When Does Information Sharing Improve Decentralized Discovery? Aggregation, Independent Rescue, and Equilibrium Selection
cs.AI · cs.GT
Quick verdict
Exact synthetic models: sharing helps only when pooled error contracts faster than lost rescue capacity; rigorous, transparent, but small-scale self-synthesis.
Summary
This working paper asks when information sharing improves decentralized discovery, treating aggregation gains and the loss of independent 'rescue' actions as separate forces, in four layers. First, exact enumeration on a frozen M=4, N=3 channel registry shows one-person accuracy and even direct private discovery (two channels share 1/2 and 7/8) fail to determine centralized top-L portfolio value or recovery budgets (L*=1 to 3). Second, an incremental-sharing identity yields an exact break-even rule: adding a pooled agent helps precisely when residual pooled error contracts faster than the removed rescue factor, e_{s+1}/e_s < 1−q (Theorems 6, 9). Third, a bounded 177-scenario registry classifies curves as compression-dominated (126), aggregation-dominated (16), or all-neutral (35), with zero mixed curves preserved as a bounded null. Fourth, a two-agent Bayesian game with a hidden common/independent source mixture shows that, at p=3/5 and under named equilibrium selections, sharing strictly improves discovery exactly for ρ ∈ ((5√73−17)/48, 1); opposite constant-target equilibria attain discovery one and centralized top-two dominates, so the positive result is selection-dependent. Limitations: entirely synthetic finite models, rescue independence assumed, the strategic model is binary two-agent with p fixed at 3/5, sharing is truthful and costless, and the paper is an unreviewed synthesis of the author's own verified records (DD-019–DD-022) rather than a novel study.
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Assessment at a glance
5
risk flags identified
● high 0
● medium 3
● low 2
4 strengths · 0 weaknesses · 5 author questions
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Key claims under review
- One-person accuracy, and even the pair (accuracy, direct private discovery), does not determine the centralized top-L portfolio value or the minimum recovery budget (Table 2: two channels with q=1/2 and P3=7/8 need L*=3 versus L*=1).
- Under the registered independent-rescue protocol, an incremental sharing step improves discovery exactly when residual pooled error contracts faster than the removed rescue factor: e_{s+1}/e_s < 1−q (Theorems 6 and 9).
- In the frozen 177-scenario registry, sharing curves split into 126 compression-dominated, 16 aggregation-dominated, and 35 all-neutral, with zero mixed curves preserved as a bounded null rather than promoted to a theorem.
- At p=3/5, under named equilibrium selections, sharing strictly improves discovery and per-agent payoff exactly for ρ ∈ ((5√73−17)/48, 1); the result is selection-dependent, since discovery-one private equilibria exist and a centralized top-two planner strictly dominates.
Strengths
Exact, transparent break-even criterion for sharing
Theorem 6/9 reduces a sharing step's sign to e_{s+1}/e_s versus 1−q, with boundary cases (e_s=0, q=1) handled in Appendix A and sign agreement verified across all 2,044 registered transitions (1,848 negative, 196 neutral, 0 positive).
Sharp sufficiency-failure example with exact fractions
Tables 2-3 show two channels with identical q=1/2 and P3=7/8 whose centralized profiles are (7/12, 43/54, 25/27), L*=3 versus (17/18, 1, 1), L*=1 — a crisp demonstration that accuracy is not a sufficient statistic for portfolio value.
Unusual epistemic discipline against overclaiming
Strong provenance and reproducibility infrastructure
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Scope & Claims
This working paper (arXiv 2609.01814; cs.AI, cs.GT; self-declared 'not submitted, not peer reviewed') asks when information sharing improves decentralized discovery, treating the posterior-aggregation gain and the loss of independent 'rescue' actions as separate forces. It is explicitly a 'theorem-family synthesis' of the author's own verified records DD-019 through DD-022 (claims DD-C-0089 to DD-C-0110) and states it creates no new scientific claim. Four layers: (1) centralized action-budget profiles on a frozen M=4, N=3 channel registry (five families: noisy point, noisy shortlist, guaranteed shortlist, exclusion, confidence point); (2) an incremental-sharing identity; (3) a general residual-error frontier plus a bounded 177-scenario census; (4) a two-agent binary Bayesian game with a hidden common/independent source mixture solved at p=3/5. Headline claims: the sufficiency failure of one-person accuracy (Props 3-5), the exact break-even frontier e_{s+1}/e_s < 1−q (Thms 6, 9), the bounded classification 126/16/35/0 (Props 10-12), and the selection-dependent positive interval ρ ∈ ((5√73−17)/48, 1) (Thms 15-19).
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Frequently asked questions
When does information sharing improve decentralized discovery according to this paper?
Under the paper's registered independent-rescue protocol, a sharing step improves discovery exactly when the pooled block's residual error contracts faster than the independent rescue action it removes: formally e_{s+1}/e_s < 1−q, where q is each remaining private searcher's success probability. One-person accuracy alone does not decide this, because posterior error can be concentrated or diffuse across candidates.
What is the main positive result of the two-agent game?
At signal accuracy p=3/5 and under the paper's named equilibrium selections, sharing strictly improves discovery exactly when the common-source probability ρ lies in ((5√73−17)/48, 1), i.e., above roughly 0.536 up to 1. The authors stress this is selection-dependent: opposite constant-target equilibria attain discovery probability one, so it is not an every-equilibrium claim.
Is the paper based on real-world data or experiments?
No. The paper states explicitly that all results are exact computations on synthetic, finite, model-generated objects; no human or organizational data were collected and no experiment was run. The displayed counts are enumerations of registered model inputs, not observations.
Can I reproduce the paper's numbers?
Per Appendix D, all figures and tables are generated from checksum-validated immutable run outputs in a public GitHub repository (distributed-discovery, pinned commit), with exact fractions, SHA-256 manifests, deliberate corruption tests, and a twice-built byte-identical PDF. The arithmetic is simple enough to re-verify independently with a short script.
About this review
This is an AI-generated preliminary review by Evolve.Pub, produced from the openly available text of the paper. It is not peer review and does not represent the authors' views. The canonical record of the paper is at the publisher link above.
- Figures appear in the extraction only as captions; plotted curves (Figs. 1-7) could not be visually inspected, though their underlying exact values are tabulated in Tables 2, 3, 7 and Appendix B, so no number cited here relies on figure content.
- Mathematical notation is locally mangled by PDF extraction (e.g., equation (3) and the piecewise formulas in Table 6), but every quantity was recoverable from surrounding text, proofs, and tables.
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