Automatically Generated Weight Methods for Human and Machine Decision-Making

In real-life decision analysis, whether in human-deliberated situations or non-human (real-time semi-intelligent machine/agent) situations, there are well-documented problems regarding the elicitation of probabilities, utilities, and criteria weights. In this paper, we investigate automatic multi-criteria weight-generating methods with a detailed investigation method not seen before. The results confirm that the Sum Rank method for the ordinal case, and the corresponding Cardinal Sum Rank method for the cardinal case, outperform all other methods regarding robustness. New findings include that there is no indication that the difference in the results in the weight generation is diminished as the number of degrees of freedom grows which was previously thought to be true. Further, as expected the cardinal models outperform the ordinal models. More unexpectedly, though, the performance of the dominance intensity-based weight models is at most mediocre for some combinations and not even suitable for other combinations. Another insight from the investigation in this paper is that previous literature is not homogeneous in the modelling of the attribute values, resulting in not all methods considered in this investigation can be directly compared.