14,529 research outputs found
Mechanism Design without Money via Stable Matching
Mechanism design without money has a rich history in social choice
literature. Due to the strong impossibility theorem by Gibbard and
Satterthwaite, exploring domains in which there exist dominant strategy
mechanisms is one of the central questions in the field. We propose a general
framework, called the generalized packing problem (\gpp), to study the
mechanism design questions without payment. The \gpp\ possesses a rich
structure and comprises a number of well-studied models as special cases,
including, e.g., matroid, matching, knapsack, independent set, and the
generalized assignment problem.
We adopt the agenda of approximate mechanism design where the objective is to
design a truthful (or strategyproof) mechanism without money that can be
implemented in polynomial time and yields a good approximation to the socially
optimal solution. We study several special cases of \gpp, and give constant
approximation mechanisms for matroid, matching, knapsack, and the generalized
assignment problem. Our result for generalized assignment problem solves an
open problem proposed in \cite{DG10}.
Our main technical contribution is in exploitation of the approaches from
stable matching, which is a fundamental solution concept in the context of
matching marketplaces, in application to mechanism design. Stable matching,
while conceptually simple, provides a set of powerful tools to manage and
analyze self-interested behaviors of participating agents. Our mechanism uses a
stable matching algorithm as a critical component and adopts other approaches
like random sampling and online mechanisms. Our work also enriches the stable
matching theory with a new knapsack constrained matching model
The tropical shadow-vertex algorithm solves mean payoff games in polynomial time on average
We introduce an algorithm which solves mean payoff games in polynomial time
on average, assuming the distribution of the games satisfies a flip invariance
property on the set of actions associated with every state. The algorithm is a
tropical analogue of the shadow-vertex simplex algorithm, which solves mean
payoff games via linear feasibility problems over the tropical semiring
. The key ingredient in our approach is
that the shadow-vertex pivoting rule can be transferred to tropical polyhedra,
and that its computation reduces to optimal assignment problems through
Pl\"ucker relations.Comment: 17 pages, 7 figures, appears in 41st International Colloquium, ICALP
2014, Copenhagen, Denmark, July 8-11, 2014, Proceedings, Part
Automatic Probabilistic Program Verification through Random Variable Abstraction
The weakest pre-expectation calculus has been proved to be a mature theory to
analyze quantitative properties of probabilistic and nondeterministic programs.
We present an automatic method for proving quantitative linear properties on
any denumerable state space using iterative backwards fixed point calculation
in the general framework of abstract interpretation. In order to accomplish
this task we present the technique of random variable abstraction (RVA) and we
also postulate a sufficient condition to achieve exact fixed point computation
in the abstract domain. The feasibility of our approach is shown with two
examples, one obtaining the expected running time of a probabilistic program,
and the other the expected gain of a gambling strategy.
Our method works on general guarded probabilistic and nondeterministic
transition systems instead of plain pGCL programs, allowing us to easily model
a wide range of systems including distributed ones and unstructured programs.
We present the operational and weakest precondition semantics for this programs
and prove its equivalence
A stochastic user-operator assignment game for microtransit service evaluation: A case study of Kussbus in Luxembourg
This paper proposes a stochastic variant of the stable matching model from
Rasulkhani and Chow [1] which allows microtransit operators to evaluate their
operation policy and resource allocations. The proposed model takes into
account the stochastic nature of users' travel utility perception, resulting in
a probabilistic stable operation cost allocation outcome to design ticket price
and ridership forecasting. We applied the model for the operation policy
evaluation of a microtransit service in Luxembourg and its border area. The
methodology for the model parameters estimation and calibration is developed.
The results provide useful insights for the operator and the government to
improve the ridership of the service.Comment: arXiv admin note: substantial text overlap with arXiv:1912.0198
Generalized Kruithof approach for traffic matrix estimation
[Abstract]: In this paper, the traffic matrix estimation problem is formulated as an nonlinear optimization problem based on the generalized Kruithof approach which uses the Kullback distance to measure the probabilistic distance between two traffic matrices. In addition, an algorithm using the affine scaling method is provided to solve the constraint optimization problem
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