Probability Functions in Decision Trees
 
Probability
Functions in Decision Trees
You might be asking: What about delays other than 20 days,
or why 20 days? The project manager and project team may be able to estimate
much finer segments than 20 days. There really is no limit to how many
individual discrete estimates could be made and summed at a node. It is required
that the sum of all probabilities equal 1. So as more discrete estimates are
made, say for 1, 2, 5, 10, or other days of delay, the individual probabilities
must be made individually less so that the total summation of the "p"s equaling
1 is honored.
Now you may recognize this discussion as similar to the discussion
in the chapter on statistics regarding the morphing of the discrete probability
distribution into the continuous probability function. Obviously, the values at
the input of the summing node are the values from the discrete probability
function of the random variable or event that feeds into the summing node. There
is no reason that the random variable could not be continuous rather than
discrete. There is no reason that the discrete probability function cannot be
replaced with a single continuous probability function for the event.
Suppose the inputs to the summing nodes are replaced with
continuous probability functions for a continuous random variable. Figure 4-7 shows such a case. Now comes
the task of summing these continuous functions. We know we can sum the expected
values, and if they are independent random variables, we know we can sum the variances with simple arithmetic.
However, to sum the distribution functions to arrive at a distribution function
at the decision square is another matter. The mathematics for such a summing
task is complex. The best approach is to use a Monte Carlo simulation in the
decision tree. For such a simulation you will need software tools, but they are
available.
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