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

• URL: http://arxiv.org/abs/2406.04082v1
• Date: Thu, 6 Jun 2024 13:51:44 GMT
• Title: Leveraging automatic strategy discovery to teach people how to select better projects
• Authors: Lovis Heindrich, Falk Lieder,
• Abstract summary: 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. This article is the first to extend this approach to a real-world decision problem, namely project selection.
• Score: 0.9821874476902969
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• SMART: Self-learning Meta-strategy Agent for Reasoning Tasks [44.45037694899524]
  We introduce SMART (Self-learning Meta-strategy Agent for Reasoning Tasks), a novel framework that enables LMs to learn and select the most effective strategies for various reasoning tasks. We model the strategy selection process as a Markov Decision Process and leverage reinforcement learning-driven continuous self-improvement. Our experiments demonstrate that SMART significantly enhances the ability of models to choose optimal strategies without external guidance.
  arXiv  Detail & Related papers  (2024-10-21T15:55:04Z)
• Combining AI Control Systems and Human Decision Support via Robustness and Criticality [53.10194953873209]
  We extend a methodology for adversarial explanations (AE) to state-of-the-art reinforcement learning frameworks. We show that the learned AI control system demonstrates robustness against adversarial tampering. In a training / learning framework, this technology can improve both the AI's decisions and explanations through human interaction.
  arXiv  Detail & Related papers  (2024-07-03T15:38:57Z)
• Risk-reducing design and operations toolkit: 90 strategies for managing risk and uncertainty in decision problems [65.268245109828]
  This paper develops a catalog of such strategies and develops a framework for them. It argues that they provide an efficient response to decision problems that are seemingly intractable due to high uncertainty. It then proposes a framework to incorporate them into decision theory using multi-objective optimization.
  arXiv  Detail & Related papers  (2023-09-06T16:14:32Z)
• A Reinforcement Learning-assisted Genetic Programming Algorithm for Team Formation Problem Considering Person-Job Matching [70.28786574064694]
  A reinforcement learning-assisted genetic programming algorithm (RL-GP) is proposed to enhance the quality of solutions. The hyper-heuristic rules obtained through efficient learning can be utilized as decision-making aids when forming project teams.
  arXiv  Detail & Related papers  (2023-04-08T14:32:12Z)
• On solving decision and risk management problems subject to uncertainty [91.3755431537592]
  Uncertainty is a pervasive challenge in decision and risk management. This paper develops a systematic understanding of such strategies, determine their range of application, and develop a framework to better employ them.
  arXiv  Detail & Related papers  (2023-01-18T19:16:23Z)
• Boosting human decision-making with AI-generated decision aids [8.373151777137792]
  We developed an algorithm for translating the output of our previous method into procedural instructions. Experiments showed that these automatically generated decision-aids significantly improved people's performance in planning a road trip and choosing a mortgage. These findings suggest that AI-powered boosting might have potential for improving human decision-making in the real world.
  arXiv  Detail & Related papers  (2022-03-05T15:57:20Z)
• Improving Human Sequential Decision-Making with Reinforcement Learning [29.334511328067777]
  We design a novel machine learning algorithm that is capable of extracting "best practices" from trace data. Our algorithm selects the tip that best bridges the gap between the actions taken by human workers and those taken by the optimal policy. Experiments show that the tips generated by our algorithm can significantly improve human performance.
  arXiv  Detail & Related papers  (2021-08-19T02:57:58Z)
• Decision Rule Elicitation for Domain Adaptation [93.02675868486932]
  Human-in-the-loop machine learning is widely used in artificial intelligence (AI) to elicit labels from experts. In this work, we allow experts to additionally produce decision rules describing their decision-making. We show that decision rule elicitation improves domain adaptation of the algorithm and helps to propagate expert's knowledge to the AI model.
  arXiv  Detail & Related papers  (2021-02-23T08:07:22Z)
• Improving Human Decision-Making by Discovering Efficient Strategies for Hierarchical Planning [0.6882042556551609]
  People need efficient planning strategies because their computational resources are limited. Our ability to compute those strategies used to be limited to very small and very simple planning tasks. We introduce a cognitively-inspired reinforcement learning method that can overcome this limitation.
  arXiv  Detail & Related papers  (2021-01-31T19:46:00Z)
• Automatic Discovery of Interpretable Planning Strategies [9.410583483182657]
  We introduce AI-Interpret, a method for transforming idiosyncratic policies into simple and interpretable descriptions. We show that prividing the decision rules generated by AI-Interpret as flowcharts significantly improved people's planning strategies and decisions.
  arXiv  Detail & Related papers  (2020-05-24T12:24:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.