Search arXiv⌕ Search

arXiv · 2409.09768

Balancing Selection Efficiency and Societal Costs in Selective Contests

Abstract

Selective contests can impair participants' overall welfare in overcompetitive environments, such as school admissions. This paper models the situation as an optimal contest design problem with binary actions, treating effort costs as societal costs incurred to achieve a desired level of selectivity. We provide a characterization for the feasible set of selection efficiency and societal cost in selective contests by establishing their relationship with feasible equilibrium strategies. We find that selection efficiency and contestants' welfare are complementary, i.e. it is almost impossible to improve one without sacrificing the other. We derive the optimal equilibrium outcome given the feasible set and characterize the corresponding optimal contest design. Our analysis demonstrates that it is always optimal for a contest designer who is sufficiently concerned with societal cost to intentionally introduce randomness into the contest. Furthermore, we show that the designer can optimize any linear payoff function by adjusting a single parameter related to the intensity of randomness, without altering the specific structure of the contest.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Penghuan Yan. 2024-10-04. Balancing Selection Efficiency and Societal Costs in Selective Contests. https://arxiv.org/abs/2409.09768

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Constraint Preferences: Inattention and Aggregation

We study robust decision problems when individuals have maxmin preferences whose belief sets are neighborhoods around reference models, commonly known as "constraint preferences." We first show that a more disciplined form of rationally inattentive behavior is equivalent to the behavior implied by a subclass of constraint preferences. We then introduce an aggregation principle that requires collective beliefs to satisfy every individual's constraint. This requirement links collective beliefs to the information-processing technologies that generate individual constraints. Applications reveal how these technologies determine asset prices, when prediction-market prices become self-confirming, and how much dynamic mechanisms can reduce information rents.

econ.TH↗

Insuring the Fallback: Capital, Monitoring, and the Certification of Preserved Human Capability under Improving AI

When generative AI makes the deliverable uninformative, a professional-services provider can still certify the preserved human capability to catch the machine's errors, through a liability pledge whose expected cost falls in that capability. The pledge is credible only up to what can be collected, and that ceiling is set by an underwriter, which bears part of the pledge and audits the insured. The range of client stakes over which one certificate separates has a width bounded by the provider's own capital plus the audited share of the underwriter's capacity, so unmonitored capacity adds nothing to it. Blind capital instead relocates that range upward, through a cross-subsidy that exists only under class rating. Monitoring converts capital into width, removes the cross-subsidy, raises the return to preserving skill, and, because audit information leaks, makes the certificate redundant beyond an interior precision.

econ.TH↗

Complementary Information Sources

A decision maker may have several information sources available and choose which one to consult only after learning the decision problem she faces. When is one such set of sources uniformly more valuable than another? For unrestricted Bayesian decision problems, we show that the answer can be stated entirely in terms of Blackwell comparisons. Form a tagged mixture by drawing a source independently of the state and revealing both its identity and its signal. One source set is more valuable in every decision problem if and only if each tagged mixture of the second source set is Blackwell dominated by some tagged mixture of the first. The result applies to compact, possibly infinite source sets and general signal spaces. It also has an exact quantitative counterpart: the largest normalized value shortfall is the directed Le Cam deficiency between the source sets' tagged hulls. The analogous program for monotone decision problems reveals a boundary. We call the passage from problem-by-problem source-set superiority to a problem-independent pairwise dominance a lifting. The Blackwell lifting does not extend directly to the Lehmann order: mixing sources that individually satisfy the monotone likelihood ratio property (MLRP) need not preserve MLRP, and even when it does, no fixed Lehmann-dominating mixture need exist. Requiring one source to serve a finite bundle of monotone decision problems restores the equivalence.

econ.TH↗