Building Composite Indicators With Unweighted-TOPSIS

Composite indicators have been widely used in a large number of fields, including innovation and entrepreneurship as a useful tool for conveying summary information about overall performance in a relatively simple way. The construction of composite indicators implies several stages concerning collection of data, selection of criteria and individual indicators, normalization and weighting of criteria and indicators, aggregation, and comparison of overall performance of the alternatives or options. This article aims at contributing to the construction of synthetic indicators by showing with a real example, how the proposed methodology can overcome the problem of the establisment of the decision criteria relative importance. Determination of weighting schemes can be a controversial question for decision makers in those contexts where subjective weights are used. The method proposed in this article allows the ranking of a set of alternatives without the establishment of
a priori
criteria weights. In our proposal, based on TOPSIS, weights are unknown variables contributing to the optimization of the relative proximity of each alternative to an ideal solution.