Copyright 2014 Kenneth M. Chipps Ph.D. Decision Support System Lab Using Analytica Last Update

Objectives See how to use a decision support system to make unstructured decisions Copyright 2014 Kenneth M. Chipps Ph.D. 2

Sources This lab is based on the Analytica tutorial manual and user guide Most of it is copied from the tutorial Copyright 2014 Kenneth M. Chipps Ph.D. 3

What is a DSS A DSS - Decision Support System is an information system that supports organizational decision-making These systems are designed for use by the management, operations, and planning levels of an organization at the middle and upper layers of management Copyright 2014 Kenneth M. Chipps Ph.D. 4

What is a DSS These systems are aimed at those decisions, which may be rapidly changing and not easily defined in advance In other words they are unstructured or semi-structured problems These systems typically combine data mining with models and analytical techniques such as risk analysis and uncertainty Copyright 2014 Kenneth M. Chipps Ph.D. 5

What is a DSS Examples of the types of decisions that such a system could help with include –Sale projections where multiple variables impact sales –Revenue projections based on these sales projections Copyright 2014 Kenneth M. Chipps Ph.D. 6

What is Analytica Analytica is a program developed by Lumina Decision Systems that is intended to create quantitative decision models It uses hierarchical influence diagrams so as to present the problem and the interaction among the variables in a problem in a clear visual format Copyright 2014 Kenneth M. Chipps Ph.D. 7

What is Analytica These variables and the influences they have on each other are modeled using multidimensional data, Monte Carlo simulation for analyzing risk and uncertainty, and optimization, including linear and nonlinear programming This approach is based on the field of decision analysis Copyright 2014 Kenneth M. Chipps Ph.D. 8

What is Analytica Analytica models are presented as influence diagrams The variables are represented by nodes of various shapes on a diagram that are connected by arrows that provide a visual representation of dependencies These influence diagrams may be hierarchical, where a single node on a diagram represents a submodel Copyright 2014 Kenneth M. Chipps Ph.D. 9

What is Analytica This visual representation of the relationships makes the interrelationship of the variables clear to the decision maker This is a distinct improvement over spreadsheet and formula based methods that require analysis first of what is seen before the variables can be adjusted Copyright 2014 Kenneth M. Chipps Ph.D. 10

What is Analytica This method also makes it easier to communicate to others how a decision was made Let’s see how Analytica works Here is what the Analytica tutorial says –You are about to discover a powerful tool for real-world modeling and analysis –Analytica embodies the idea of using a white board for problem solving Copyright 2014 Kenneth M. Chipps Ph.D. 11

What is Analytica –Using a visual, point-and-click approach, you draw nodes and arrows to depict the relationships between model components –This approach allows you to describe the essential qualitative nature of the problem without getting lost in the details –As the model develops and your understanding of the problem becomes clear, you can define the exact quantitative details of the model Copyright 2014 Kenneth M. Chipps Ph.D. 12

What is Analytica –A key feature of Analytica is its ability to create hierarchies of models –By grouping related components of a problem into separate submodels, you can impose a multi-level organization to your model –This helps you to manage complex relationships and allows other users to more easily grasp important concepts The resulting model looks like this Copyright 2014 Kenneth M. Chipps Ph.D. 13

What is Analytica Copyright 2014 Kenneth M. Chipps Ph.D. 14

Create a Virtual Machine To run this program we will create a VirtualBox virtual machine using Windows 7 as the operating system to avoid any problems with installing this on the lab machines Start VirtualBox Click on the –New button Copyright 2014 Kenneth M. Chipps Ph.D. 15

Create a Virtual Machine In the Name box enter –Windows 7 In the Version dropdown select –Windows 7 (32 bit) Click –Next Copyright 2014 Kenneth M. Chipps Ph.D. 16

Create a Virtual Machine Copyright 2014 Kenneth M. Chipps Ph.D. 17

Create a Virtual Machine For Memory size use –1024 MB Click –Next Copyright 2014 Kenneth M. Chipps Ph.D. 18

Create a Virtual Machine Copyright 2014 Kenneth M. Chipps Ph.D. 19

Create a Virtual Machine Use the defaults for all of the drive settings The virtual machine is created To load Windows 7 in this virtual machine select it from the virtual machine list in the left panel and click the –Start button Copyright 2014 Kenneth M. Chipps Ph.D. 20

Create a Virtual Machine In the window that appears click on the small yellow file folder icon on the right side Copyright 2014 Kenneth M. Chipps Ph.D. 21

Create a Virtual Machine Copyright 2014 Kenneth M. Chipps Ph.D. 22

Create a Virtual Machine Navigate to wherever you placed the Windows 7 iso file Select it Click –Start In the virtual machine the operating system will be loaded just as it would be on a physical machine Copyright 2014 Kenneth M. Chipps Ph.D. 23

Install Analytica Now that the virtual machine is created we need to install Analytica in the virtual machine Go to – Click in the menu –Products Copyright 2014 Kenneth M. Chipps Ph.D. 24

Install Analytica At the bottom of the drop down menu select –Analytica Free 101 Edition Once it is downloaded in the virtual machine, install it Copyright 2014 Kenneth M. Chipps Ph.D. 25

Buy v Rent Example Let’s first look at a simple example of this using the rent v buy model from the Analytica tutorial Start Analytica Select –File –Open Model Copyright 2014 Kenneth M. Chipps Ph.D. 26

Buy v Rent Example Click on the box on the left side titled –Example Analytica … Copyright 2014 Kenneth M. Chipps Ph.D. 27

Buy v Rent Example Copyright 2014 Kenneth M. Chipps Ph.D. 28

Buy v Rent Example Double Click on –Tutorial Models Select the file named –Rent vs. Buy Model.ana Click –Open Copyright 2014 Kenneth M. Chipps Ph.D. 29

Buy v Rent Example Copyright 2014 Kenneth M. Chipps Ph.D. 30

Buy v Rent Example This window will appear Copyright 2014 Kenneth M. Chipps Ph.D. 31

Buy v Rent Example Copyright 2014 Kenneth M. Chipps Ph.D. 32

Buy v Rent Example With the model open, a top-level diagram window is shown The model diagram shows several input variables that affect the trade-offs between renting and buying Since the graph is of probability densities, both buying and renting have probabilistic, or uncertain, inputs Copyright 2014 Kenneth M. Chipps Ph.D. 33

Buy v Rent Example Copyright 2014 Kenneth M. Chipps Ph.D. 34

Buy v Rent Example If you hover the mouse pointer over any of the items an explanation will appear The Calc button will produce the results of the model The Model button reveals the highest level model itself The other boxes and buttons contain the variables that impact the results computed for the model Copyright 2014 Kenneth M. Chipps Ph.D. 35

Buy v Rent Example Let’s look at the basic model Double click on the Model button This window will appear Copyright 2014 Kenneth M. Chipps Ph.D. 36

Buy v Rent Example Copyright 2014 Kenneth M. Chipps Ph.D. 37

Buy v Rent Example These are the variables and the relationship between the variables Copyright 2014 Kenneth M. Chipps Ph.D. 38

Buy v Rent Example The output value displays in a Result window This Result window shows a graph of two probability density curves, one for buying and one for renting In a probability density graph, the units of the vertical scale are chosen so that the total area under each curve is 1 or 100% Copyright 2014 Kenneth M. Chipps Ph.D. 39

Buy v Rent Example What’s the answer Press the Calc button We see this Copyright 2014 Kenneth M. Chipps Ph.D. 40

Buy v Rent Example Copyright 2014 Kenneth M. Chipps Ph.D. 41

Buy v Rent Example This tells us that with the values seen above the cost of renting is between –105,000 and 155,000 dollars –Renting is always a cost, a negative number Whereas the cost of buying is between –115,000 and 75,000 –This is a cost of 115,000 up to a gain of 75,000 Why the range and not a single number Copyright 2014 Kenneth M. Chipps Ph.D. 42

Buy v Rent Example Some of the values seen in the model can vary, such as –The rate of inflation –The appreciation rate of the property So it depends What if we vary a constant In other words we change something such as the time horizon from 10 to 7 Copyright 2014 Kenneth M. Chipps Ph.D. 43

Buy v Rent Example Go back to the model window Change 10 to 7 in the time horizon box Change the Buying price to 180,000 Click outside of the box Click Calc The result is Copyright 2014 Kenneth M. Chipps Ph.D. 44

Buy v Rent Example Copyright 2014 Kenneth M. Chipps Ph.D. 45

Buy v Rent Example What if we change the uncertain values such as the Rate of inflation And the expectation of the variance in inflation Right now it is a normal distribution which is Copyright 2014 Kenneth M. Chipps Ph.D. 46

Buy v Rent Example Copyright 2014 Kenneth M. Chipps Ph.D. 47

Buy v Rent Example A mean of 3.5 and a standard deviation of 1.3 Rather than using the normal distribution, we will use the uniform distribution, and assume that inflation has an equal probability of being anywhere between 3% and 4% per year Copyright 2014 Kenneth M. Chipps Ph.D. 48

Buy v Rent Example When an input is defined as a probability distribution, a button with the name of the distribution appears next to the input’s name Clicking this button opens the Object Finder window, in which we can see details and change the distribution’s parameters or type of distribution Copyright 2014 Kenneth M. Chipps Ph.D. 49

Buy v Rent Example Rate of inflation’s button says Normal, indicating that it is defined as a normal distribution Click the Normal button to the right of –Rate of inflation The Object Finder window appears It shows that Rate of inflation is defined as a normal distribution with a mean of 3.5 and a standard deviation of 1.3 Copyright 2014 Kenneth M. Chipps Ph.D. 50

Buy v Rent Example Let’s change this Copyright 2014 Kenneth M. Chipps Ph.D. 51

Buy v Rent Example Copyright 2014 Kenneth M. Chipps Ph.D. 52

Buy v Rent Example Copyright 2014 Kenneth M. Chipps Ph.D. 53

Buy v Rent Example We modified the probability distribution that defines Rate of inflation Rather than using the normal distribution, we used the uniform distribution, and changed the inflation assumption to an equal probability of being anywhere between 3% and 4% per year Copyright 2014 Kenneth M. Chipps Ph.D. 54

Buy v Rent Example Copyright 2014 Kenneth M. Chipps Ph.D. 55

Buy v Rent Example As you can see the uncertainty is much reduced The ranges now are –The cost to rent is 105,000 to 109,000, while the uncertainty in the cost of buying has flattened to between a cost of 125,000 to a gain of 10,000 Copyright 2014 Kenneth M. Chipps Ph.D. 56

Buy v Rent Example The next question is which of the variables has the most impact on the results In other words, which variable do we need to spend the most time on to ensure it is accurate as miss estimating it may produce spurious results Copyright 2014 Kenneth M. Chipps Ph.D. 57

Buy v Rent Example To do this we will use importance analysis As the tutorial points out –In the Rent vs. Buy Analysis model, as in most complex models, several of the input variables are uncertain –It is often useful to understand how much each uncertain input contributes to the uncertainty in the output Copyright 2014 Kenneth M. Chipps Ph.D. 58

Buy v Rent Example –Typically, a few key uncertain inputs are responsible for the lion’s share of the uncertainty in the output, while the rest of the inputs have little impact –Analytica’s importance analysis features can help you understand which uncertain inputs contribute most to the uncertainty in the output Copyright 2014 Kenneth M. Chipps Ph.D. 59

Buy v Rent Example –You can then concentrate on getting more precise estimates or building a more detailed model for the one or two most important inputs –Analytica defines importance as the rank order correlation between the output value and each uncertain input –Each variable’s importance is calculated on a relative scale from 0 to 1 Copyright 2014 Kenneth M. Chipps Ph.D. 60

Buy v Rent Example –An importance value of 0 indicates that the uncertain input variable has no effect on the uncertainty in the output –A value of 1 implies total correlation, where all of the uncertainty in the output is due to the uncertainty of a single input To see the importance of the variables Copyright 2014 Kenneth M. Chipps Ph.D. 61

Buy v Rent Example Copyright 2014 Kenneth M. Chipps Ph.D. 62

Buy v Rent Example This window will appear Copyright 2014 Kenneth M. Chipps Ph.D. 63

Buy v Rent Example Copyright 2014 Kenneth M. Chipps Ph.D. 64

Buy v Rent Example Obviously the Appreciation rate is the most important variable It contributes the most to the uncertainty in the difference between renting and buying Copyright 2014 Kenneth M. Chipps Ph.D. 65

Summary In this lab we have seen a simple, but practical, example of a decision support system Copyright 2014 Kenneth M. Chipps Ph.D. 66