For the first example, we use a simple function with a single decision variable, the same one considered on the summary page.

The form describing the function is shown in the picture.

The Integration dialog selects the function to be integrated, the location of the integration results, the number of steps in the integration, the integration method, and the strategy for performing calculations.

The first page of this section describes the Calculation options. For small dimensionality problems with simple functions, the Automatic option is probably the best. When using this add-in it is important to set the Excel Calculation option, set in the Preference dialog, to Automatic.

Simpson's Rule

For the example, the integral can be computed exactly as:

Simpson's Rule evaluates the function in the range [a,b] with points at a and b with the remaining points equally spaced in the interval. There are 2n intervals and 2n + 1 the evaluation points.

The results of Simpson's Rule with 100 observations for the example are shown below. The program does not count the observation at the lower bound, so there are actually 101 observations.

The numerically obtained results are quite close to the correct value with 100 observations. Simpson's Rule is generally satisfactory for functions of a single variable when the function has continuous derivatives. With a single-dimensional quadratic function, 3 observations (n = 1 in the expression above) yields an accurate solution since Simpson's rule is based on a piece-wise quadratic approximation.

Monte Carlo Method

An alternative way to estimate the integral is through simulation. The procedure is called the Monte Carlo method. To estimate an integral we draw a number of random observations of the function with the range of the integration variables and use the average of the observations as an estimate of the integral. With a single variable of integration, an observation of the function is determined using a randomly selected value within the range of the variable.

For the moment assume that the range of integration is from 0 to 1. To estimate the integral and at the same time find the variance of the estimate, we divide the total number of observations into samples of 30. The mean of the observations is computed for each sample. The grand mean of the 30 sample means is the estimate of the integral. An estimate of the variance of integral is obtained from the variance of the sample means.

When the range of integration is not [0,1], the estimate of the integral and its variance must be adjusted for the range. Because we have used 30 samples, we can use the consequences of the Central Limit Theorem to create a confidence limit.

The results for the example are shown for three different numbers of observations.

For the Monte-Carlo integration method, observations of the independent variables are randomly selected, 120 in this case. The average function value of the observations multiplied by the range provides an estimate of the integral, presented shown in cell B18. Since the result is a random variable, we estimate the standard deviation of the integral estimate, presented in cell B19. Because of the Central Limit Theorem the mean of the sample means is approximately Normally distributed, so we use the Normal distribution to provide a confidence limit for the integral. The confidence level in B21 can be changed by the user. Formulas in cells C21 and D21 show the range of the confidence limit. With a larger number of observations, the standard deviation of the estimate and the width of the confidence interval is decreased. To decrease the standard deviation of the estimate by a factor of 10, the number of observations must be increased by a factor of 100. This is illustrated by comparing the standard deviation with 120 observations (0.01156) with the standard deviation for 12000 observations (0.00117).

Quasi-Monte Carlo Method

The Quasi-Monte Carlo method generates the values for observations that more equally distribute the observations in the range of the integration variables. For a single dimension the formula for the observation k is:

Quasi-Monte Carlo provides more accurate results for the same number of observations than the Monte Carlo when the number of variables is moderate. The add-in limits the number of integration variables to 20 for the Quasi-Monte Carlo method. This is a limitation of the program, not the method. The random number depends on the sample number n. It is added to introduce randomness between samples to allow the computation of the standard deviation. When modified in this way the method is called Randomized Quasi-Monte Carlo.

To illustrate integration over more than one integration variable we use the four dimensional example below.

The absolute values used in the function make the first derivatives of the function discontinuous whenever a variable has the value 0.5. The integral of this function of the ranges of the four decision variables is:

The add-in integrates over the ranges prescribed by the lower and upper bounds given on the function form. The function's representation on the worksheet refers to the calculations just below the function form.

We apply the three integration methods below. Simpson's Rule is applied recursively to the four variables of integration. The number of points investigated for each dimension is the 4th root of 10000 or 10. If the 4th root of the number of observations is not an integer, the number of observations is adjusted to obtain an integer value. For this case one would not expect the Simpson's Rule to give a very accurate answer, and it does not.

In the case of the Monte Carlo and Quasi Monte Carlo methods, the number of observation must be a multiple of 30. When 10000 observations are requested, the program increases this number to the next higher multiple of 30, 10020. With this number the methods run 30 samples of 304 observations each (30*304 = 10020).

This function has a complicated shape with discontinuous first derivatives. We see that Simpson's Rule with 10 observations per dimension is not accurate. The two Monte Carlo methods provide 90% confidence intervals that contain the true value.

Simpson's Rule is perfectly accurate for quadratic functions. With a four dimensional quadratic function, the Rule returns an accurate solution with only 81 observations (3 per dimension).

Integration over a Subset of Variables

With multiple variables, it is possible to integrate over a subset of the variables with the remainder of the variables fixed at specified values. To fix a variable give equal lower and upper bounds to the variable in the function form. The example shows P(2) fixed at 0.25 and P(4) fixed at 0.75. These values make the factor for each of these terms equal to 1 in the function.

Integration is over the remaining variables. In the results shown below, Simpson's Rule provides the exact result (the exact integral has the value 1). With only two remaining integration variables, Simpson's Rule has 100 observations for each dimension. The Monte Carlo results are also more accurate because of the reduced dimensionality of the integral. In the result below, the names of the integration variables are colored red and the fixed values are shown in column L.

Application

One important application of integration is computing the expected value of some measure that depends on random variables.

Here the integrations are performed over the ranges of the random variables.

For simulation studies, some measure of effectiveness is defined for the simulated system and a model is constructed that relates the measure to parameters of the system. When some parameters are random variables, the model uses random number seeds for each random variable. A simulation run with a specified set of seeds, one for each random variable, gives a single observation of the measure. The goal of the simulation study is to determine the expected value of the effectiveness measure. To do this requires an integration as described on this page. The seeds are chosen from uniform distributions that range from 0 to 1.

As illustrated by some of the examples, integration in multiple dimensions is not always easy. Since the functions computed by simulation are often very complex, many observations may be required to obtain accurate results.