Potentially All Pairwise RanKings of all possible Alternatives (PAPRIKA) is a method for multi-criteria decision making (MCDM) or conjoint analysis, as implemented by decision-making software and conjoint analysis products 1000minds and MeenyMo. The PAPRIKA method is based on users expressing their preferences with respect to the relative importance of the criteria or attributes of interest for the decision or choice at hand by pairwise comparing (ranking) alternatives. In MCDM applications, PAPRIKA is used by decision-makers to determine weights on the criteria for the decision being made, representing their relative importance. Depending on the application, these weights are used to rank, prioritize or choose between alternatives. In conjoint analysis applications, PAPRIKA is used with consumers or other stakeholders to estimate 'part-worth utilities' (i.e. weights) representing the relative importance of the attributes characterizing products or other objects of interest (i.e., choice modelling, conjoint analysis and discrete choice).
Applications The PAPRIKA method is implemented by decision-making software and conjoint analysis products 1000minds and MeenyMo. Examples of areas in which the method is used for multi-criteria decision making or conjoint analysis include (see also 1000minds applications):
Patient and health technology prioritization Disease diagnosis and classification Clinical guidelines development Disease R&D prioritization Marketing research (conjoint analysis) Environmental resources management and climate change research Animal and plant breeding Urban planning and waste management Information and communications technology (ICT) Research into monetary policy, retirement income policies and charitable giving
Additive multi-attribute value models The PAPRIKA method specifically applies to additive multi-attribute value models with performance categories – also known as 'points', 'scoring', 'point-count' or 'linear' systems or models. The following explanations are mostly couched in terms of multi-criteria decision making. Analogous explanations in terms of conjoint analysis are possible but not presented here. As the name implies, additive multi-attribute value models with performance categories – hereinafter referred to simply as 'value models' – consist of multiple criteria (or 'attributes'), with two or more performance categories (or 'levels') within each criterion, that are combined additively. Each category within each criterion is worth a certain number of points that is intended to reflect both the relative importance ('weight') of the criterion and its degree of achievement. For each alternative being considered, the point values are summed across the criteria to get a total score – hence, these are additive value models – by which the alternatives are prioritized or ranked (or otherwise classified) relative to each other. Thus, a value model (or 'points system') is simply a schedule of criteria (and categories) and point values for the decision problem at hand; for an example, see Table 1 in the sub-section below. This 'points system' representation is equivalent to a more traditional approach involving normalized criterion weights and 'single-criterion value functions' to represent the relative importance of the criteria and to combine values overall (see weighted sum model). The unweighted points system representation is easier to use and helps inform the explanation of the PAPRIKA method below.
An example application of a points system An example application of a points system is ranking candidates applying for a job. Imagine that 'Maartje', 'Michelle' and 'Paulien' are three job candidates to be ranked using the value model in Table 1 below. Suppose they are assessed on the five criteria (see Table 1) like this:
Maartje's education is excellent, she has > 5 years of experience, and her references, social skills and enthusiasm are all poor. Michelle's education is poor, she has 2–5 years of experience, and her references, social skills and enthusiasm are all good. Paulien's education is good, she has < 2 years of experience, and her references, social skills and enthusiasm are all good. Table 1: Example of a value model (points system) for ranking job candidates
Summing the point values in Table 1 corresponding to the descriptions for Maartje, Michelle and Paulien gives their total scores:
Maartje's total score = 40 + 10 + 0 + 0 + 0 = 50 points Michelle's total score = 0 + 3 + 27 + 10 + 13 = 53 points Paulien's total score = 8 + 0 + 27 + 10 + 13 = 58 points Clearly, Paulien has the highest total score. Therefore, according to the value model (and how Maartje, Michelle and Paulien were assessed) Paulien is the best candidate for the job. (Though, clearly, relative to other candidates who could potentially have applied, Paulien is not as good as the best hypothetically-possible candidate – who would score a 'perfect' 40 + 10 + 27 + 10 + 13 = 100 points.) In general terms, having specified the criteria and categories for a given value model, the challenge is to derive point values that accurately reflect the relative importance of the criteria and categories to the decision-maker. Deriving valid and reliable point values is arguably the most difficult task when creating a value model. The PAPRIKA method does this based on decision-makers' preferences as expressed using pairwise rankings of alternatives.
… excerpt ends here. Continue reading the full article.
