The Graphical Hierarchy Process for decision making

Abstract

Decision making and appraisal often involve multicriteria evaluation since several variables are not easy to translate into money and in some cases they are not even quantitative. Any simple multicriteria procedure is based in a utility function that maps the n-dimensional space of the decision variables or criteria over the real segment [0, 1] to produce an ordered ranking. These methods are simple but are too “normative” in the sense that they clearly numerically rank the alternatives in the mentioned [0, 1] segment and decision makers do not like the “dictatorship of the numbers”. Any sophisticated multicriteria method that avoids this mapping is usually too difficult to be understood and used by decision makers. Decision makers may prefer a simple picture or drawing to see which alternatives are best according to different weights for the criteria as opposed to a numerical ranking. We present the Graphical Hierarchy Process, a multi-criteria decision making method based on graphical representations of preference maps: the contribution lays on the hierarchy process rather than in the mathematical algorithm for the aggregation process. By simulating all possible criteria weights with Monte Carlo simulation, the decision maker visualizes at a glance all the possibilities and the relevant issues in the decision making process. By working out a complex hierarchy tree with up to three branches, it is possible to take complex decisions…. with graphics (dots, segments or triangles). The Graphical Hierarchy Process may be of great interest for decision makers since the appraisal process (evaluation of each alternative according to each criteria) and the hierarchy process are objective and transparent. They still have some maneuvers to make the final decision since they have represented all the possible outcomes of all possible technical weights. This decision making procedure provides a new philosophy to understand and to implement rational decision making in complex environments and it can be of interest for planning in developing countries and to address complex issues like urban mobility.