Optimal Dynamic Allocation: Simplicity through Information Design

Ashlagi, Itai; Monachou, Faidra; Nikzad, Afshin

doi:10.2139/ssrn.3610386

Cited by 6 publications

(6 citation statements)

References 30 publications

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“…We keep the rest of the model as presented in Section I unchanged. We then derive the optimal information structure given the 3 There are a number of recent papers that use the ironing techinque as introduced by Mussa and Rosen (1978) and Myerson (1979) to solve problems in optimal pricing and optimal gerrymandering-see, for example, Loertscher and Muir (2022); Ashlagi, Monachou, and Nikzad (2021); Dworczak, Kominers, and Akbarpour (2021); Kang (2020); and Kolotilin and Wolitzky (2020), respectively. Kleiner, Moldovanu, and Strack (2021) provide an elegant formulation of problems where majorization is added to the ironing problem that we can apply directly.…”

Section: Variations Of the Second-price Auctionmentioning

confidence: 99%

Optimal Information Disclosure in Classic Auctions

Bergemann

Heumann

Morris

et al. 2022

American Economic Review: Insights

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We characterize the revenue-maximizing information structure in the second-price auction. The seller faces a trade-off: more information improves the efficiency of the allocation but creates higher information rents for bidders. The information disclosure policy that maximizes the revenue of the seller is to fully reveal low values (where competition is high) but to pool high values (where competition is low). The size of the pool is determined by a critical quantile that is independent of the distribution of values and only dependent on the number of bidders. We discuss how this policy provides a rationale for conflation in digital advertising. (JEL D44, D82, D83, M37)

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Section: Variations Of the Second-price Auctionmentioning

confidence: 99%

Optimal Information Disclosure in Classic Auctions

Bergemann

Heumann

Morris

et al. 2022

American Economic Review: Insights

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“…[2020], Hu et al [2020], Ashlagi et al [2020], Anunrojwong et al [2021], Besbes and Mouchtaki [2021], and Ashlagi et al [2021] study information-sharing in the design and analysis of markets (see Bergemann and Bonatti [2019] for a survey).…”

Section: Related Literaturementioning

confidence: 99%

Optimal and Differentially Private Data Acquisition: Central and Local Mechanisms

Fallah¹,

Makhdoumi²,

Malekian³

et al. 2022

Preprint

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We consider a platform's problem of collecting data from privacy sensitive users to estimate an underlying parameter of interest. We formulate this question as a Bayesian-optimal mechanism design problem, in which an individual can share her (verifiable) data in exchange for a monetary reward or services, but at the same time has a (private) heterogeneous privacy cost which we quantify using differential privacy. We consider two popular differential privacy settings for providing privacy guarantees for the users: central and local. In both settings, we establish minimax lower bounds for the estimation error and derive (near) optimal estimators for given heterogeneous privacy loss levels for users. Building on this characterization, we pose the mechanism design problem as the optimal selection of an estimator and payments that will elicit truthful reporting of users' privacy sensitivities. Under a regularity condition on the distribution of privacy sensitivities we develop efficient algorithmic mechanisms to solve this problem in both privacy settings. Our mechanism in the central setting can be implemented in time O(n log n) where n is the number of users and our mechanism in the local setting admits a Polynomial Time Approximation Scheme (PTAS).* We would like to thank Kunal Talwar for helpful comments.

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“…Intuitively, when the planner has the power to ensure that the queue is not too long, FIFO dispatching is the most effective since it provides the strongest incentive for participants to join and to stay in the queue. Su and Zenios [2006] and Ashlagi et al [2020] study settings where an agent's value for an item depends on the type of the item and the private type of the agent. Su and Zenios [2006] design disjoint queue mechanisms that optimize either efficiency or equity (i.e.…”

Section: Related Workmentioning

confidence: 99%

“…the minimum utility across all agent types). Assuming that the value for a match is supermodular in the types of the agent and the item, Ashlagi et al [2020] establishes that a monotone disjoint queue mechanism is welfareoptimal. In both settings, agents cannot decline the allocated items.…”

Section: Related Workmentioning

confidence: 99%

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Randomized FIFO Mechanisms

Castro¹,

Ma²,

Nazerzadeh³

et al. 2021

Preprint

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We study the matching of jobs to workers in a queue, e.g. a ridesharing platform dispatching drivers to pick up riders at an airport. Under FIFO dispatching, the heterogeneity in trip earnings incentivizes drivers to cherry-pick, increasing riders' waiting time for a match and resulting in a loss of efficiency and reliability. We first present the direct FIFO mechanism, which offers lower-earning trips to drivers further down the queue. The option to skip the rest of the line incentivizes drivers to accept all dispatches, but the mechanism would be considered unfair since drivers closer to the head of the queue may have lower priority for trips to certain destinations. To avoid the use of unfair dispatch rules, we introduce a family of randomized FIFO mechanisms, which send declined trips gradually down the queue in a randomized manner. We prove that a randomized FIFO mechanism achieves the first best throughput and the second best revenue in equilibrium. Extensive counterfactual simulations using data from the City of Chicago demonstrate substantial improvements of revenue and throughput, highlighting the effectiveness of using waiting times to align incentives and reduce the variability in driver earnings.

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Optimal Dynamic Allocation: Simplicity through Information Design

Cited by 6 publications

References 30 publications

Optimal Information Disclosure in Classic Auctions

Optimal Information Disclosure in Classic Auctions

Optimal and Differentially Private Data Acquisition: Central and Local Mechanisms

Randomized FIFO Mechanisms

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