Efficient penalty scoring functions for group decision-making with TCP-nets

This paper studies the problem of collective decision-making in combinatorial domain where the agents' preferences are represented by qualitative models with TCP-nets (Tradeoffs-enhanced Conditional Preference Network). The features of TCP-nets enable us to easily encode human preferences and the relative importance between the decision variables; however, many group decision-making methods require numerical measures of degrees of desirability of alternative outcomes. To permit a natural way for preference elicitation while providing quantitative comparisons between outcomes, we present a computationally efficient approach that compiles individual TCP-nets into ordinal penalty scoring functions. After the individual penalty scores are computed, we further define a collective penalty scoring function to aggregate multiple agents' preferences.