Optimal Delegation in Markets for Matching with Signaling
This paper studies a delegation problem faced by the planner who wants to regulate receivers' reaction choices in markets for matching between receivers and senders with signaling. We provide a noble insight into the planner's willingness to delegate and the design of optimal (reaction) interval delegation as a solution to the planner's general mechanism design problem. The relative heterogeneity of receiver types and the productivity of the sender' signal are crucial in deriving optimal interval delegation in the presence of the trade-off between matching efficiency and signaling costs.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Monotone Equilibrium in Matching Markets with Signaling
We introduce a notion of competitive signaling equilibrium (CSE) in one-to-one matching markets with a continuum of heterogeneous senders and receivers. We then study monotone CSE where equilibrium outcomes - sender acti…
Social Integration in Two-Sided Matching Markets
When several two-sided matching markets merge into one, it is inevitable that some agents will become worse off if the matching mechanism used is stable. I formalize this observation by defining the property of integrati…
Vocal Bursts Valence PredictionDelegation in Veto Bargaining
A proposer requires the approval of a veto player to change a status quo. Preferences are single peaked. Proposer is uncertain about Vetoer's ideal point. We study Proposer's optimal mechanism without transfers. Vetoer i…
Optimal Wage Band for Job Matching with Signaling
We study an optimal wage band problem in which a policymaker sets a wage band prior to a competitive matching labor market, where education signals worker ability. We first prove the uniqueness of the competitive signali…
Data Market Design through Deep Learning
The $\textit{data market design}$ problem is a problem in economic theory to find a set of signaling schemes (statistical experiments) to maximize expected revenue to the information seller, where each experiment reveals…
Deep Learning