Limitations of Incentive Compatibility on Discrete Type Spaces

by   Taylor Lundy, et al.

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 T of discrete types can be extended to the convex hull of T. 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.



page 1

page 2

page 3

page 4


Two-player incentive compatible mechanisms are affine maximizers

In mechanism design, for a given type space, there may be incentive comp...

Incentive Compatible Active Learning

We consider active learning under incentive compatibility constraints. T...

No-Regret and Incentive-Compatible Online Learning

We study online learning settings in which experts act strategically to ...

Mechanism Design without Money for Common Goods

We initiate the study of mechanism design without money for common goods...

Quantized VCG Mechanisms for Polymatroid Environments

Many network resource allocation problems can be viewed as allocating a ...

Ordinal Bayesian incentive compatibility in random assignment model

We explore the consequences of weakening the notion of incentive compati...

How Much is Performance Worth to Users? A Quantitative Approach

Architects and systems designers artfully balance multiple competing des...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.