Structuring Collective Action with LLM-Guided Evolution: From Ill-Structured Problems to Executable Heuristics

ICLR 2026 Conference Submission18325 Authors

Keywords: Large Language Models (LLMs), Evolutionary Algorithms (EA), Program Synthesis, Code Heuristics, Hyper-heuristics, LLM-guided Search, Collective Action, Commons Governance, Mechanism Design & Nudging, Personalized Persuasion, Behavioral Messaging, Multi-agent Coordination, Ecological Connectivity

TL;DR: ECHO-MIMIC uses LLM-guided evolutionary search to evolve interpretable heuristics and personalized nudges that align many local agents to a global objective, turning ill-structured collective-action problems into tractable ones.

Abstract: Collective action problems, which require aligning individual incentives with collective goals, are classic examples of Ill-Structured Problems (ISPs). For an individual agent, the causal links between local actions and global outcomes are unclear, stakeholder objectives often conflict, and no single, clear algorithm can bridge micro-level choices with macro-level welfare. We present ECHO-MIMIC, a computational framework that converts this global complexity into a tractable, Well-Structured Problem (WSP) for each agent by discovering compact, executable heuristics and persuasive rationales. The framework operates in two stages: ECHO (Evolutionary Crafting of Heuristics from Outcomes) evolves snippets of Python code that encode candidate behavioral policies, while MIMIC (Mechanism Inference \& Messaging for Individual-to-Collective Alignment) evolves companion natural language messages that motivate agents to adopt those policies. Both phases employ a large-language-model-driven evolutionary search: the LLM proposes diverse and context-aware code or text variants, while population-level selection retains those that maximize collective performance in a simulated environment. We demonstrate this framework on a canonical ISP in agricultural landscape management, where local farming decisions impact global ecological connectivity. Results show that ECHO-MIMIC discovers high-performing heuristics compared to baselines and crafts tailored messages that successfully align simulated farmer behavior with landscape-level ecological goals. By coupling algorithmic rule discovery with tailored communication, ECHO-MIMIC transforms the cognitive burden of collective action into a simple set of agent-level instructions, making previously ill-structured problems solvable in practice and opening a new path toward scalable, adaptive policy design.

Primary Area: other topics in machine learning (i.e., none of the above)

Submission Number: 18325