16 research outputs found
Incentives and Efficiency in Uncertain Collaborative Environments
We consider collaborative systems where users make contributions across
multiple available projects and are rewarded for their contributions in
individual projects according to a local sharing of the value produced. This
serves as a model of online social computing systems such as online Q&A forums
and of credit sharing in scientific co-authorship settings. We show that the
maximum feasible produced value can be well approximated by simple local
sharing rules where users are approximately rewarded in proportion to their
marginal contributions and that this holds even under incomplete information
about the player's abilities and effort constraints. For natural instances we
show almost 95% optimality at equilibrium. When players incur a cost for their
effort, we identify a threshold phenomenon: the efficiency is a constant
fraction of the optimal when the cost is strictly convex and decreases with the
number of players if the cost is linear
Incentives-Based Mechanism for Efficient Demand Response Programs
In this work we investigate the inefficiency of the electricity system with
strategic agents. Specifically, we prove that without a proper control the
total demand of an inefficient system is at most twice the total demand of the
optimal outcome. We propose an incentives scheme that promotes optimal outcomes
in the inefficient electricity market. The economic incentives can be seen as
an indirect revelation mechanism that allocates resources using a
one-dimensional message space per resource to be allocated. The mechanism does
not request private information from users and is valid for any concave
customer's valuation function. We propose a distributed implementation of the
mechanism using population games and evaluate the performance of four popular
dynamics methods in terms of the cost to implement the mechanism. We find that
the achievement of efficiency in strategic environments might be achieved at a
cost, which is dependent on both the users' preferences and the dynamic
evolution of the system. Some simulation results illustrate the ideas presented
throughout the paper.Comment: 38 pages, 9 figures, submitted to journa
Utility Design for Distributed Resource Allocation -- Part I: Characterizing and Optimizing the Exact Price of Anarchy
Game theory has emerged as a fruitful paradigm for the design of networked
multiagent systems. A fundamental component of this approach is the design of
agents' utility functions so that their self-interested maximization results in
a desirable collective behavior. In this work we focus on a well-studied class
of distributed resource allocation problems where each agent is requested to
select a subset of resources with the goal of optimizing a given system-level
objective. Our core contribution is the development of a novel framework to
tightly characterize the worst case performance of any resulting Nash
equilibrium (price of anarchy) as a function of the chosen agents' utility
functions. Leveraging this result, we identify how to design such utilities so
as to optimize the price of anarchy through a tractable linear program. This
provides us with a priori performance certificates applicable to any existing
learning algorithm capable of driving the system to an equilibrium. Part II of
this work specializes these results to submodular and supermodular objectives,
discusses the complexity of computing Nash equilibria, and provides multiple
illustrations of the theoretical findings.Comment: 15 pages, 5 figure
Designing Coalition-Proof Reverse Auctions over Continuous Goods
This paper investigates reverse auctions that involve continuous values of
different types of goods, general nonconvex constraints, and second stage
costs. We seek to design the payment rules and conditions under which
coalitions of participants cannot influence the auction outcome in order to
obtain higher collective utility. Under the incentive-compatible
Vickrey-Clarke-Groves mechanism, we show that coalition-proof outcomes are
achieved if the submitted bids are convex and the constraint sets are of a
polymatroid-type. These conditions, however, do not capture the complexity of
the general class of reverse auctions under consideration. By relaxing the
property of incentive-compatibility, we investigate further payment rules that
are coalition-proof without any extra conditions on the submitted bids and the
constraint sets. Since calculating the payments directly for these mechanisms
is computationally difficult for auctions involving many participants, we
present two computationally efficient methods. Our results are verified with
several case studies based on electricity market data