Dynamic Incentive Mechanisms

DSpace/Manakin Repository

Dynamic Incentive Mechanisms

Citable link to this page


Title: Dynamic Incentive Mechanisms
Author: Parkes, David C.; Cavallo, Ruggiero; Constantin, Florin; Singh, Satinder

Note: Order does not necessarily reflect citation order of authors.

Citation: Parkes, David C., Ruggiero Cavallo, Florin Constantin, and Satinder Singh. Forthcoming. Dynamic incentive mechanisms. Artificial Intelligence Magazine.
Full Text & Related Files:
Abstract: Much of AI is concerned with the design of
intelligent agents. A complementary challenge
is to understand how to design “rules of encounter”
(Rosenschein and Zlotkin 1994) by which
to promote simple, robust and beneficial interactions
between multiple intelligent agents. This is
a natural development, as AI is increasingly used
for automated decision making in real-world settings.
As we extend the ideas of mechanism design
from economic theory, the mechanisms (or rules)
become algorithmic and many new challenges surface.
Starting with a short background on mechanism
design theory, the aim of this paper is to provide
a non-technical exposition of recent results
on dynamic incentive mechanisms, which provide
rules for the coordination of agents in sequential
decision problems. The framework of dynamic
mechanism design embraces coordinated decision
making both in the context of uncertainty about
the world external to an agent and also in regard
to the dynamics of agent preferences. In addition
to tracing some recent developments, we point to
ongoing research challenges.
Published Version: http://www.aaai.org/ojs/index.php/aimagazine/index
Terms of Use: This article is made available under the terms and conditions applicable to Open Access Policy Articles, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#OAP
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:4481299
Downloads of this work:

Show full Dublin Core record

This item appears in the following Collection(s)


Search DASH

Advanced Search