Introduces CPM for eliciting multivariate linear and univariate nonlinear utility functions via pre-specified coordinate-wise cuts and linear-system query design, with proven linear convergence rates and piecewise-linear extensions.
arXiv preprint arXiv:2003.01899 , year=
2 Pith papers cite this work. Polarity classification is still indexing.
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MUS designs preference questions using one probabilistic lottery and one deterministic outcome at the maximum utility range point to halve the ambiguity set of utility functions, converging to the true utility under moderate conditions via linear programming.
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Coordinate-wise Polyhedral Method for Eliciting Multivariate Linear Utility and Univariate Nonlinear Utility Functions
Introduces CPM for eliciting multivariate linear and univariate nonlinear utility functions via pre-specified coordinate-wise cuts and linear-system query design, with proven linear convergence rates and piecewise-linear extensions.
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Maximum Utility Split Method for Utility Preference Elicitation
MUS designs preference questions using one probabilistic lottery and one deterministic outcome at the maximum utility range point to halve the ambiguity set of utility functions, converging to the true utility under moderate conditions via linear programming.