EDBT 2026 Demo / reviewers in the wild / expert
Zhou Ziheng
dblp:414/9574
· DBLP profile ↗
2ranked-venue papers
1as first author
2since 2021 · last 2026
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 1 first-author · 2 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
2 papers |
Multi-agent systems · 77% Language models and text generation · 12% Knowledge representation and reasoning · 12% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Multi-agent systems
agent-based simulation |
2.0 | 2 | 2026 | Why Are We Moral? An LLM-based Agent Simulation Approach to the Study of Moral Evolution · ACL (1) 2026 How do Role Models Shape Collective Morality? Exemplar-Driven Moral Learning in Multi-Agent Simulation · ACL (1) 2026 |
Natural language and speech › Language models and text generation
LLM agents |
0.3 | 1 | 2026 | How do Role Models Shape Collective Morality? Exemplar-Driven Moral Learning in Multi-Agent Simulation · ACL (1) 2026 |
Knowledge, reasoning and agents › Knowledge representation and reasoning › normative reasoning
moral reasoning |
0.3 | 1 | 2026 | Why Are We Moral? An LLM-based Agent Simulation Approach to the Study of Moral Evolution · ACL (1) 2026 |
Methods — techniques the papers use, named apart from their topics
large language model agents · 1.0large language model · 1.0agent-based simulation · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | How do Role Models Shape Collective Morality? Exemplar-Driven Moral Learning in Multi-Agent SimulationabstractWe investigate how role models shape collective morality. To explore this, we build a multi-agent simulation powered by a Large Language Models (LLMs), where agents with diverse intrinsic drives, ranging from cooperative to competitive, interact and adapt through a four-stage cognitive loop (plan-act-observe-reflect). We design four experimental games (Alignment, Collapse, Conflict, and Construction) and conduct motivational ablation studies to identify the key drivers of imitation. The results indicate that identity-driven conformity can substantially reshape the initial dispositions. Agents tend to adapt their values to align with a perceived successful exemplar, leading to rapid value convergence. Huacong Tang, Zhou Ziheng, Fangwei Zhong |
ACL (1) | 3 |
| 2026 | Why Are We Moral? An LLM-based Agent Simulation Approach to the Study of Moral EvolutionabstractZhou Ziheng, Huacong Tang, Mingjie Bi, Wanying He, Fang Sun, Yizhou Sun, Ying Nian Wu, Demetri Terzopoulos, Yipeng Kang, Fangwei Zhong. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026. Zhou Ziheng, Huacong Tang, Mingjie Bi, Wanying He, Yizhou Sun, Ying Nian Wu, Demetri Terzopoulos, Yipeng Kang, Fangwei Zhong |
ACL (1) | 1 |