This assignment can be done either individually or as a team of 2. If you would like to work on this as a team, you need to send me the names of the team members by Monday, Sept. 21st. For this assignment, you need to choose ONLY ONE of the following decision contexts: Buying a house or Rent an apartment A real decision making case from your organization where one alternative needs be selected among different alternatives, and the organization has multiple objectives that are of interest to the decision makers. You will need to examine at least 3 different alternatives in your analysis You should gather REAL data (from the internet or from your organization) for your analysis. The source of the data should be mentioned at the end of your assignment. If you choose a) that you have data that are current: July - October 2015. The alternatives that you consider must be in a similar category to be compared (same size cars, houses in the same price range Houses: less $100,000 difference between most expensive and least expensive house Apartment: similarly priced apartments: no more than a $600/month difference between least and most expensive. You must use Logical Decisions Software for the analysis. In your final submission, you need to include the following: Create an objective hierarchy If you decide to work on the used car case, you are allowed to use some but not all of the objectives and measures described in Tables 5.11 and 5.12. You can eliminate some measures found in the text if you think they do not represent your concerns. You must add at least four measures of your own. There must be at least one constructed measure that would involve creating a table similar 4.5, 4.6 or 4.7. You cannot simply use phrases like good or excellent. There must be a way of looking at an alternative and determining what category it belongs to. (See for example, how Amazon describes the state of a used book.) You will need to explain why each alternative was assigned to a specific category. Explain the meaning of each and every measure. Pay special attention to explaining all constructed measures. In any case, there should be at least 3 major objectives and 8 measures At least, one of the measures must include data that has probabilistic estimates. Explain how you obtained the probability distribution. Input the data matrix for all the alternatives. Assign weights in the LDW file using SMART (swing) weight method. Interview 1 person (could be yourself or a close acquaintance) and assess that persons weights for the measures. (If you are doing this as a team, you must interview 2 people and save both preferences in the same LDW file.) Describe the rationale behind the weights that the individual(s) had for each measure. This is an important part of the assignment. First explain the logic behind the rank ordering. Then explain the relative scores. Assess at least one label measure as nonlinear. Explain why this measure is non-linear. Assign values that make the utility curve look non-linear. Perform comprehensive analysis of alternatives, rank-order them, and identify the best alternative. If you are working as a team, determine the best alternative for each interviewee and explain the differences. Include preference set summary table. Compare the 2 best alternatives using a paired chart, and explain what are their strengths and weaknesses. If you are working as a team, do this for JUST ONE Determine the robustness of the best alternative to changes in weights of the 3 most important measures using sensitivity graphs. Give some explanation of the sensitivity graphs. If you are working as a team, do this for JUST ONE Determine the sensitivity of the best alternative to uncertainty in the data. Identify whether or not uncertainty might change ranking. If you are working as a team, do this for JUST ONE On what relatively highly weighted measure is the best alternative weak? What could be done to improve the best alternatives performance on that measure? If you are working as a team, do this for JUST ONE Mention your specific source and date(s) for gathering data.