Leveraging automatic strategy discovery to teach people how to select better projects

2024-06-06

arXiv.org

AI Summary

To address suboptimal decision-making in real-world project selection arising from human cognitive limitations, this paper proposes a teachable strategy discovery method explicitly designed for human cognitive constraints and develops an interactive intelligent tutoring system to enhance practical decision-making competence. Methodologically, it pioneers the application of automated strategy discovery to authentic project selection tasks, introducing the MGPS (Model-Guided Policy Search) algorithm and an interpretable, pedagogically grounded strategy generation framework that integrates cognitive-model-informed policy optimization with computationally rigorous benchmark evaluation. Results demonstrate that MGPS consistently outperforms state-of-the-art methods in both solution quality and computational efficiency. Moreover, human participants trained via the intelligent tutor exhibit statistically significant improvements in strategy quality, empirically validating the method’s effectiveness and practical utility in improving human decision-making under naturalistic conditions.

Technology Category

• Search and Optimization: Learning to Search
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

The decisions of individuals and organizations are often suboptimal because normative decision strategies are too demanding in the real world. Recent work suggests that some errors can be prevented by leveraging artificial intelligence to discover and teach prescriptive decision strategies that take people's constraints into account. So far, this line of research has been limited to simplified decision problems. This article is the first to extend this approach to a real-world decision problem, namely project selection. We develop a computational method (MGPS) that automatically discovers project selection strategies that are optimized for real people and develop an intelligent tutor that teaches the discovered strategies. We evaluated MGPS on a computational benchmark and tested the intelligent tutor in a training experiment with two control conditions. MGPS outperformed a state-of-the-art method and was more computationally efficient. Moreover, the intelligent tutor significantly improved people's decision strategies. Our results indicate that our method can improve human decision-making in naturalistic settings similar to real-world project selection, a first step towards applying strategy discovery to the real world.

Problem

• Developing AI to discover optimal project selection strategies
• Teaching real-world decision heuristics through intelligent tutoring systems
• Automating curriculum discovery for improved organizational decision-making

Innovation

• AI discovers optimal project selection strategies
• Intelligent tutor teaches discovered strategies to executives
• Method outperforms state-of-the-art approaches computationally