Budget feasible mechanism design

Singer, Yoram

doi:10.1145/2692359.2692366

Cited by 12 publications

(8 citation statements)

References 17 publications

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“…Procuring services subject to a budget constraint is also the subject of the literature on budget-feasible mechanisms initiated by Singer [10]. However, in this literature, the service of each seller is fixed and the utility of the buyer is a combinatorial function of the set of sellers the buyer picks.…”

Section: Related Work Vickerymentioning

confidence: 99%

“…When both bidders have type 100, by Equation (10), p i (100, 100) = 100 x i (100, 100). Therefore, total payments are maximized when x i (100, 100) = 1/2.…”

Section: Robust Revenue = Bayesian Revenuementioning

confidence: 99%

“…Total expected pseudo-surplus 5: Calculate the payment rule p using Equation (10) 6: return W , x, p Example 5.1. Suppose there are n bidders, with T i = {1}, for all bidders i ∈ N = {1, .…”

Section: Algorithm 3 Equi-marginal Principle Solvermentioning

confidence: 99%

“…Analogous to Algorithm 6, which describes a method to approximate RRM from below, Algorithm 7 approximates BRM from below. The only difference between these two algorithms is that Algorithm 6 uses Equation (10) to determine payments, whereas this new heuristic uses Equation (11). for all i ∈ N do 3:…”

Section: Bayesian Revenue Maximizationmentioning

confidence: 99%

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Optimal Auctions with Convex Perceived Payments

Greenwald,

Oyakawa,

Syrgkanis

2016

Preprint

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Myerson derived a simple and elegant solution to the single-parameter revenuemaximization problem in his seminal work on optimal auction design assuming the usual model of quasi-linear utilities. In this paper, we consider a slight generalization of this usual model-from linear to convex "perceived" payments. This more general problem does not appear to admit a solution as simple and elegant as Myerson's.While some of Myerson's results extend to our setting, like his payment formula (suitably adjusted), and the observation that "the mechanism is incentive compatible only if the allocation rule is monotonic," others do not. For example, we observe that the solutions to the Bayesian and the robust (i.e., non-Bayesian) optimal auction design problems in the convex perceived payment setting do not coincide like they do in the case of linear payments. We therefore study the two problems in turn.Myerson finds an optimal robust (and Bayesian) auction by solving pointwise: for each vector of virtual values, he finds an optimal, ex-post feasible auction; then he plugs the resulting allocation, which he first verifies is monotonic, into his payment formula. This strategy relies on a key theorem, that expected revenue equals expected virtual surplus, which does not hold in our setting. Still, we derive an upper and a heuristic lower bound on expected revenue in our setting. These bounds are easily computed pointwise, and yield monotonic allocation rules, so can be supported by Myerson payments (suitably adjusted). In this way, our bounds yield heuristics that approximate the optimal robust auction, assuming convex perceived payments.In tackling the Bayesian problem, we derive a mathematical program that improves upon the default formulation in that it has only polynomially-many payment variables; however, it still has exponentially-many allocation variables and ex-post feasibility constraints. To address this latter issue, we also study the ex-ante relaxation, which requires only polynomially-many constraints, in all. Specifically, we present a closedform solution to a straightforward relaxation of this relaxation. Then, following similar

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Section: Related Work Vickerymentioning

confidence: 99%

“…When both bidders have type 100, by Equation (10), p i (100, 100) = 100 x i (100, 100). Therefore, total payments are maximized when x i (100, 100) = 1/2.…”

Section: Robust Revenue = Bayesian Revenuementioning

confidence: 99%

Section: Algorithm 3 Equi-marginal Principle Solvermentioning

confidence: 99%

Section: Bayesian Revenue Maximizationmentioning

confidence: 99%

See 2 more Smart Citations

Optimal Auctions with Convex Perceived Payments

Greenwald,

Oyakawa,

Syrgkanis

2016

Preprint

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“…al. [13] considered a budgeted setting of the problem in [12] motivated by [14] for hiring k doctors out of the available n doctors (k < n), such that the total payment made to the ECs do not exceed the total budget of a patient. As opposed to the money involved hiring of ECs as mentioned in [12,13], another market of hiring ECs can be thought of where the ECs are providing their expertise free of cost.…”

Section: Introductionmentioning

confidence: 99%

Hiring Expert Consultants in E-Healthcare: An Analytics-Based Two Sided Matching Approach

Singh

Mukhopadhyay

Xhafa

et al. 2018

Transactions on Computational Collective Intelligence XXX

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Very often in some censorious healthcare scenario, there may be a need to have some expert consultancies (especially by doctors) that are not available in-house to the hospitals. Earlier, this interesting healthcare scenario of hiring the ECs (mainly doctors) from outside of the hospitals had been studied with the robust concepts of mechanism design with or without money. In this paper, we explore the more realistic two sided matching in our set-up, where the members of the two participating communities, namely patients and doctors are revealing the strict preference ordering over all the members of the opposite community for a stipulated amount of time. We assume that patients and doctors are strategic in nature. With the theoretical analysis, we demonstrate that the TOMHECs, that results in stable allocation of doctors to the patients is strategy-proof (or truthful ) and optimal. The proposed mechanisms are also validated with exhaustive experiments.

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