後悔駆動ニューラルネットワークによる人間のインタラクティブ学習の予測
Davide Marchiori1, Massimo Warglien
1Interdepartmental Center for Research Training in Economics and Management (CIFREM), University of Trento, Italy.
Science (New York, N.Y.)
|February 23, 2008
まとめ
ゲームにおける人間のインタラクティブな学習をモデル化したニューラルネットワークは,行動を正確に予測します. 後悔に基づくフィードバックは,予測を大幅に改善し,社会的学習のダイナミクスを理解するための伝統的な経済モデルを上回る.
科学分野:
- コグニティブ・サイエンス コグニティブ・サイエンス
- 神経科学は神経科学である.
- ゲーム理論はゲーム理論である.
背景:
- 社会的文脈における人間の学習は本質的にインタラクティブであり,個人の学習は他者の同時学習によって影響を受けます.
- ゲームは,インタラクティブな意思決定シナリオの標準モデルとして機能します.
- インタラクティブな学習の理解と予測は,社会的文脈において極めて重要です.
研究 の 目的:
- 繰り返されるゲーム環境における人間のインタラクティブ学習のモデリングと予測におけるニューラルネットワークの有効性を調査する.
- これらのモデルの予測精度に対する後悔に基づくフィードバックの影響を評価する.
- 既定の経済モデルに対する後悔ベースのニューラルネットワークモデルのパフォーマンスを比較する.
主な方法:
- 学習プロセスをシミュレートするために設計された単純なニューラルネットワークを利用した.
- 学習ネットワークに後悔に基づくフィードバックメカニズムを組み込みました.
- ユニークな混合戦略のバランスを特徴とする21の多様なゲームでモデルをテストしました.
- モデル予測を,実験環境で観察された人間の行動と比較した.
主要な成果:
- 後悔に基づくフィードバックを持つ基本的なニューラルネットワークでさえ,繰り返されたゲームにおける人間の行動を正確に予測しました.
- 後悔をフィードバックメカニズムに含めることで,ニューラルネットワークの予測性能が大幅に改善されました.
- 後悔に基づくモデルは,従来の経済モデルと比較して優れた予測能力を示した.
結論:
- ニューラルネットワークは,特に後悔に基づくフィードバックを組み込む場合,人間のインタラクティブな学習をモデル化するための強力なツールを提供します.
- 後悔は,社会的学習の重要な要因であり,コンピューティングモデルによって効果的に捉えることができます.
- これらの発見は,インタラクティブな意思決定とソーシャル・ラーニングの理解をコンピューティングのアプローチを通じて進めるための有望な方向性を示唆しています.
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