@article{Lundy_Fu_2020, title={Limitations of Incentive Compatibility on Discrete Type Spaces}, volume={34}, url={https://ojs.aaai.org/index.php/AAAI/article/view/5588}, DOI={10.1609/aaai.v34i02.5588}, abstractNote={<p>In the design of incentive compatible mechanisms, a common approach is to enforce incentive compatibility as constraints in programs that optimize over feasible mechanisms. Such constraints are often imposed on sparsified representations of the type spaces, such as their discretizations or samples, in order for the program to be manageable. In this work, we explore limitations of this approach, by studying whether all dominant strategy incentive compatible mechanisms on a set <em>T</em> of discrete types can be extended to the convex hull of <em>T</em>.</p><p>Dobzinski, Fu and Kleinberg (2015) answered the question affirmatively for all settings where types are single dimensional. It is not difficult to show that the same holds when the set of feasible outcomes is downward closed. In this work we show that the question has a negative answer for certain non-downward-closed settings with multi-dimensional types. This result should call for caution in the use of the said approach to enforcing incentive compatibility beyond single-dimensional preferences and downward closed feasible outcomes.</p>}, number={02}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Lundy, Taylor and Fu, Hu}, year={2020}, month={Apr.}, pages={2136-2143} }