arXiv:2606.16989v1 Announce Type: cross Abstract: Using an open problem from the EC 2025 paper "Stable Menus of Public Goods" as a testbed, we conduct experiments to understand the effectiveness of different AI-for-EconCS research workflows. Specifically, we study three questions: Does providing human intuition in the prompt help? Does automated multi-turn interaction help? And, does an LLM outperform a first-year PhD student? Regarding the first two questions, we provide evidence for the following workflow suggestions: (1) prompting with human intuition can encourage the LLM to have better "t
The paper leverages a recent open problem from EC 2025, indicating an immediate application and exploration of current AI capabilities in economic contexts.
This study offers empirical insights into optimal AI-for-EconCS research workflows, which can accelerate progress in stable economic mechanism design and resource allocation, with potential broad societal benefits.
The understanding of how to best leverage LLMs in complex problem-solving, particularly in economic mechanism design, will be refined through structured workflows and an evaluation against human performance.
- · AI researchers
- · Economists using computational methods
- · AI-enabled platforms for complex decision-making
- · Traditional economic modeling approaches lacking AI integration
- · PhD students without AI augmentation
Refined methodologies for integrating AI, especially LLMs, into economic and computational social science research.
Accelerated development of AI agents capable of designing and optimizing complex economic systems.
Enhanced efficiency and fairness in allocating public goods, potentially leading to new forms of governance and resource management.
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Read at arXiv cs.AI