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The DTREE Procedure 
The wildcatter is impressed with the results of calculating
the values of perfect information and perfect control. After
comparing those values with the costs of the sounding test
and the costcontrolling procedure, he prefers to spend $60,000 on
sounding test, which has a potential improvement of $155,000.
He is informed
that the sounding will disclose whether the terrain below has
no structure (which is bad), open structure (which is okay),
or closed structure (which is really hopeful). The expert also
provides him with the following table, which shows the conditional
probabilities.
Seismic Outcomes  
State  No Structure  Open Structure  Closed Structure 
Dry  0.6  0.3  0.1 
Wet  0.3  0.4  0.3 
Soaking  0.1  0.4  0.5 
To include this additional information into his basic problem, the wildcatter needs to add two stages to his model: a decision stage to represent the decision whether or not to take the sounding test, and one chance stage to represent the uncertain test result. The new STAGEIN= data set is
/*  create the STAGEIN= data set  */ data Dtoils2; input _STNAME_ $12. _STTYPE_ $3. _OUTCOM_ $16. _SUCCES_ $12. _REWARD_ dollar12.0; datalines; Drill D Drill Cost . . . Not_Drill . . Cost C Low Oil_Deposit . . . Fair Oil_Deposit . . . High Oil_Deposit . Oil_Deposit C Dry . . . . Wet . . . . Soaking . . Sounding D Noseismic Drill . . . Seismic Structure $60,000 Structure C No_Struct Drill . . . Open_Struct Drill . . . Closed_Struct Drill . ;
Note that the cost for the seismic soundings is represented as negative reward (of the outcome Seismic) in this data set. The conditional probabilities for stage Structure are added to the PROBIN= data set as follows:
/*  create PROBIN= data set  */ data Dtoilp2; input _GIVEN_ $8. _EVENT1 $10. _PROB1 _EVENT2 $12. _PROB2 _EVENT3 $14. _PROB3; datalines; . Low 0.2 Fair 0.6 High 0.2 . Dry 0.5 Wet 0.3 Soaking 0.2 Dry No_Struct 0.6 Open_Struct 0.3 Closed_Struct 0.1 Wet No_Struct 0.3 Open_Struct 0.4 Closed_Struct 0.3 Soaking No_Struct 0.1 Open_Struct 0.4 Closed_Struct 0.5 ;
It is not necessary to make any change to the PAYOFFS= data set. To evaluate his new model, the wildcatter invokes PROC DTREE as follows:
/*  PROC DTREE statements  */ title "Oil Wildcatter's Problem"; proc dtree stagein=Dtoils2 probin=Dtoilp2 payoffs=Dtoilu1 nowarning; evaluate;
As before, the following messages are written to the SAS log:
NOTE: Present order of stages: Sounding(D), Structure(C), Drill(D), Cost(C), Oil_Deposit(C), _ENDST_(E). NOTE: The currently optimal decision yields 140000.
The following SUMMARY statements produce optimal decision summary as shown in Figure 3.5 and Figure 3.6:
summary / target=Sounding; summary / target=Drill;
The optimal strategy for the oildrilling problem is found to be the following:

Note that the value of sample information also can be obtained by using the following statements:
modify Seismic reward 0; evaluate;
The following messages, which appear in the SAS log, show the expected payoff with soundings test is $180,100. Recall that the expected value without test information is $140,000. Again, following the previous calculation, the value of test information is $180,100  $140,000 = $40,100.
NOTE: The reward of outcome Seismic has been changed to 0. NOTE: The currently optimal decision yields 180100.
Now, the wildcatter has the information to make his best decision.
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