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). 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 Environmental resources management and 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 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.
Matthias Grossglauser, Aswin Suresh, Chi Hsuan Wu
Francesco Mondada, Hadi Ardiny, Stefan John Witwicki