Optimal multi-unit combinatorial auctions

Gujar, Sujit; Narahari, Y.

doi:10.1007/s12351-011-0116-4

Cited by 5 publications

(4 citation statements)

References 13 publications

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“…Analogous to the literature on optimal auction [11,16,22], we assume regularity on our type distribution as follows. We say that a type distribution is regular if ∀i, H i is nondecreasing in c i and non-increasing in k i .…”

Section: Sufficiency Conditions For Optimalitymentioning

confidence: 99%

“…In our work, cost and capacity parameters constitute the private information and the valuation of the agents is not additive in these two parameters. Notably, Iyengar and Kumar [16] have designed optimal single item multi unit auction for capacitated bidders and this is further developed by Gujar and Narahari [11] for multi-item multi unit auctions. However, as pointed out in Section 1, the above works [11,16] assume that all agents are of the same quality.…”

Section: Positioning Of Our Workmentioning

confidence: 99%

“…Analogous to the literature on optimal auction [11,16,22], we assume regularity on our type distribution as follows.…”

Section: Sufficiency Conditions For Optimalitymentioning

confidence: 99%

“…[29] have proposed a greedy approach to learn the qualities of the crowd workers. On the other hand, if all the agents have the same quality that is common knowledge but with strategic costs and capacities, the auctioneer can deploy the techniques available in the literature [11,16] to elicit true costs and capacities. In the setting considered in this paper, in addition to strategic costs and capacities, we also address heterogeneity amongst agents and moreover we learn their qualities.…”

Section: Introductionmentioning

confidence: 99%

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An optimal bidimensional multi-armed bandit auction for multi-unit procurement

Bhat

Jain

Gujar

et al. 2019

Ann Math Artif Intell

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We study the problem of a buyer (aka auctioneer) who gains stochastic rewards by procuring multiple units of a service or item from a pool of heterogeneous strategic agents. The reward obtained for a single unit from an allocated agent depends on the inherent quality of the agent; the agent's quality is fixed but unknown. Each agent can only supply a limited number of units (capacity of the agent). The costs incurred per unit and capacities are private information of the agents. The auctioneer is required to elicit costs as well as capacities (making the mechanism design bidimensional) and further, learn the qualities of the agents as well, with a view to maximize her utility. Motivated by this, we design a bidimensional multi-armed bandit procurement auction that seeks to maximize the expected utility of the auctioneer subject to incentive compatibility and individual rationality while simultaneously learning the unknown qualities of the agents. We first assume that the qualities are known and propose an optimal, truthful mechanism 2D-OPT for the auctioneer to elicit costs and capacities. Next, in order to learn the qualities of the agents in addition, we provide sufficient conditions for a learning algorithm to be Bayesian incentive compatible and individually rational. We finally design a novel learning mechanism, 2D-UCB that is stochastic Bayesian incentive compatible and individually rational.

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Section: Sufficiency Conditions For Optimalitymentioning

confidence: 99%