BUS 520 Module 2 Case

Module 2 - Home

RISK: FREQUENCY DISTRIBUTION, PROBABILITIES, AND EXPECTED VALUE

Modular Learning Outcomes

Upon successful completion of this module, the student will be able to satisfy the following outcomes:

• Case o Identify when to use a Histogram (Frequency Diagram). o Enter data into Excel and set up the structure of a

histogram. o Manipulate the cell (bin) ranges in Excel to create the

best-formed histogram. o Discuss the results of a histogram analysis in a

business report. • SLP o Estimate probabilities in a decision situation under Risk. o Use Expected Value to evaluate alternatives in a risky

decision. o Discuss the results of a decision in a business report. • Discussion o Explain the Availability Bias and how it plays a role in

decision making under risk. Module Overview

In Module 1, we considered that we could assume that the future was known with certainty. In other words, we considered only one future state of the world. In some circumstances this is a convenient assumption. But very often there is more than one future state to consider. For a common example, consider the weather—sunny or cloudy; rain or dry; hot or cold. A business example is the future demand of a new product—high demand, medium demand or low demand.

We can often estimate the probabilities of these future states with some degree of accuracy. Many consultants specialize in this type of activity and build a track record that shows their degree of accuracy.

From a data analysis standpoint, we can record and collect specific data over time from a specific process or several processes and use this data to determine probabilities or frequency of occurrence.

Case 2 provides a situation in which you will use data that has been collected to develop a frequency distribution which you can use to estimate probabilities.

SLP 2 provides an introduction to risk and different future states in which you can estimate probabilities. You will learn how to analyze a risky decision using Expected Value and Expected Value of Money (EVM).

Module 2 - Background

RISK: FREQUENCY DISTRIBUTION, PROBABILITIES, AND EXPECTED VALUE

Case Background: Frequency Diagrams (Histograms)

Histograms (frequency distribution diagram). Consider a repetitive process, for example, driving home from work. You (and your spouse) have noticed that it takes longer to get home sometimes than others. So you want to do an experiment and find out just how long it does take. You record your time to drive home for 6 weeks and get 30 data points. (5 days, 6 weeks). Then you decide to analyze this statistically and see just how frequent the short trips and long trips and medium trips take. The best way to

do this is with a frequency diagram. Here is an example of what one looks like:

Note that you can analyze this data in more detail. And you can use it determine the Expected Value if you convert the raw frequencies into relative frequencies (probabilities.) The SLP will discuss the concept of Expected Value. In this example, the expected value is 44.3 minutes. Review this problem in the Excel file that you can download.

Watch this video that shows you how to do it:

View an example of a histogram. Download this Excel file with two examples and a Sample Problem: Case 2- Examples-Sample Problem.xlsx

PRACTICE: Now try the sample problem with the data in the downloaded Excel file.

• Follow the instructions in the file to create a histogram. • Check your results by reviewing the last Tab in the file.

You should be ready to do the Case 2 Assignment.

SLP Background: Decision Making Under Risk

Recall from Module 1 Background that in decision situations there are three different levels of uncertainty: assumed certainty, risk, and uncertainty. In SLP 1, we covered assumed certainty. In this Module we will discuss RISK. Recall what it means for a decision under risk:

Risk. In this situation, the decision maker distinguishes several possible future states, and is able to determine the probabilities of these distinct futures, or estimate the probabilities with a degree of confidence. There may be few or many options to choose from and the outcomes of these options may be different in the possible future states. For example, consider the weather which is always risky. And we usually have some estimates of the future states based on what the weatherman says. Two possible states are Rain and No Rain. The choices to consider here might be: Walk w/no umbrella, Walk w/umbrella, or Drive. The decision maker can determine the probabilities of Rain/No Rain from the forecast, for example, 60% chance of Rain (and 40% No Rain.) There are costs and payoffs involved with each option. And different people may have different decisions to make. The office employee may need to decide to walk or ride to work. The farmer may need to decide to work in the fields or protect the crops.

In all of these decisions there are basic elements that must be determined before the decision can be made. First, determine the possible future states (F) and the probabilities (p) for each. Note that the law of probability requires that the DM identify all a set of mutually exclusive and collectively exhaustive set of future states. And the probabilities must sum to 1.0 (100%). Then the DM must identify the alternatives (A). Note this is a key step as specified in Module 1. The next step is to identify the outcomes (O), payoffs, or consequences of each alternative for each future state. Quite often in business, this will be a monetary value. Then the DM can use the concept of Expected

Value to determine the probability payoff for each alternative which allows for choosing the best alternative.

Review the following PowerPoint introducing decision making under risk:

Introduction to Decisions Under Risk (PPT)

Now, watch this video that explains decision making under risk:http://permalink.fliqz.com/aspx/permalink.aspx?at=8786 29d1caf94d26b7e30bb857cbf6bf&a=5fae3cf0f1624f39b0341 263a6541ea0

Download this Excel file that shows the example used in the video: SLP 2 Examples-Sample Problem.xlsx

Try the Sample problem in this Excel file. Check your solution.

You should be ready for SLP 2.

Additional Required Reading

(For Discussions, Modules 2, 3, and 4)

Download and read this paper on subjective probabilities in risk decisions. This paper will be useful for the discussion questions in Module 2, 3, and 4: Subjective Assessments of Risk and Uncertainty

Get this journal article from the library. It is lengthy, but you only need to read Section 1.1, pp. 3-5. This section provides a very good review of three major biases that have been studied by the famous team of Kahneman and Tversky.

Laibson, D., & Zeckhauser, R. (1998). Amos Tversky and the ascent of behavioral economics.Journal of Risk & Uncertainty. Feb1998, Vol. 16 Issue 1, p7-47. 41p. Retrieved from Business Source Complete (EBSCO) in the Trident Online Library.

Optional Reading

This is nice paper by Daniel Kahneman. It is the basis for his recent book, Thinking Fast and Slow.

Kahneman, D. (2003). Maps of Bounded Rationality: Psychology for Behavioral Economics, American Economic Review, December, 2003. Retrieved from Ebsco.

Reading for Module 3 to Read Now

This reading is important background for Cases 3 and 4. You should read it now to prepare for these coming Cases.

Chapter 2 (pp. 54–76), and Ch. 3 (pp. 77–91), from: Chase, C. W., (2013). Demand-Driven Forecasting: A Structured Approach to Forecasting, John Wiley & Sons: Somerset, NJ. Retrieved from the Ebrary.