Many decisions can be effectively model considering only a single measure of value, such as the net present value (NPV) of cash flows discounted at weighted average cost of capital (WACC). However, some decision problems, including many of those in the public sector, may require you to quantitatively modeled several measures of value, or you may find that including more than one measure is more enlightening. These value measures are referred to as attributes.
Attributes can be useful even when the focus of the analysis is financial. You may find it useful to look at both free cash flow and earnings, for example. But in this deck we address a decision in which the attributes are not all financial. In a subsequent deck we will see an example of slightly more complex multiple attribute models in which the focus is financial.
10 Easiest Ways To Buy Verified TransferWise Accounts
103 Optimizing on Multiple Attributes
1. 103
Optimizing on Multiple Attributes
M J V L L C 5 0 5 M O N T G O M E R Y S T 1 1 T H F L R S A N F R A N C I S C O + 1 . 4 1 5 . 4 2 5 . 1 4 3 6
P R E - R E Q U I S I T E : 1 0 2 C R E A T I N G V A L U E T H R O U G H “ R E A L O P T I O N S ”
2. The Value Proposition
Many decisions can be effectively modeled considering only a single measure of value,
such as the net present value (NPV) of cash flows discounted at weighted average cost of
capital (WACC). However, some decision problems, including many of those in the public
sector, may require you to quantitatively modeled several measures of value, or you may
find that including more than one measure is more enlightening. These value measures
are referred to as attributes.
Attributes can be useful even when the focus of the analysis is financial. You may find it
useful to look at both free cash flow and earnings, for example. But in this deck we
address a decision in which the attributes are not all financial. In a subsequent deck we
will see an example of slightly more complex multiple attribute models in which the
focus is financial.
3. Incorporating Multiple Attributes
Let’s begin with a model that has only a single, economic attribute. We’ll then add a second attribute to this model to account
for environmental considerations. Let’s start with the model we encountered in deck 102, and we’ll compute its Policy Tree.
The expected value of proceeding with the core sample test is $31 million, whereas drilling without the test has an expected
value of $26.5 million.
4. Incorporating Multiple Attributes
The oilfield in question is in a pristine wilderness area. Testing and drilling would require some areas be cleared and an access
road be built. To take this into account in our decision, we will add an attribute representing the amount of land that would
need to be disturbed. The amount of land disturbed will be conditioned on both the decision to drill and the decision to test.
We will also add a Land Weighting of -0.5 to show the unit value of the disturbed land. These are reflected in the associated
influence diagram on the next page.
6. Incorporating Multiple Attributes
The decision tree has three Get/Pay expressions: one for each of Test Costs, Drilling Costs and Revenues. At this point, all three
of these expressions contribute their value to a single, implicit attribute which, if it were named, would probably be called
“Profit”.
Test Costs
+ Drilling Costs
+ Revenues
= Profit
7. Incorporating Multiple Attributes
Next will add the attribute for land and configure to create a two-attribute model with an objective function that linearly
combines the attributes, then calculate the Policy Tree.
8. Incorporating Multiple Attributes
The expected values of both alternatives are lower than in the original Policy Tree, reflecting a “penalty” of $0.5 million per
unit of land. The Core sample Test still the preferred alternative, although the gap has narrowed.
9. Incorporating Multiple Attributes
Let’s run a Rainbow Diagram on the Land Weight value. We want to know how much more penalty there would need
to be associated with land disturbed for the optimal alternative to change, i.e., how much more negative Land Weight
would need to be. The Rainbow Diagram shows that Land Weight would have to be less than -0.625 for the decision
policy to change.
10. Contact
We brought in Marc Vandenplas
Marc Vandenplas
project portfolio management
mjv llc
+01 415 425 1436
505 montgomery st 11th floor
san francisco ca 94111
mjv1436@gmail.com
Available Direct or through your IP provider.
All rights reserved.
