戦略的状況を分類し,人間の行動の背後にある動機を解読するために,大規模な言語モデルを使用する
Yutong Xie1, Qiaozhu Mei1, Walter Yuan2
1School of Information, University of Michigan, Ann Arbor, MI 48109.
まとめ
研究者は経済ゲームにおける 人間の行動を理解するために 大きな言語モデルを使いました プロンプトを分析することで 動機づけが明らかになり 集団間の行動の違いが 分類されます
科学分野:
- 行動経済学
- 人工知能
- コンピュータ社会科学
背景:
- 経済ゲームにおける 人間の意思決定を理解することは 極めて重要です
- 伝統的な方法は,しばしば自己報告されたデータや観察された行動に依存しています.
- 大型言語モデル (LLM) は,行動をシミュレートし分析するための新しいアプローチを提供します.
研究 の 目的:
- 経済ゲームにおける人間の行動の誘導と分析のためのLLMの使用を調査する.
- 素早い工学を用いて人間の行動の動機を推論する方法を開発する.
- 戦略的状況を分類し,異なる集団の行動傾向を比較する.
主な方法:
- 大きな言語モデルに与えられた 体系的に変化するプロンプトです
- 古典的な経済ゲームの文脈の中で誘発された行動を分析する.
- 迅速な対応パターンに基づく戦略シナリオを分類するための枠組みを開発する.
主要な成果:
- LLMは迅速な変化に基づいた経済ゲームで幅広い人間の行動をシミュレートすることができます.
- 素早い分析は 認知プロセスや 行動を誘発する 潜在的な動機についての洞察を提供します
- この方法は,異なる戦略的状況と人口レベルの行動の違いを分類することを可能にします.
結論:
- リアルタイム・エンジニアリングは 非標準で効果的な 人間の動機を解読する方法を提供します
- このアプローチは 行動経済学と戦略的意思決定の より深い理解を促進します
- LLMは様々なグループの行動傾向を比較分析する貴重なツールとして役立つ.
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