Incomplete Preference

Incomplete preference modeling addresses the challenge of representing and utilizing preferences that are not fully specified, encompassing situations where individuals may be uncertain, inconsistent, or only partially reveal their choices. Current research focuses on developing algorithms and models, such as interactive genetic algorithms and max-margin optimization approaches, to efficiently elicit and utilize incomplete preferences in various decision-making contexts, including multi-criteria sorting and multi-objective optimization. This work has implications for improving decision support systems, designing more robust artificial agents (e.g., ensuring shutdownability), and enhancing fairness and efficiency in participatory budgeting and other social choice settings.