Decomposition Strategies for Constructive Preference Elicitation

We tackle the problem of constructive preference elicitation, that is the problem of learning user preferences over very large decision problems, involving a combinatorial space of possible outcomes. In this setting, the suggested configuration is synthesized on-the-fly by solving a constrained optimization problem, while the preferences are learned itera tively by interacting with the user. Previous work has shown that Coactive Learning is a suitable method for learning user preferences in constructive scenarios. In Coactive Learning the user provides feedback to the algorithm in the form of an improvement to a suggested configuration. When the problem involves many decision variables and constraints, this type of interaction poses a significant cognitive burden on the user. We propose a decomposition technique for large preference-based decision problems relying exclusively on inference and feedback over partial configurations. This has the clear advantage of drastically reducing the user cognitive load. Additionally, part-wise inference can be (up to exponentially) less computationally demanding than inference over full configurations. We discuss the theoretical implications of working with parts and present promising empirical results on one synthetic and two realistic constructive problems.Comment: Accepted at the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18

On Learnable Mechanism Design

Engineering and Applied Science

Does the value of quality of life depend on duration?

The aims of this study are to investigate the feasibility of eliciting Time Trade Off (TTO) valuations using short durations; to determine the effect of contrasting durations on individuals’ responses to the TTO; to examine variations within and between respondents’ values with respect to duration; and to consider the insights provided by participants’ comments and explanations regarding their reaction to duration in the valuation task. 27 participants provided TTO values using short and long durations for three EQ-5D states. Feedback was sought using a series of open ended questions. Of the 81 opportunities to observe it, strict constant proportionality was satisfied twice. 11 participants had no systematic relationship between duration and value; 11 provided consistently lower valuations in long durations, while 5 had higher valuations in long durations. Comments provided by participants were consistent with the values they provided. Mean TTO values did not differ markedly between alternative durations. We conclude that it is feasible to elicit TTO values for short durations. There is considerable heterogeneity in individuals’ responses to the time frames used to elicit values. Further research is required to ensure that the values used in cost effectiveness analysis adequately represent preferences about quality and length of life