Stable Menus of Public Goods: AI-Enabled Progress

2026-06-15 · Source: Artificial Intelligence · Field: Science & Research — Artificial Intelligence & Machine Learning, Research Methodology & Innovation · Depth: Expert, quick

Summary

An experiment using an open problem from the EC 2025 paper "Stable Menus of Public Goods" investigates the effectiveness of AI-for-EconCS research workflows. The study explored three key questions: whether providing human intuition in prompts aids large language models (LLMs), if automated multi-turn interaction improves performance, and how an LLM compares to a first-year PhD student. Findings indicate that prompting with human intuition can enhance an LLM's "taste," and multi-turn workflows are beneficial when they encourage "ambitious" steps. However, a comparison against an unpublished manuscript by senior authors revealed that the LLM was slightly less effective than a first-year PhD student. This research, published on 2026-06-15, provides workflow suggestions for integrating AI into economic and computer science research.

Key takeaway

For AI Scientists developing economic or computer science solutions, consider integrating human intuition directly into your LLM prompts to improve output quality. While multi-turn interactions can enhance ambitious problem-solving, be aware that current LLMs may still be slightly less effective than a first-year PhD student for complex open problems. Focus on workflow designs that encourage iterative, ambitious steps to maximize AI's contribution.

Key insights

AI-for-EconCS workflows benefit from human intuition and ambitious multi-turn interactions, though LLMs currently trail first-year PhD students.

Principles

• Human intuition improves LLM "taste."
• Multi-turn workflows aid ambitious steps.

Method

The study used an open problem from the EC 2025 "Stable Menus of Public Goods" paper as a testbed to compare LLM performance against human intuition, multi-turn interactions, and a PhD student.

In practice

• Incorporate human intuition in LLM prompts.
• Design multi-turn AI pipelines for ambition.

Topics

• AI-for-EconCS
• Large Language Models
• Prompt Engineering
• Multi-turn Interaction
• Public Goods Theory
• Research Workflows

Best for: AI Scientist, Research Scientist

Related on AIssential

• Mar 23, 2026ScienceVibe physics: The AI grad student — AI can significantly accelerate expert-guided theoretical physics research, but requires rigorous human oversight.
• Does Theory of Mind Improvement Really Benefit Human-AI Interactions? Empirical Findings from Interactive Evaluations — Static ToM benchmarks do not predict LLM performance in dynamic human-AI interactions.
• How do ML researchers actually use AI tools to improve their writing? [D] — LLMs offer efficiency in technical writing and coding but risk diminishing cognitive skills and originality if over-relied upon.
• Teaching Machines to Code: A Social Scientist’s Guide to LLM-Based Information Extraction — Multi-agent LLM pipelines excel at complex information extraction by mimicking human collaborative coding processes.
• 10 Years of AlphaGo: The Turning Point for AI | Thore Graepel & Pushmeet Kohli — AlphaGo's victory marked AI's transition beyond human-level intelligence, combining intuition and calculation to solve complex problems.
• Checking the math behind OpenAI and Anthropic’s latest headlines — OpenAI's new reasoning model disproved an 80-year-old math conjecture by systematically exploring overlooked solution paths.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.