OR/MS
independent rescue
residual independent coverage outside the pooled block
Active working paper
Identifies when pooled error contraction beats lost independent rescue, then separates the selected positive result from its equilibrium-selection boundary.
Active working paper28 pages
Nakajima, Yohei (2026). When Does Information Sharing Improve Decentralized Discovery? Aggregation, Independent Rescue, and Equilibrium Selection. arXiv:2609.01814 [cs.AI].
@misc{Nakajima2026InformationSharingFrontier,
author = {Yohei Nakajima},
title = {When Does Information Sharing Improve Decentralized Discovery? Aggregation, Independent Rescue, and Equilibrium Selection},
year = {2026},
howpublished = {arXiv preprint},
eprint = {2609.01814},
archivePrefix = {arXiv},
primaryClass = {cs.AI},
doi = {10.48550/arXiv.2609.01814},
url = {https://arxiv.org/abs/2609.01814},
note = {v1, primary category cs.AI, cross-list cs.GT; DOI registration pending at observation; not peer reviewed}
}Read the build source · Inspect validation and provenance
scientific: valid-with-scope · editorial: active · publication: submitted · lifecycle: active-working-paper · DOI 10.48550/arXiv.2609.01814 · DOI registration pending at observation · arXiv 2609.01814 [cs.AI, cs.GT] · arXiv license: arXiv perpetual non-exclusive distribution license · arXiv submitted · not peer reviewed
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Translation layer
These formulations translate a canonical object without changing its scientific scope.
OR/MS
residual independent coverage outside the pooled block
Economics / AGT
outside option value of unpooled search attempts
Multi-agent AI
agents kept on independent task attempts can recover pooled misses
Practitioner
keep some independent attempts available to catch what the shared answer misses
OR/MS
cardinality-constrained posterior coverage frontier
Economics / AGT
value of an experiment under a portfolio-action decision problem
Multi-agent AI
best exact task coverage available with L coordinated agent actions
Practitioner
how much the group can cover with one two or more distinct attempts
OR/MS
minimum resource budget meeting a benchmark value
Economics / AGT
smallest action capacity restoring a declared outside option
Multi-agent AI
number of distinct coordinated agent actions needed to match a private baseline
OR/MS
error-contraction versus residual-coverage frontier
Economics / AGT
aggregation benefit net of decentralized action compression
Multi-agent AI
shared context helps only if better joint inference beats lost task diversity
Practitioner
share when the map improves faster than the portfolio narrows
OR/MS
centralized portfolio recovery benchmark
Economics / AGT
full-information team benchmark with action assignment
Multi-agent AI
oracle task assignment from pooled evidence
Practitioner
what a coordinator could recover by assigning different attempts
OR/MS
implementation performance depends on the selected stable outcome
Economics / AGT
selected-equilibrium result with an adverse correspondence witness
Multi-agent AI
one coordination policy can look good while other compatible policies fail
Practitioner
a good outcome is not enough if the rules also permit bad ones