Springer Nature OA
open access
Towards Agentic Agent-Based Models: Feasibility, Performance, and Statistical Model Checking
Agent-based models (ABMs) rely on simple, explicit and reproducible rules for individual decision making, while complex collective behavior emerges from interactions among agents. Recent advances in large language models (LLMs) make it tempting to replace, enrich, or perturb these rules with LLM-bas
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Bias Control and Injection for Multi-Agent Aggregation#1356626
- Engineering Simplicity: Simple Mechanism Interfaces Steer LLM Agents#1131207
- ECMR: Evidence-Aware Deliberation Control for Multi-Agent LLM Reasoning#1343070
- A Survey of Agentic Reasoning for Large Language Models: Towards Recursively Self-Improving and Collective Agents#1049743
- Drivers of LLM Consensus Formation Under Competing Incentives: A Simulation Study#1355131
Record · ID 1360659
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive