 4 M ing Choices Slide 54 of 58 Lena r R r Clem r Reilly W m copvmewezuus hvGWU Multiple Objective Analysis Assess TradeOff ks weight for salary kf weight for fun kskf1a Using Expert Judgment Going from worst to best in salary objective is 15 times more important than going from worst to best in having fun objective Hence k 15kf k 2 h k5kf1ltZgt 15kfkf1ltgt f 25 5 kszlS39k kszlS39k 6523222 g 2 5 5 Making Hard Decisions Chapter 4 Making Choices Slide 55 of 58 R r cwemen r Remy Le ureNmesbv A Mazzuchr copvmew mums y bvcvvu Multiple Objective Analysis Convert Scales Draft Version 1 Consequ ences ll Salary 06 5010 81 Fun 81 Forest Job 81 81 1 005 81 hours 100 InTOWn Job 34 hours 050 40 0 Fun Level 04 100 90 60 25 0 60 60 60 Making Hard Decisions r Ciemen i REHW Chapter 4 Making Choices Le meNmesbv m vanDamandYA Menu2m Nipwwseasngedul39vdavmv Slide 56 of 58 copvmew e 2005 hvGWU Multiple Objective Analysis Combine Objectives Total Score 5 010 886 F quot 040 Forest Job 726 586 1 005 486 hours 840 E InTovm Job g 480 a a E 240 Making Hard Decisions Chapter 4 Making Choices Slide 57 of 58 v Ciemen Remy Le uveNmesbv m vanDavpandYA Menu2m covvRieHvezuus Niplvwwvseasngedul39vdamn bvevvu Analysis Overall Satisfaction Forest Job lnTown Job Overall Satisfaction Prob OSProb Overall Satisfaction Prob OSProb 8857 010 89 8400 035 2940 8457 025 211 4800 050 2400 7257 040 290 2400 015 360 5857 020 117 EIOS 5700 4857 005 24 EIOS 732 Conclusion 1 Forest Job preferred g 08 VI 39 Over InTown JOb g o 06 8 g 04 CRP s do not cross a 02 5 Hence Stochastic 0 Dominance present 0 20 40 60 30 100 2 Overall Sati action g Fore5 Job rrTown Job Making Hard Decisions Chapter 4 Making Choices Slide 58 of 58 i am Rem Lemngst m mmm Mamm covlecHrezuus Niplvwwvseasngedul39vdamn bvcvvu

### BOOM! Enjoy Your Free Notes!

We've added these Notes to your profile, click here to view them now.

### You're already Subscribed!

Looks like you've already subscribed to StudySoup, you won't need to purchase another subscription to get this material. To access this material simply click 'View Full Document'

## Why people love StudySoup

#### "I was shooting for a perfect 4.0 GPA this semester. Having StudySoup as a study aid was critical to helping me achieve my goal...and I nailed it!"

#### "I made $350 in just two days after posting my first study guide."

#### "I was shooting for a perfect 4.0 GPA this semester. Having StudySoup as a study aid was critical to helping me achieve my goal...and I nailed it!"

#### "Their 'Elite Notetakers' are making over $1,200/month in sales by creating high quality content that helps their classmates in a time of need."

### Refund Policy

#### STUDYSOUP CANCELLATION POLICY

All subscriptions to StudySoup are paid in full at the time of subscribing. To change your credit card information or to cancel your subscription, go to "Edit Settings". All credit card information will be available there. If you should decide to cancel your subscription, it will continue to be valid until the next payment period, as all payments for the current period were made in advance. For special circumstances, please email support@studysoup.com

#### STUDYSOUP REFUND POLICY

StudySoup has more than 1 million course-specific study resources to help students study smarter. If you’re having trouble finding what you’re looking for, our customer support team can help you find what you need! Feel free to contact them here: support@studysoup.com

Recurring Subscriptions: If you have canceled your recurring subscription on the day of renewal and have not downloaded any documents, you may request a refund by submitting an email to support@studysoup.com

Satisfaction Guarantee: If you’re not satisfied with your subscription, you can contact us for further help. Contact must be made within 3 business days of your subscription purchase and your refund request will be subject for review.

Please Note: Refunds can never be provided more than 30 days after the initial purchase date regardless of your activity on the site.