5,967 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
A Game Theoretic Analysis of Incentives in Content Production and Sharing over Peer-to-Peer Networks
User-generated content can be distributed at a low cost using peer-to-peer
(P2P) networks, but the free-rider problem hinders the utilization of P2P
networks. In order to achieve an efficient use of P2P networks, we investigate
fundamental issues on incentives in content production and sharing using game
theory. We build a basic model to analyze non-cooperative outcomes without an
incentive scheme and then use different game formulations derived from the
basic model to examine five incentive schemes: cooperative, payment, repeated
interaction, intervention, and enforced full sharing. The results of this paper
show that 1) cooperative peers share all produced content while non-cooperative
peers do not share at all without an incentive scheme; 2) a cooperative scheme
allows peers to consume more content than non-cooperative outcomes do; 3) a
cooperative outcome can be achieved among non-cooperative peers by introducing
an incentive scheme based on payment, repeated interaction, or intervention;
and 4) enforced full sharing has ambiguous welfare effects on peers. In
addition to describing the solutions of different formulations, we discuss
enforcement and informational requirements to implement each solution, aiming
to offer a guideline for protocol designers when designing incentive schemes
for P2P networks.Comment: 31 pages, 3 figures, 1 tabl
Computing large market equilibria using abstractions
Computing market equilibria is an important practical problem for market
design (e.g. fair division, item allocation). However, computing equilibria
requires large amounts of information (e.g. all valuations for all buyers for
all items) and compute power. We consider ameliorating these issues by applying
a method used for solving complex games: constructing a coarsened abstraction
of a given market, solving for the equilibrium in the abstraction, and lifting
the prices and allocations back to the original market. We show how to bound
important quantities such as regret, envy, Nash social welfare, Pareto
optimality, and maximin share when the abstracted prices and allocations are
used in place of the real equilibrium. We then study two abstraction methods of
interest for practitioners: 1) filling in unknown valuations using techniques
from matrix completion, 2) reducing the problem size by aggregating groups of
buyers/items into smaller numbers of representative buyers/items and solving
for equilibrium in this coarsened market. We find that in real data
allocations/prices that are relatively close to equilibria can be computed from
even very coarse abstractions
Cross-layer distributed power control: A repeated games formulation to improve the sum energy-efficiency
The main objective of this work is to improve the energy-efficiency (EE) of a
multiple access channel (MAC) system, through power control, in a distributed
manner. In contrast with many existing works on energy-efficient power control,
which ignore the possible presence of a queue at the transmitter, we consider a
new generalized cross-layer EE metric. This approach is relevant when the
transmitters have a non-zero energy cost even when the radiated power is zero
and takes into account the presence of a finite packet buffer and packet
arrival at the transmitter. As the Nash equilibrium (NE) is an
energy-inefficient solution, the present work aims at overcoming this deficit
by improving the global energy-efficiency. Indeed, as the considered system has
multiple agencies each with their own interest, the performance metric
reflecting the individual interest of each decision maker is the global
energy-efficiency defined then as the sum over individual energy-efficiencies.
Repeated games (RG) are investigated through the study of two dynamic games
(finite RG and discounted RG), whose equilibrium is defined when introducing a
new operating point (OP), Pareto-dominating the NE and relying only on
individual channel state information (CSI). Accordingly, closed-form
expressions of the minimum number of stages of the game for finite RG (FRG) and
the maximum discount factor of the discounted RG (DRG) were established. The
cross-layer model in the RG formulation leads to achieving a shorter minimum
number of stages in the FRG even for higher number of users. In addition, the
social welfare (sum of utilities) in the DRG decreases slightly with the
cross-layer model when the number of users increases while it is reduced
considerably with the Goodman model. Finally, we show that in real systems with
random packet arrivals, the cross-layer power control algorithm outperforms the
Goodman algorithm.Comment: 36 pages, single column draft forma
Approaching Utopia: Strong Truthfulness and Externality-Resistant Mechanisms
We introduce and study strongly truthful mechanisms and their applications.
We use strongly truthful mechanisms as a tool for implementation in undominated
strategies for several problems,including the design of externality resistant
auctions and a variant of multi-dimensional scheduling
On the Hardness of Signaling
There has been a recent surge of interest in the role of information in
strategic interactions. Much of this work seeks to understand how the realized
equilibrium of a game is influenced by uncertainty in the environment and the
information available to players in the game. Lurking beneath this literature
is a fundamental, yet largely unexplored, algorithmic question: how should a
"market maker" who is privy to additional information, and equipped with a
specified objective, inform the players in the game? This is an informational
analogue of the mechanism design question, and views the information structure
of a game as a mathematical object to be designed, rather than an exogenous
variable.
We initiate a complexity-theoretic examination of the design of optimal
information structures in general Bayesian games, a task often referred to as
signaling. We focus on one of the simplest instantiations of the signaling
question: Bayesian zero-sum games, and a principal who must choose an
information structure maximizing the equilibrium payoff of one of the players.
In this setting, we show that optimal signaling is computationally intractable,
and in some cases hard to approximate, assuming that it is hard to recover a
planted clique from an Erdos-Renyi random graph. This is despite the fact that
equilibria in these games are computable in polynomial time, and therefore
suggests that the hardness of optimal signaling is a distinct phenomenon from
the hardness of equilibrium computation. Necessitated by the non-local nature
of information structures, en-route to our results we prove an "amplification
lemma" for the planted clique problem which may be of independent interest
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