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Shared Discovery Paradox: Pooling Info Can Harm Search, Study Finds

By PulseAugur Editorial · [2 sources] ·

A new paper titled "The Shared Discovery Paradox" explores how pooling information can paradoxically lead to worse search outcomes. The research introduces a benchmark with multiple agents and noisy clues, demonstrating that while pooling information improves individual recommendations, a "one-answer rule" drastically reduces group discovery. The paper suggests this is a protocol failure, not an information failure, and proposes solutions involving coordinated actions or modified reward structures to improve collective search efficiency. AI

IMPACT This research highlights potential pitfalls in designing AI systems that aggregate information, suggesting that protocol design is crucial for effective collective intelligence.

RANK_REASON The cluster contains a research paper published on arXiv detailing a new theoretical paradox and benchmark.

Read on arXiv cs.MA (Multiagent) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Shared Discovery Paradox: Pooling Info Can Harm Search, Study Finds

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The cluster contains a research paper published on arXiv detailing a new theoretical paradox and benchmark.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Yohei Nakajima ·

    The Shared Discovery Paradox: How a One-Answer Rule Turns Better Information into Worse Search

    arXiv:2607.18045v1 Announce Type: new Abstract: Organizations often pool dispersed information into one ranking and then allow many agents to act on that shared view. In a discovery problem, this can improve beliefs while reducing coverage. We develop an exactly solvable benchmar…

  2. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Yohei Nakajima ·

    The Shared Discovery Paradox: How a One-Answer Rule Turns Better Information into Worse Search

    Organizations often pool dispersed information into one ranking and then allow many agents to act on that shared view. In a discovery problem, this can improve beliefs while reducing coverage. We develop an exactly solvable benchmark with sixteen boxes, one target, eight searcher…

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