Multi-Criteria Decision Making Decision Making and Risk, Sp2006: Session 6
Notebook Computer Decision • Consumer is interested in buying a notebook computer. • Goes to electronics store. • Sees the following options. • How should she go about buying one?
Types of MCDM Rules • Compensatory: • Simple compensatory • Linear (integration and valuation) • Non-linear (linear integration, non-linear valuation) • Non-compensatory • Screening • Conjunctive • Disjunctive • Selection • Elimination by aspects • Lexicographic • Heuristics
Simple Compensatory Rule • Alternative with the most number of top of class performance.
Approach #1 • Imagine “n” objects, with “m” attributes each. • For the ith object: • Declare attribute importance for each of the “m” attributes (wj). • Determine how much you like the level of the attribute in the ith product (aij) • Combine attribute importance with attractiveness of the attribute level featured in the product. • Add the above combinations. • For the ith object, Vi = Σj=1 to n (wj * aij), akin to expected utility. • Repeat this for all i objects. • Pick “i” such that, i = Max (V1, V2, V3…..Vn)
Non-linear Compensatory Decision Rules • Valuation of additional unit is not identical across the range of the levels. • 60G to 80G is more consequential compared to 100 to 120G • However, integration across attributes of the alternatives is still linear.
Summary of Compensatory • Simple compensatory: No relative valuation of attribute levels other than best/not best, equal weight for all attributes. • Linear Compensatory: Equal valuation of attributes levels, linear combination of valuation through attribute-specific weights. • Non-linear Compensatory: Unequal valuation of attribute levels, linear combination of valuation through attribute-specific weights.
Non-Compensatory Decision Rules • Screening Rules • Conjunctive decision rule • Disjunctive decision rule • Choice Rules • Elimination by aspects • Lexicographic decision rule
Elimination by Aspects • Elimination By Aspects • Start with minimum cutoff on most important attribute. • Eliminate those that do not clear cutoff. • Take the next most important attribute, and repeat steps above. • Stop when you have one brand. • Elimination rule, rather than selection rule.
Conjunctive • Eliminate alternatives that don’t meet/exceed cutoff on every attribute. × ×
Disjunctive • Accept any alternative that exceeds minimum cutoff on at least one attribute.
Elimination By Aspects × × ×
Lexicographic • Select best option on the most important attribute.
Comparing Compensatory and Non-compensatory Decision Rules • Compensatory Decision Rules • Strengths of one attribute can overcome weakness of another. • Selection rules • Effortful • Non-Compensatory Decision Rules • Strength of one attribute cannot overcome weakness of another. • Elimination rules • Easier • Often decision makers use hybrid rules
Highlights • What happens when decision options come with multiple attributes? • Reduce them to a single attribute • Deal with multiple attributes • Compensatory • Weighted attribute utility approach (compensatory) • Non-compensatory • EBA (sequential elimination) • Lexicographic (selection by reduction to single attribute…repeat if necessary) • Conjunctive (inclusion based on thresholds for every attribute) • Assists in narrowing the consideration set • Disjunctive (inclusion based on threshold for at least one attribute) • Assists in broadening the consideration set • Different strategies at different stages. • Vary in effort and data required. • Regret may be a function of the type of decision strategy. • Thresholds are the result of past experiences, negatives leave a stronger imprint.
Heuristics • Decision rules, shortcuts. • Sometimes, they are meta-decisions. Some Examples • What I did the last time around? • What does the expert think? • The price-quality relationship is: • Bogus, so, look for the relatively less expensive option. • Valid, so, look for the relatively more expensive option. • Minimize decision effort/cost. • Minimize regret. • Maximize effectiveness.