11 research outputs found
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Approximately-Strategyproof and Tractable Multi-Unit Auctions
We present an approximately-efficient and approximately-strategyproof auction mechanism for a single-good multiunit allocation problem. The bidding language allows marginal-decreasing piecewise-constant curves and quantity-based side constraints. We develop a fully polynomial-time approximation scheme for the multiunit allocation problem, which computes a (1+Īµ) approximation in worst-case time T=O(n3/Īµ), given n bids each with a constant number of pieces. We integrate this approximation scheme within a VickreyāClarkeāGroves (VCG) mechanism and compute payments for an asymptotic cost of O(T log n). The maximal possible gain from manipulation to a bidder in the combined scheme is bounded by ĪµV/(1+Īµ), where V is the total surplus in the efficient outcome.Engineering and Applied Science
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Mirage: A Microeconomic Resource Allocation System for Sensornet Testbeds
In this paper, we argue that a microeconomic resource allocation scheme, specifically the combinatorial auction, is well suited to testbed resource management. To demonstrate this, we present the Mirage resource allocation system. In Mirage, testbed resources are allocated using a repeated combinatorial auction within a closed virtual currency environment. Users compete for testbed resources by submitting bids which specify resource combinations of interest in space/time (e.g., "any 32 MICA2 motes for 8 hours anytime in the next three days") along with a maximum value amount the user is willing to pay. A combinatorial auction is then periodically run to determine the winning bids based on supply and demand while maximizing aggregate utility delivered to users. We have implemented a fully functional and secure prototype of Mirage and have been operating it in daily use for approximately four months on Intel Research Berkeley's 148-mote sensornet testbed.Engineering and Applied Science
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Quantifying the Strategyproofness of Mechanisms via Metrics on Payoff Distributions
Strategyproof mechanisms provide robust equilibrium
with minimal assumptions about knowledge and rationality but can be unachievable in combination with other desirable properties such as budget-balance, stability against deviations by coalitions, and computational tractability. In the search for maximally-strategyproof mechanisms
that simultaneously satisfy other desirable properties,
we introduce a new metric to quantify the strategyproofness of a mechanism, based on comparing the payoff distribution, given truthful reports, against that of a strategyproof āreferenceā mechanism that solves a problem relaxation.
Focusing on combinatorial exchanges, we demonstrate that the metric is informative about the eventual equilibrium, where simple regret-based metrics are not, and can be used for online selection of an effective mechanism.Engineering and Applied Science
Approximately-Strategyproof and Tractable Multi-Unit Auctions
We present an approximately-efficient and approximately-strategyproof auction mechanism for a single-good multi-unit allocation problem. The bidding language allows marginaldecreasing piecewise constant curves and quantity-based side constraints. We develop a fully polynomial-time approximation scheme for the multi-unit allocation problem, which computes a -approximation in worst-case time , given bids each with a constant number of pieces. We integrate this approximation scheme within a VickreyClarke -Groves mechanism and compute payments for an asymptotic cost of ! . The maximal possible gain from manipulation to a bidder in the combined scheme is bounded by 4294-16716 " is the total surplus in the efficient outcome
Stochastic Mechanisms for Truthfulness and Budget Balance in Computational Social Choice
In this thesis, we examine stochastic techniques for overcoming game theoretic and computational issues in the collective decision making process of self-interested individuals. In particular, we examine truthful, stochastic mechanisms, for settings with a strong budget balance constraint (i.e. there is no net flow of money into or away from the agents). Building on past results in AI and computational social choice, we characterise affine-maximising social choice functions that are implementable in truthful mechanisms for the setting of heterogeneous item allocation with unit demand agents. We further provide a characterisation of affine maximisers with the strong budget balance constraint. These mechanisms reveal impossibility results and poor worst-case performance that motivates us to examine stochastic solutions.
To adequately compare stochastic mechanisms, we introduce and discuss measures that capture the behaviour of stochastic mechanisms, based on techniques used in stochastic algorithm design. When applied to deterministic mechanisms, these measures correspond directly to existing deterministic measures. While these approaches have more general applicability, in this work we assess mechanisms based on overall agent utility (efficiency and social surplus ratio) as well as fairness (envy and envy-freeness).
We observe that mechanisms can (and typically must) achieve truthfulness and strong budget balance using one of two techniques: labelling a subset of agents as ``auctioneers'' who cannot affect the outcome, but collect any surplus; and partitioning agents into disjoint groups, such that each partition solves a subproblem of the overall decision making process. Worst-case analysis of random-auctioneer and random-partition stochastic mechanisms show large improvements over deterministic mechanisms for heterogeneous item allocation. In addition to this allocation problem, we apply our techniques to envy-freeness in the room assignment-rent division problem, for which no truthful deterministic mechanism is possible. We show how stochastic mechanisms give an improved probability of envy-freeness and low expected level of envy for a truthful mechanism. The random-auctioneer technique also improves the worst-case performance of the public good (or public project) problem.
Communication and computational complexity are two other important concerns of computational social choice. Both the random-auctioneer and random-partition approaches offer a flexible trade-off between low complexity of the mechanism, and high overall outcome quality measured, for example, by total agent utility. They enable truthful and feasible solutions to be incrementally improved on as the mechanism receives more information and is allowed more processing time.
The majority of our results are based on optimising worst-case performance, since this provides guarantees on how a mechanism will perform, regardless of the agents that use it. To complement these results, we perform empirical, average-case analyses on our mechanisms. Finally, while strong budget balance is a fixed constraint in our particular social choice problems, we show empirically that this can improve the overall utility of agents compared to a utility-maximising assignment that requires a budget imbalanced mechanism