Endogenize the information-censoring structure

Develop a formal mechanism-design or Bayesian-persuasion model in which a principal, such as a hiring platform or market intermediary, optimally designs the censoring threshold to maximize the searcher’s engagement or aggregate match quality.

Background

The paper takes the censoring rule as given and studies how lower- and upper-censoring affect Bayesian learning, monotonicity, and optimal stopping. The conclusion explicitly leaves open the question of how the information structure itself should be chosen.

The proposed direction would place the censoring threshold within a strategic information-design problem, allowing a principal to trade off the searcher’s engagement against outcomes such as aggregate match quality.

References

Several theoretical and applied extensions remain open. First, extending the framework to continuous payoff distributions or richer multidimensional state spaces requires moving beyond scalar single-crossing conditions. In such environments, the learning effect becomes multidimensional, and preserving monotonicity will likely demand strict shape restrictions on the posterior predictive distributions, such as log-concavity or the Monotone Likelihood Ratio Property (MLRP). Second, the information structure could be endogenized within a formal mechanism design or Bayesian persuasion framework. Rather than assuming an exogenous censoring rule, future research could analyze how a principal, such as a hiring platform or a market intermediary, optimally designs the censoring threshold to maximize the searcher's engagement or the aggregate match quality.

— Bayesian Sequential Search with Censored Observations  (2608.14326 - Lehrer et al., 14 Aug 2026) in Section 6, Final Remarks, subsection “Conclusion”