LLM4AAMAS: Add gurcan24fai

README.md

@@ -173,7 +173,7 @@ performance at inference time. Pretraining is crucial for novel tasks requiring
 new skills, whereas TTS is effective when base models already perform reasonably
 well.
 
-- **[LLM Post-Training: A Deep Dive into Reasoning Large Language Models](https://arxiv.org/abs/2502.21321)**  
+- **[LLM Post-Training: A Deep Dive into Reasoning Large Language Models](https://arxiv.org/abs/2502.21321)**
   Komal Kumar, Tajamul Ashraf, Omkar Thawakar, Rao Muhammad Anwer, Hisham
   Cholakkal, Mubarak Shah, Ming-Hsuan Yang, Phillip H. S. Torr, Salman Khan,
   Fahad Shahbaz Khan (2025) on *arXiv* (cs.CL).
@@ -400,6 +400,16 @@ objectives, and interact and communicate in natural language.
   [repository](https://github.com/tsinghua-fib-lab/LLM-Agent-Based-Modeling-and-Simulation).
 
 
+An LLM can be viewed as a non-deterministic simulator with the ability to
+role-play an endless array of characters.
+
+- **[LLM-Augmented Agent-Based Modelling for Social Simulations: Challenges and Opportunities](https://ebooks.iospress.nl/volumearticle/68007)**
+  *Önder Gürcan (2024)* Published in *HHAI 2024: Hybrid Human AI Systems for the
+  Social Good*, Frontiers in Artificial Intelligence and Applications, Volume
+  386, pages 134–144. DOI:
+  [10.3233/FAIA240190](https://doi.org/10.3233/FAIA240190)
+
+
 Simulacra studies the emergent social behaviors of a generative multi-agent
 simulation in an environment inspired by The Sims.
 
@@ -479,8 +489,8 @@ simple patterns, and may modify refined belief when taking actions.
 - **[Can Large Language Models Serve as Rational Players in Game Theory? A
   Systematic Analysis](https://ojs.aaai.org/index.php/AAAI/article/view/29751)**
   Caoyun Fan, Jindou Chen, Yaohui Jin, and Hao He (2024) *Presented at AAAI*,
-  **38**(16), 17960-17967. 
-  
+  **38**(16), 17960-17967.
+
 When LLM-based agents participate in various games designed to assess different
 traits—such as the dictator game (altruism), the ultimatum game (fairness), the
 trust game (trust, fairness, altruism, and reciprocity), the bomb risk game
@@ -546,8 +556,8 @@ behavior in the dictator game, resembling human rates of sharing with other
 humans. However, the AI agent shared substantially less of its endowment with
 the human experimenter or an anonymous charity than with other AI agents.
 
-- **[Evidence of behavior consistent with self-interest and altruism in an 
-  artificially intelligent agent](https://arxiv.org/abs/2301.02330)**  
+- **[Evidence of behavior consistent with self-interest and altruism in an
+  artificially intelligent agent](https://arxiv.org/abs/2301.02330)**
   Tim Johnson, Nick Obradovich (2023) on *arXiv*.
 
 
@@ -558,7 +568,7 @@ strategic scenarios, including the Water Allocation Challenge. The findings
 indicate that while LLMs are capable of accounting for long-term outcomes, they
 still show limitations in adaptability.
 
-- **[ALYMPICS: LLM Agents Meet Game Theory](https://aclanthology.org/2025.coling-main.193/)**  
+- **[ALYMPICS: LLM Agents Meet Game Theory](https://aclanthology.org/2025.coling-main.193/)**
   Shaoguang Mao, Yuzhe Cai, Yan Xia, Wenshan Wu, Xun Wang, Fengyi Wang, Qiang
   Guan, Tao Ge, Furu Wei (2025) at *COLING 2025*.
 
@@ -574,9 +584,9 @@ induction, and Bayesian belief updating. Interestingly, the model that initially
 performed the worst without the workflow becomes the best with it — and vice
 versa.
 
-- **[Game-theoretic LLM: Agent Workflow for Negotiation Games](https://arxiv.org/abs/2411.05990)**  
-  Wenyue Hua, Ollie Liu, Lingyao Li, Alfonso Amayuelas, Julie Chen, Lucas Jiang, Mingyu Jin, Lizhou Fan, 
-  Fei Sun, William Wang, Xintong Wang, Yongfeng Zhang (2024) on *arXiv*.  
+- **[Game-theoretic LLM: Agent Workflow for Negotiation Games](https://arxiv.org/abs/2411.05990)**
+  Wenyue Hua, Ollie Liu, Lingyao Li, Alfonso Amayuelas, Julie Chen, Lucas Jiang, Mingyu Jin, Lizhou Fan,
+  Fei Sun, William Wang, Xintong Wang, Yongfeng Zhang (2024) on *arXiv*.
 
 ### Generative MAS on the shelf