Nathanael Hyafil
Department of Computer Science
University of Toronto
Toronto, ON M5S 3H5
email: nhyafil@cs.toronto.edu
Craig Boutilier
Department of Computer Science
University of Toronto
Toronto, ON M5S 3H5
email: cebly@cs.toronto.edu
Abstract
Classic direct mechanisms suffer from the drawback of requiring
full type (or utility function)
revelation from participating agents. In complex settings
with multi-attribute utility, assessing utility functions
can be very difficult,
a problem addressed by recent work on preference elicitation.
In this work
we propose a framework for incremental, partial revelation
mechanisms and study the use of minimax regret as
an optimization criterion for allocation determination with
type uncertainty. We examine the incentive properties of incremental
mechanisms when minimax regret is used to determine allocations
with no additional elicitation of payment information,
and when additional payment information is obtained. We argue that
elicitation effort can be focused simultaneously on reducing
allocation and payment uncertainty.
To appear, AAAI-2006
Return to List of Papers