Scholarlink

Competitive markets face a fundamental tension related to the Grossman-Stiglitz paradox: agents could benefit from aggregating predictions, yet sharing them destroys competitive advantage. We introduce the Distributed Prediction Ledger (DPL), a protocol that shifts from traditional value consensus to process consensus. This paradigm shift enables competitive agents to harness collective intelligence without revealing individual strategies. DPL orchestrates a unique combination of Byzantine fault tolerance, differential privacy, and verifiable delay functions within a hierarchical architecture, achieving optimal communication complexity while preserving prediction confidentiality. Our Master Theorem proves that DPL approximates optimal aggregation with formal privacy and robustness guarantees. The framework directly addresses real-world challenges in quantitative finance, where hedge funds using proprietary models could benefit from aggregated market insights but cannot share their strategies. This work opens a new research direction in distributed systems, transforming zero-sum competition into collaborative intelligence.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Distributed Prediction Ledger: A Theoretical Framework for Privacy-Preserving Consensus on Heterogeneous Continuous Values

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Walter Balzano,
  • Pasquale Miranda

摘要

Competitive markets face a fundamental tension related to the Grossman-Stiglitz paradox: agents could benefit from aggregating predictions, yet sharing them destroys competitive advantage. We introduce the Distributed Prediction Ledger (DPL), a protocol that shifts from traditional value consensus to process consensus. This paradigm shift enables competitive agents to harness collective intelligence without revealing individual strategies. DPL orchestrates a unique combination of Byzantine fault tolerance, differential privacy, and verifiable delay functions within a hierarchical architecture, achieving optimal communication complexity while preserving prediction confidentiality. Our Master Theorem proves that DPL approximates optimal aggregation with formal privacy and robustness guarantees. The framework directly addresses real-world challenges in quantitative finance, where hedge funds using proprietary models could benefit from aggregated market insights but cannot share their strategies. This work opens a new research direction in distributed systems, transforming zero-sum competition into collaborative intelligence.