• Corpus ID: 240070417

Counterbalancing Learning and Strategic Incentives in Allocation Markets

@inproceedings{Kang2021CounterbalancingLA,
  title={Counterbalancing Learning and Strategic Incentives in Allocation Markets},
  author={Jamie Kang and Faidra Georgia Monachou and Moran Koren and Itai Ashlagi},
  booktitle={NeurIPS},
  year={2021}
}
Motivated by the high discard rate of donated organs in the United States, we study an allocation problem in the presence of learning and strategic incentives. We consider a setting where a benevolent social planner decides whether and how to allocate a single indivisible object to a queue of strategic agents. The object has a common true quality, good or bad, which is ex-ante unknown to everyone. Each agent holds an informative, yet noisy, private signal about the quality. To make a correct… 

Figures from this paper

References

SHOWING 1-10 OF 32 REFERENCES
Congested observational learning
Bayesian Exploration: Incentivizing Exploration in Bayesian Games
TLDR
The goal is to design a recommendation policy for the principal which respects agents' incentives and minimizes a suitable notion of regret, and shows how the principal can identify (and explore) all explorable actions, and use the revealed information to perform optimally.
Incentivizing Exploration with Unbiased Histories
TLDR
This paper studies a particular class of disclosure policies that use messages, called unbiased subhistories, consisting of the actions and rewards from by a subsequence of past agents, where the subsequence is chosen ahead of time.
Crowdsourcing Exploration
TLDR
A decentralized multi-armed bandit framework where a forward-looking principal commits upfront to a policy that dynamically discloses information regarding the history of outcomes to a series of short-lived rational agents, demonstrating that consumer surplus is non-monotone in the accuracy of the designer's information-provision policy.
Bayesian Exploration with Heterogeneous Agents
TLDR
This work considers Bayesian Exploration: a simple model in which the recommendation system (the “principal”) controls the information flow to the users and strives to incentivize exploration via information asymmetry, and allows heterogeneous users.
The Wisdom of the Crowd When Acquiring Information Is Costly
TLDR
The optimal policy that balances between the incentive to acquire information and the optimal investment decision is characterized, based on time-varying transparency levels such that it may be worthwhile to conceal some information in some periods.
Herding in Quality Assessment: An Application to Organ Transplantation
There are many economic environments in which an object is offered sequentially to prospective buyers. It is often observed that once the object for sale is turned down by one or more agents, those
The One-Shot Crowdfunding Game.
TLDR
This game models a standard crowdfunding setting as it is executed in popular crowdfunding platforms such as Kickstarter and Indiegogo and studies how well crowdfunding performs from the firm's perspective, in terms of market penetration, and how it performs fromThe case where the public good is excludable, agents have a common value and each agent receives a private signal about the common value.
Implementing the "Wisdom of the Crowd"
TLDR
The optimal disclosure policy of a planner whose goal is to maximizes social welfare is characterized, which is the implementation of what is known as the 'wisdom of the crowd'.
Information Aggregation, Rationality, and the Condorcet Jury Theorem
The Condorcet Jury Theorem states that majorities are more likely than any single individual to select the "better" of two alternatives when there exists uncertainty about which of the two
...
...