The role of indicators to measure trends in every area of interest is increasing. Especially in the field of politics and sociology, where modeling based on multiple indicators typically is difficult, multi-indicator systems call for attention. Multi-indicator systems are most often the first step to derive a ranking indicator. Correspondingly there is a high interest in how to transform multi-indicator systems into a one-dimensional metric scale. The scientific discipline of decision support systems provides many well-known techniques, classical examples are PROMETHEE, ELECTRE, DEA or the simple TOPSIS. The mathematical technique is pretty sophisticated, therefore the simplest variant, namely the weighted sum of indicators plays its role too, just because of its simplicity and transparency. Although the need of a derivation of a one-dimensional scale is evident, we argue that there is an interim step, between the multi-indicator system and the ranking index, provided by simple elements of partial order theory. This interim step bears useful information too and in this paper we show how and which useful additional information can be derived. We derive for example a bias-free sensitivity study, where the indicator “chronic and sustained human flight” turns out to be the most important indicator within the multi-indicator system of 12 indicators, which are the basis of the Failed State Index.
