Your Clarifying Values and Measuring Tradeoffs: Multi-Attribute Value Analysis In multiple-issue negotiations, one faces difficult tradeoffs between issues that are qualitatively different, but each highly valued. One must decide how much of one issue to give up in order to obtain a gain in another issue. This exercise is designed to lead you through some steps of qualitative and quantitative analysis that will prepare you to make these decisions. The field of decision analysis has developed a method for sorting out one's preferences in situations involving multiple issues. Most negotiators find that use of this method during preparation for a negotiation yields a substantial payoff. It increases one's confidence in tradeoff decisions, increases the clarity with which one can communicate, and decreases the chances of making a decision one will later regret. Because money is quantifiable and familiar, it would be convenient to try to convert all negotiation outcomes to dollars. However, many things of value to you are difficult to translate into money terms. In a job decision, can you precisely value health benefits, vacation days, office space, computer support, job prestige, company prestige, and industry prestige? Moreover, although we are more comfortable thinking in terms of dollars, what we are maximizing in life is some underlying utility function, for which money is just one way to meet the needs and goals that give us utility. Finally, there is often not a linear relationship between money and utility-- money typically has diminishing marginal value. Thus, the ability to quantify underlying values is a useful general tool for decision making and negotiation that can supplement a purely financial analysis. Step 1: Identify the relevant issues and range of possible outcomes The issues in a negotiation are not always obvious and need to be identified by considering how the upcoming negotiation could contribute to meeting one's interests. List the issues that will arise in your negotiation. For each issue, list the possible outcomes (e.g., for salary, it would be specific dollar amounts; for location, it would be specific cities). You should list the outcome levels that could be part of a possible settlement; that is, not a level which is so good as to be implausible or a level so bad as to be, by itself, a deal breaker. Carefully identifying the range of outcome levels on an issue makes it possible to precisely weight issues. Example Cathy expects to receive job offers from three companies. After careful reflection, Cathy decides that her main interests in the next few years will be to live in a city with mild winters and many outdoor activities, a comfortable salary, and large blocks of vacation time to take extended hiking trips.
All the companies pursuing Cathy have offices in Boston, Detroit, San Francisco, and Seattle. The salary in her field ranges from $50,000 to $70,000. Three weeks vacation is typical, but it can range from 2 weeks to 6 weeks. Step 2: Identify the best and worst outcomes within each issue. Within each issue, decide which outcome is the best and which is the worst. Example Cathy decides that the worst location is Detroit and the best is San Francisco. Naturally, the worst salary is $50,000 and the best is $70,000. The worst vacation time is 2 weeks and the best is 6 weeks. Step 3: Assign value levels within each issue. Assign the best outcome for a given issue the value of 100 and the worst outcome a value of 0. Note that 0 does not mean that an outcome has no value! Rather, it means that within the range of outcomes you have identified, it has the lowest value. Similarly, 100 does not imply perfection; it only implies that it is the best outcome in the range identified. (Technical aside: Using 0 and 100 improves consistency when weights are assigned in Steps 4 and 5.) Then for the other outcomes on that issue, assign a number between 0 and 100 that reflects their value relative to the best and worst outcomes. Do this for all of the issues. Example Cathys most preferred city, San Francisco, is assigned 100, and her least preferred city, Detroit, receives 0. Her assessment of outdoor opportunities and mild winters leads her to assign Seattle a score of 80 and New York a score of 40. She analyzes the other issues in a similar manner. Location Detroit Boston San Francisco Seattle Score 0 40 100 80 Salary 70 65 60 55 50 Score 100 90 70 40 0 Vacation 2 3 4 5 6 Score 0 20 40 70 100
Keep in mind, once again, that 0 does not mean that an issue has no value to Cathy. Cathy simply would enjoy Detroit, $50,000, and 2 weeks vacation the least.
Step 4: Assign preliminary issue weights. Construct a package consisting of the worst outcomes on all of your issues. For Cathy, this would be Detroit, $50K, and 2 weeks vacation (0, 0, 0). For each issue, think about how much enjoyment that moving from the worst outcome to the best outcome would provide. Then look at all of the issues and decide for which one this change would have the largest impact on your overall satisfaction. This is your top-ranked issue. Then look for the issue on which moving from worst to best would have the next greatest impact. This is your second-ranked issue. Continue in this way with all of your issues. Now we need to assign them a weight. Assume that Cathy has ranked the issues in the order (1) location, (2) salary, and (3) vacation. Note that ranking location ahead of salary does not mean that location is more important than salary to Cathy in general; it simply means that it is more important for the range of outcomes she is considering. There are several techniques for assigning weights. A relatively straightforward method is to assign your top-ranked issue the score of 1.00. Then evaluate how moving from worst to best on the second ranked compares issue to the same movement on the first ranked issue. If it is nearly as desirable, give it a high score (e.g., .80, or "80% as important"); if it is only about half as desirable, give it a middle score (e.g., .50, or "50% as important"). Do this for all of the issues. Example Changing from the least preferred location (Detroit) to the most preferred (San Francisco) gives Cathy the most enjoyment. Location, therefore, is her most important issue, and she gives it a preliminary weight of 1.00. The next most important change is from a salary of 50,000 to a salary of 70,000, but its not nearly as important as location, so she gives it a weight of .60. Moving from 2 weeks to 6 weeks vacation is nearly as important as salary, so she gives it a .50. Note: An alternative method for assigning issue weights is to work from specific tradeoffs. For example, we can assign a weight to Cathy's second-ranked issue (W2), salary, by comparing a job that has the best salary and the worst location (100W2 + 0W1) to a job that has the worst salary and a move to a better location (0W2 + xW1). How much improvement in location would be needed to make these equivalent in value? Would a move to Boston be enough? If not, we know that x > 40. Would a move to Seattle be enough? If so, we know that x < 80. Let's say that x does lie between 80 and 40, and Cathy splits the difference in value, setting x = 60. Once we know the value required on the location issue that makes Cathy indifferent (60), and given that the weight of the location issue has been set to 1 (W1 = 1.00), it follows that W2 = .60.
Step 5: Normalize the issue weights. Sum the points you've assigned to all of the issues (in the example, 1.00 + .60 + .50 = 2.10). Divide the points you've assigned to each issue by this total (e.g., 1.00/2.10, .60/2.10, and so on). Now you have converted all of the points to a standard scale that sums to 1.00, and that reflects the issues relative weight (e.g., Cathys most important issue has a weight of .48, the next most important issue has a weight of .29, and so on). Step 6: Multiply the outcome values and issue weights. Finally, take the product of the normalized weight for each issue (Step 5) and the value you assigned to each outcome on that issue (Step 3). This gives you a standardized value score for each outcome. Now all possible combinations of outcomes can be compared to each other, and to your ideal and worst set of outcomes (which have scores of 100 and 0, respectively). The completed example is on the next page. As you look at it, imagine Cathy's offer from Company A is (Boston, $60,000, 4 weeks vacation) and from Company B is (Seattle, $50,000, 3 weeks vacation). Which does she prefer? What should she negotiate for? Beyond Step 6: Before relying on the MAV model you have constructed, you should test the model in two ways. First, look at the tradeoffs implied by the model. For example, Cathy should ask whether an increase in salary from $55,000 to $65,000 (+14 points) would truly offset a change in assignment from San Francisco to Seattle (-10 points). Second, generate a number of alternative settlements and check whether the preferences implied by the model match your direct preferences. If not, you should adjust the weights and values until the model's preferences and your direct preferences converge. This approach makes the assumption of additivity. In some instances, value may not be additive. The value of an outcome on ...