177 research outputs found
A Mechanism for Fair Distribution of Resources without Payments
We design a mechanism for Fair and Efficient Distribution of Resources
(FEDoR) in the presence of strategic agents. We consider a multiple-instances,
Bayesian setting, where in each round the preference of an agent over the set
of resources is a private information. We assume that in each of r rounds n
agents are competing for k non-identical indivisible goods, (n > k). In each
round the strategic agents declare how much they value receiving any of the
goods in the specific round. The agent declaring the highest valuation receives
the good with the highest value, the agent with the second highest valuation
receives the second highest valued good, etc. Hence we assume a decision
function that assigns goods to agents based on their valuations. The novelty of
the mechanism is that no payment scheme is required to achieve truthfulness in
a setting with rational/strategic agents. The FEDoR mechanism takes advantage
of the repeated nature of the framework, and through a statistical test is able
to punish the misreporting agents and be fair, truthful, and socially
efficient. FEDoR is fair in the sense that, in expectation over the course of
the rounds, all agents will receive the same good the same amount of times.
FEDoR is an eligible candidate for applications that require fair distribution
of resources over time. For example, equal share of bandwidth for nodes through
the same point of access. But further on, FEDoR can be applied in less trivial
settings like sponsored search, where payment is necessary and can be given in
the form of a flat participation fee. To this extent we perform a comparison
with traditional mechanisms applied to sponsored search, presenting the
advantage of FEDoR
Quid Pro Quo: A Mechanism for Fair Collaboration in Networked Systems
Collaboration may be understood as the execution of coordinated tasks (in the
most general sense) by groups of users, who cooperate for achieving a common
goal. Collaboration is a fundamental assumption and requirement for the correct
operation of many communication systems. The main challenge when creating
collaborative systems in a decentralized manner is dealing with the fact that
users may behave in selfish ways, trying to obtain the benefits of the tasks
but without participating in their execution. In this context, Game Theory has
been instrumental to model collaborative systems and the task allocation
problem, and to design mechanisms for optimal allocation of tasks. In this
paper, we revise the classical assumptions and propose a new approach to this
problem. First, we establish a system model based on heterogenous nodes (users,
players), and propose a basic distributed mechanism so that, when a new task
appears, it is assigned to the most suitable node. The classical technique for
compensating a node that executes a task is the use of payments (which in most
networks are hard or impossible to implement). Instead, we propose a
distributed mechanism for the optimal allocation of tasks without payments. We
prove this mechanism to be robust event in the presence of independent selfish
or rationally limited players. Additionally, our model is based on very weak
assumptions, which makes the proposed mechanisms susceptible to be implemented
in networked systems (e.g., the Internet).Comment: 23 pages, 5 figures, 3 algorithm
Multi-round Master-Worker Computing: a Repeated Game Approach
We consider a computing system where a master processor assigns tasks for
execution to worker processors through the Internet. We model the workers
decision of whether to comply (compute the task) or not (return a bogus result
to save the computation cost) as a mixed extension of a strategic game among
workers. That is, we assume that workers are rational in a game-theoretic
sense, and that they randomize their strategic choice. Workers are assigned
multiple tasks in subsequent rounds. We model the system as an infinitely
repeated game of the mixed extension of the strategic game. In each round, the
master decides stochastically whether to accept the answer of the majority or
verify the answers received, at some cost. Incentives and/or penalties are
applied to workers accordingly. Under the above framework, we study the
conditions in which the master can reliably obtain tasks results, exploiting
that the repeated games model captures the effect of long-term interaction.
That is, workers take into account that their behavior in one computation will
have an effect on the behavior of other workers in the future. Indeed, should a
worker be found to deviate from some agreed strategic choice, the remaining
workers would change their own strategy to penalize the deviator. Hence, being
rational, workers do not deviate. We identify analytically the parameter
conditions to induce a desired worker behavior, and we evaluate experi-
mentally the mechanisms derived from such conditions. We also compare the
performance of our mechanisms with a previously known multi-round mechanism
based on reinforcement learning.Comment: 21 pages, 3 figure
Resource location based on precomputed partial random walks in dynamic networks
The problem of finding a resource residing in a network node (the
\emph{resource location problem}) is a challenge in complex networks due to
aspects as network size, unknown network topology, and network dynamics. The
problem is especially difficult if no requirements on the resource placement
strategy or the network structure are to be imposed, assuming of course that
keeping centralized resource information is not feasible or appropriate. Under
these conditions, random algorithms are useful to search the network. A
possible strategy for static networks, proposed in previous work, uses short
random walks precomputed at each network node as partial walks to construct
longer random walks with associated resource information. In this work, we
adapt the previous mechanisms to dynamic networks, where resource instances may
appear in, and disappear from, network nodes, and the nodes themselves may
leave and join the network, resembling realistic scenarios. We analyze the
resulting resource location mechanisms, providing expressions that accurately
predict average search lengths, which are validated using simulation
experiments. Reduction of average search lengths compared to simple random walk
searches are found to be very large, even in the face of high network
volatility. We also study the cost of the mechanisms, focusing on the overhead
implied by the periodic recomputation of partial walks to refresh the
information on resources, concluding that the proposed mechanisms behave
efficiently and robustly in dynamic networks.Comment: 39 pages, 25 figure
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