PEMAND: Persona-Enriched Multi-Agent Negotiation for Household Decision-Making
Quick summary
arXiv:2604.10475v2 Announce Type: replace Abstract: Modeling household-level decisions is central to many real-world applications, including trip planning, residential mobility and migration, disaster management, etc. Existing studies primarily rely on classical machine learning models with limited predictive capacity, while recent LLM-based approaches have yet to incorporate behavioral theory or intra-household interaction dynamics, both of which are essential for modeling realistic household decisions. To address these limitations, we propose Persona-Enriched Multi-Agent Negotiation for hous
Key takeaways
- arXiv:2604.10475v2 Announce Type: replace Abstract: Modeling household-level decisions is central to many real-world applications, including trip planning, residential mobility and migration, disaster management, etc.
- Existing studies primarily rely on classical machine learning models with limited predictive capacity, while recent LLM-based approaches have yet to incorporate behavioral theory or intra-household interaction dynamics, both of which are essential for modeling realistic household decisions.
- To address these limitations, we propose Persona-Enriched Multi-Agent Negotiation for hous
Why it matters
The importance of “PEMAND: Persona-Enriched Multi-Agent Negotiation for Household Decision-Making” will be measured by what changes in practice. User behavior, access conditions, verifiable performance and responsible-use outcomes are the signals worth following.
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