金融におけるハイブリッド意思決定の情報理論的コスト・ベネフィット分析
Philip Beaucamp1, Harvey Maylor2, Min Chen1
1Department of Engineering Science, University of Oxford, Oxford OX1 3QG, UK.
Entropy (Basel, Switzerland)
|August 28, 2025
まとめ
この研究は,ビジネスとファイナンスにおけるハイブリッド意思決定のワークフローを分析するための新しい定量的な方法を導入しています. データに基づくプロセスを最適化し,トレードオフを理解するために情報理論的コスト・ベネフィット分析を使用します.
科学分野:
- ビジネス アナリティクス
- 財務管理
- 情報理論
背景:
- データ主導の意思決定は ビジネスや金融の組織的成長に不可欠です
- 現在の方法は,ハイブリッドワークフローのコスト・ベネフィットを分析するための定量的なアプローチを欠いています.
- 人間と機械のプロセスの間のトレードオフを決定し,バイアスを定量化することは依然として課題です.
研究 の 目的:
- ビジネスとファイナンスにおけるハイブリッド意思決定ワークフローを分析するための方法論を開発する.
- 費用と利益の分析の枠組みに情報理論の概念を翻訳する.
- これらのワークフロー内の個々のプロセスのコスト/利益を定量的に推定します.
主な方法:
- 情報理論的アプローチと工学的アプローチ (ワークフロー分解) を組み合わせた.
- 定量的なコスト・ベネフィットの見積もりのために情報理論的尺度を使用した.
- 3つの異なるケーススタディで 方法論を適用した.
主要な成果:
- 提案された情報理論的コスト・ベネフィット分析の可行性を実証した.
- ケーススタディは統計的アルゴリズム,人間の知識と不完全な情報,そして認知バイアスをカバーしました.
- ハイブリッド意思決定のワークフローを分析するための定量的な基礎を提供しました.
結論:
- 提案された方法論は,データに基づいた意思決定のワークフローを最適化するための体系的かつ定量的なアプローチを提供します.
- この研究は,ビジネスと金融におけるコンピュータ支援による最適化に向けた早期の重要なステップです.
- 人間と機械のハイブリッドプロセスにおけるトレードオフとバイアスの理解を深める.
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