Obviously Strategy-proof Mechanism Design With Rich Private Information

Strategy-proof allocation rules incentivize truthfulness in simultaneous move games, but real world mechanisms sometimes elicit preferences sequentially. Surprisingly, even when the underlying rule is strategy-proof and nonbossy, sequential elicitation can yield equilibria where agents have a strict incentive to be untruthful. This occurs only under incomplete information, when an agent anticipates that truthful reporting would signal false private information about others’ preferences. We provide conditions ruling out this phenomenon, guaranteeing all equilibrium outcomes to be welfare-equivalent to truthful ones. (JEL C73, D45, D82, D83)

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Mechanism design with financially constrained agents and costly verification

Theoretical Economics ◽

10.3982/te3907 ◽

2021 ◽

Vol 16 (3) ◽

pp. 1139-1194

Author(s):

Yunan Li

Keyword(s):

Mechanism Design ◽

Private Information ◽

Cash Transfers ◽

Cash Transfer ◽

Sales Tax ◽

Lower Price ◽

Two Stage ◽

Indivisible Good ◽

Stage Scheme ◽

Resale Market

A principal distributes an indivisible good to budget‐constrained agents when both valuation and budget are agents' private information. The principal can verify an agent's budget at a cost. The welfare‐maximizing mechanism can be implemented via a two‐stage scheme. First, agents report their budgets, receive cash transfers, and decide whether to enter a lottery over the good. Second, recipients of the good can sell it on a resale market but must pay a sales tax. Low‐budget agents receive a higher cash transfer, pay a lower price to enter the lottery, and face a higher sales tax. They are also randomly inspected.

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Mechanism Design for Facility Location Problems: A Survey

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2021/596 ◽

2021 ◽

Author(s):

Hau Chan ◽

Aris Filos-Ratsikas ◽

Bo Li ◽

Minming Li ◽

Chenhao Wang

Keyword(s):

Mechanism Design ◽

Facility Location ◽

Private Information ◽

Practical Importance ◽

Future Research ◽

Facility Location Problems ◽

Social Objective ◽

Comprehensive Survey ◽

Strategic Agents ◽

High Level

The study of approximate mechanism design for facility location has been in the center of research at the intersection of artificial intelligence and economics for the last decade, largely due to its practical importance in various domains, such as social planning and clustering. At a high level, the goal is to select a number of locations on which to build a set of facilities, aiming to optimize some social objective based on the preferences of strategic agents, who might have incentives to misreport their private information. This paper presents a comprehensive survey of the significant progress that has been made since the introduction of the problem, highlighting all the different variants and methodologies, as well as the most interesting directions for future research.

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The Limits of Incentives in Economic Matching Procedures

Management Science ◽

10.1287/mnsc.2020.3591 ◽

2020 ◽

Cited By ~ 3

Author(s):

Avinatan Hassidim ◽

Assaf Romm ◽

Ran I. Shorrer

Keyword(s):

Private Information ◽

Graduate Programs ◽

Revenue Maximization ◽

Matching Markets ◽

Positive Signal ◽

Best Interest ◽

High Stakes ◽

Field Evidence ◽

Deferred Acceptance ◽

Strategy Proof

Organizations often require agents’ private information to achieve critical goals such as efficiency or revenue maximization, but frequently it is not in the agents’ best interest to reveal this information. Strategy-proof mechanisms give agents incentives to truthfully report their private information. In the context of matching markets, they eliminate agents’ incentives to misrepresent their preferences. We present direct field evidence of preference misrepresentation under the strategy-proof deferred acceptance in a high-stakes matching environment. We show that applicants to graduate programs in psychology in Israel often report that they prefer to avoid receiving funding, even though the mechanism preserves privacy and funding comes with no strings attached and constitutes a positive signal of ability. Surveys indicate that other kinds of preference misrepresentation are also prevalent. Preference misrepresentation in the field is associated with weaker applicants. Our findings have important implications for practitioners designing matching procedures and for researchers who study them. This paper was accepted by Axel Ockenfels, decision analysis.

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An Auction Based Task Dispatching and Pricing Mechanism in Bike-sharing

10.21203/rs.3.rs-261715/v1 ◽

2021 ◽

Author(s):

Bing Shi ◽

Yaping Deng ◽

Han Yuan

Keyword(s):

Private Information ◽

Optimal Algorithm ◽

Incentive Compatibility ◽

Unit Cost ◽

Low Carbon ◽

Budget Balance ◽

Coverage Ratio ◽

Bike Sharing ◽

Dispatching Algorithm ◽

Strategy Proof

Abstract As a green and low-carbon transportation way, bike-sharing provides lots of convenience in the daily life. However, the daily usage of sharing bikes results in dispatching problems, i.e. dispatching bikes to the specific destinations. The bike-sharing platform can hire and pay to workers in order to incentivize them to accomplish the dispatching tasks. However, there exist multiple workers competing for the dispatching tasks, and they may strategically report their task accomplishing costs (which are private information only known by themselves) in order to make more profits, which may result in inefficient task dispatching results. In this paper, we first design a dispatching algorithm named GDY-MAX to allocate tasks to workers, which can achieve good performance. However it cannot prevent workers strategically misreporting their task accomplishing costs. Regarding this issue, we further design a strategy proof mechanism under the budget constraint, which consists of a task dispatching algorithm and a worker pricing algorithm. We theoretically prove that our mechanism can satisfy the properties of incentive compatibility, individual rationality and budget balance. Furthermore we run extensive experiments to evaluate our mechanism based on a dataset from Mobike. The results show that the performance of the proposed strategy proof mechanism and GDY-MAX is similar to the optimal algorithm in terms of the coverage ratio of accomplished task regions and the sum of task region values, and our mechanism has better performance than the uniform algorithm in terms of the total payment and the unit cost value.

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Mechanism Design for R&D Outsourcing with Double-Sided Moral Hazard and Double-Sided Adverse Selection

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.204-210.1569 ◽

2011 ◽

Vol 204-210 ◽

pp. 1569-1574

Author(s):

Xu Ding ◽

Wei Dong Meng ◽

Bo Huang ◽

Feng Ming Tao

Keyword(s):

Moral Hazard ◽

Mechanism Design ◽

Adverse Selection ◽

Private Information ◽

Incentive Mechanism ◽

Profit Sharing ◽

Transfer Payment

It is studied that how to use profit sharing arrangement as an incentive mechanism to stimulate both parties of R&D outsourcing to reveal their private information and commit enough R&D resources or efforts. First, it is proved that the double-sided moral hazard in R&D outsourcing can not be totally prevented under traditional profit-sharing arrangement, namely, fixed, proportional or mixed profit-sharing arrangement. And a new mixed profit sharing arrangement is proposed, which is composed of a fixed transfer payment and allocation proportion, and proved to be able to prevent the double-sided moral hazard, and motivate both parties to reveal their private information and commit enough efforts.

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