Large language model (LLM) agent-based modeling and simulation

A summary of works on LLM empowered agent-based modeling and simulation.

Our survey Large language models empowered agent-based modeling and simulation: a survey and perspectives is published in Humanities and Social Sciences Communications.

Please refer to the paper: link

Please cite our survey paper if you find our work helpful.

@article{gao2024large,
  title={Large language models empowered agent-based modeling and simulation: A survey and perspectives},
  author={Gao, Chen and Lan, Xiaochong and Li, Nian and Yuan, Yuan and Ding, Jingtao and Zhou, Zhilun and Xu, Fengli and Li, Yong},
  journal={Humanities and Social Sciences Communications},
  volume={11},
  number={1},
  pages={1--24},
  year={2024},
  publisher={Palgrave}
}

Table of Contents

• Challenges and approaches of LLM for ABS
  • Environment Construction and Interface
  • Human Alignment and Personalization
  • How to simulate actions
  • Evaluation of LLM agents
• Recent advances of LLM for ABS
  • Social Domain I: Social Sciences
    • Social Network Dynamics
    • Cooperation
    • Individual Social Behavior
  • Social Domain II: Economic System
    • Individual Economic Behavior
    • Interactive Economic Behavior
    • Economic System
  • Physical Domain
  • Cyber Domain
  • Hybrid Domain

Challenges and approaches of LLM for ABS

Environment Construction and Interface

Name	Paper	Venue	Code
ChatDev	Qian, C., Cong, X., Yang, C., Chen, W., Su, Y., Xu, J., ... & Sun, M. (2023). Communicative agents for software development. arXiv preprint arXiv:2307.07924.	arxiv	Python
RecAgent	Wang, L., Zhang, J., Chen, X., Lin, Y., Song, R., Zhao, W. X., & Wen, J. R. (2023). Recagent: A novel simulation paradigm for recommender systems. arXiv preprint arXiv:2306.02552.	arxiv
Generative Agents	Park, J. S., O'Brien, J., Cai, C. J., Morris, M. R., Liang, P., & Bernstein, M. S. (2023, October). Generative agents: Interactive simulacra of human behavior. In Proceedings of the 36th annual acm symposium on user interface software and technology (pp. 1-22).	UIST	Python
EconAgent	Li, N., Gao, C., Li, Y., & Liao, Q. (2023). Large language model-empowered agents for simulating macroeconomic activities. arXiv preprint arXiv:2310.10436.	ACL	Python
WebAgent	Gur, I., Furuta, H., Huang, A. V., Safdari, M., Matsuo, Y., Eck, D., & Faust, A. A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis. In The Twelfth International Conference on Learning Representations.	ICLR
UGI	Xu, F., Zhang, J., Gao, C., Feng, J., & Li, Y. (2023). Urban generative intelligence (ugi): A foundational platform for agents in embodied city environment. arXiv preprint arXiv:2312.11813.	Arxiv
Generative Agents	Park, J. S., O'Brien, J., Cai, C. J., Morris, M. R., Liang, P., & Bernstein, M. S. (2023, October). Generative agents: Interactive simulacra of human behavior. In Proceedings of the 36th annual acm symposium on user interface software and technology (pp. 1-22).	UIST	Python
GITM	Zhu, X., Chen, Y., Tian, H., Tao, C., Su, W., Yang, C., ... & Dai, J. (2023). Ghost in the minecraft: Generally capable agents for open-world environments via large language models with text-based knowledge and memory. arXiv preprint arXiv:2305.17144.	Arxiv

Human Alignment and Personalization

Name	Paper	Venue	Code
	Akata, E., Schulz, L., Coda-Forno, J., Oh, S. J., Bethge, M., & Schulz, E. (2023). Playing repeated games with large language models. arXiv preprint arXiv:2305.16867.	Arxiv
Suspicion-Agent	Guo, J., Yang, B., Yoo, P., Lin, B. Y., Iwasawa, Y., & Matsuo, Y. (2023). Suspicion-agent: Playing imperfect information games with theory of mind aware gpt-4. arXiv preprint arXiv:2309.17277.	Arxiv	Python
Alpacafarm	Dubois, Y., Li, C. X., Taori, R., Zhang, T., Gulrajani, I., Ba, J., ... & Hashimoto, T. B. (2024). Alpacafarm: A simulation framework for methods that learn from human feedback. Advances in Neural Information Processing Systems, 36.	Neurips	Python
Generative Agents	Park, J. S., O'Brien, J., Cai, C. J., Morris, M. R., Liang, P., & Bernstein, M. S. (2023, October). Generative agents: Interactive simulacra of human behavior. In Proceedings of the 36th annual acm symposium on user interface software and technology (pp. 1-22).	UIST	Python
Personalized Soups	Jang, J., Kim, S., Lin, B. Y., Wang, Y., Hessel, J., Zettlemoyer, L., ... & Ammanabrolu, P. (2023). Personalized soups: Personalized large language model alignment via post-hoc parameter merging. arXiv preprint arXiv:2310.11564.	Arxiv	Python

How to simulate actions

Name	Paper	Venue	Code
Generative Agents	Park, J. S., O'Brien, J., Cai, C. J., Morris, M. R., Liang, P., & Bernstein, M. S. (2023, October). Generative agents: Interactive simulacra of human behavior. In Proceedings of the 36th annual acm symposium on user interface software and technology (pp. 1-22).	UIST	Python
GITM	Zhu, X., Chen, Y., Tian, H., Tao, C., Su, W., Yang, C., ... & Dai, J. (2023). Ghost in the minecraft: Generally capable agents for open-world environments via large language models with text-based knowledge and memory. arXiv preprint arXiv:2305.17144.	Arxiv
Voyager	Wang, G., Xie, Y., Jiang, Y., Mandlekar, A., Xiao, C., Zhu, Y., ... & Anandkumar, A. Voyager: An Open-Ended Embodied Agent with Large Language Models. Transactions on Machine Learning Research.	TMLR	python
Adaplanner	Sun, H., Zhuang, Y., Kong, L., Dai, B., & Zhang, C. (2024). Adaplanner: Adaptive planning from feedback with language models. Advances in Neural Information Processing Systems, 36.	Neurips	python
AucArena	Chen, J., Yuan, S., Ye, R., Majumder, B. P., & Richardson, K. (2023). Put your money where your mouth is: Evaluating strategic planning and execution of llm agents in an auction arena. arXiv preprint arXiv:2310.05746.	Arxiv	python
S3	Gao, C., Lan, X., Lu, Z., Mao, J., Piao, J., Wang, H., ... & Li, Y. (2023). S $^ 3$: Social-network Simulation System with Large Language Model-Empowered Agents. arXiv preprint arXiv:2307.14984.	Arxiv
EconAgent	Li, N., Gao, C., Li, Y., & Liao, Q. (2023). Large language model-empowered agents for simulating macroeconomic activities. arXiv preprint arXiv:2310.10436.	ACL	Python
Reflexion	Shinn, N., Cassano, F., Gopinath, A., Narasimhan, K., & Yao, S. (2024). Reflexion: Language agents with verbal reinforcement learning. Advances in Neural Information Processing Systems, 36.	Neurips	Python

Evaluation of LLM agents

Name	Paper	Venue	Code
S3	Gao, C., Lan, X., Lu, Z., Mao, J., Piao, J., Wang, H., ... & Li, Y. (2023). S $^ 3$: Social-network Simulation System with Large Language Model-Empowered Agents. arXiv preprint arXiv:2307.14984.	Arxiv
EconAgent	Li, N., Gao, C., Li, Y., & Liao, Q. (2023). Large language model-empowered agents for simulating macroeconomic activities. arXiv preprint arXiv:2310.10436.	ACL	Python
LLM-eval-survey	Chang, Y., Wang, X., Wang, J., Wu, Y., Yang, L., Zhu, K., ... & Xie, X. (2024). A survey on evaluation of large language models. ACM Transactions on Intelligent Systems and Technology, 15(3), 1-45.	TIST

Recent advances of LLM for ABS

Social Domain I: Social Sciences

Social Network Dynamics

Name	Paper	Venue	Environment	What to Simulate	Code
	Schwitzgebel, E., Schwitzgebel, D., & Strasser, A. (2024). Creating a large language model of a philosopher. Mind & Language, 39(2), 237-259.	Mind & Language	Virtual	Conversation and Interaction
Werewolf	Xu, Y., Wang, S., Li, P., Luo, F., Wang, X., Liu, W., & Liu, Y. (2023). Exploring large language models for communication games: An empirical study on werewolf. arXiv preprint arXiv:2309.04658.	Arxiv	Virtual	Werewolf game
	Acerbi, A., & Stubbersfield, J. M. (2023). Large language models show human-like content biases in transmission chain experiments. Proceedings of the National Academy of Sciences, 120(44), e2313790120.	PNAS	Virtual	Information Propagation
MachineSoM	Zhang, J., Xu, X., & Deng, S. (2023). Exploring collaboration mechanisms for llm agents: A social psychology view. arXiv preprint arXiv:2310.02124.	Arxiv	Virtual	Collaboration Mechanism	Python
	Suzuki, R., & Arita, T. (2024). An evolutionary model of personality traits related to cooperative behavior using a large language model. Scientific Reports, 14(1), 5989.	Scientific Reports	Virtual	Cooperation and defection
	de Zarzà, I., de Curtò, J., Roig, G., Manzoni, P., & Calafate, C. T. (2023). Emergent cooperation and strategy adaptation in multi-agent systems: An extended coevolutionary theory with llms. Electronics, 12(12), 2722.	Electronics	Virtual	Social interaction
	Mukobi, G., Erlebach, H., Lauffer, N., Hammond, L., Chan, A., & Clifton, J. (2023). Welfare diplomacy: Benchmarking language model cooperation. arXiv preprint arXiv:2310.08901.	Arxiv	Real	Welfare diplomcy game	python
S3	Gao, C., Lan, X., Lu, Z., Mao, J., Piao, J., Wang, H., ... & Li, Y. (2023). S $^ 3$: Social-network Simulation System with Large Language Model-Empowered Agents. arXiv preprint arXiv:2307.14984.	Arxiv	Real	Online social network
SimReddit	Park, J. S., Popowski, L., Cai, C., Morris, M. R., Liang, P., & Bernstein, M. S. (2022, October). Social simulacra: Creating populated prototypes for social computing systems. In Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology (pp. 1-18).	UIST	Virtual	Online forum
GA-MAS	Cai, J., Li, J., Zhang, M., Li, M., Wang, C. S., & Tei, K. (2024). Language Evolution for Evading Social Media Regulation via LLM-based Multi-agent Simulation. arXiv preprint arXiv:2405.02858.	Arxiv	Real	Social media langauge	python
	Papachristou, M., & Yuan, Y. (2024). Network Formation and Dynamics Among Multi-LLMs. arXiv preprint arXiv:2402.10659.	Arxiv	Real	Social network dynamics

Cooperation

Name	Paper	Venue	Environment	What to Simulate	Code
COLA	Lan, X., Gao, C., Jin, D., & Li, Y. (2024, May). Stance detection with collaborative role-infused llm-based agents. In Proceedings of the International AAAI Conference on Web and Social Media (Vol. 18, pp. 891-903).	ICWSM	Real	Cooperative Task Solving	python
MAD	Liang, T., He, Z., Jiao, W., Wang, X., Wang, Y., Wang, R., ... & Shi, S. (2023). Encouraging divergent thinking in large language models through multi-agent debate. arXiv preprint arXiv:2305.19118.	Arxiv	Virtual	Cooperative Task Solving	python
ChatDev	Qian, C., Cong, X., Yang, C., Chen, W., Su, Y., Xu, J., ... & Sun, M. (2023). Communicative agents for software development. arXiv preprint arXiv:2307.07924.	Arxiv	Virtual	Cooperative Task Solving	Python
MetaGPT	Hong, S., Zhuge, M., Chen, J., Zheng, X., Cheng, Y., Wang, J., ... & Schmidhuber, J. MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework. In The Twelfth International Conference on Learning Representations.	ICLR	Virtual	Cooperative Task Solving	Python
ChatEval	Chan, C. M., Chen, W., Su, Y., Yu, J., Xue, W., Zhang, S., ... & Liu, Z. ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate. In The Twelfth International Conference on Learning Representations.	ICLR	Virtual	Cooperative Task Solving
CAMEL	Li, G., Hammoud, H., Itani, H., Khizbullin, D., & Ghanem, B. (2023). Camel: Communicative agents for" mind" exploration of large language model society. Advances in Neural Information Processing Systems, 36, 51991-52008.	Neurips	Virtual	Cooperative Task Solving	python
AgentVerse	Li, G., Hammoud, H., Itani, H., Khizbullin, D., & Ghanem, B. (2023). Camel: Communicative agents for" mind" exploration of large language model society. Advances in Neural Information Processing Systems, 36, 51991-52008.	Arxiv	Virtual	Cooperative Task Solving	python
SPP	Wang, Z., Mao, S., Wu, W., Ge, T., Wei, F., & Ji, H. (2024, June). Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) (pp. 257-279).	NAACL	Virtual	Cooperative Task Solving	python
CoELA	Zhang, H., Du, W., Shan, J., Zhou, Q., Du, Y., Tenenbaum, J. B., ... & Gan, C. (2023). Building cooperative embodied agents modularly with large language models. arXiv preprint arXiv:2307.02485.	ICLR	Real	Cooperative Task Solving	python

Individual Social Behavior

Name	Paper	Venue	Environment	What to Simulate	Code
Humanoid Agents	Wang, Z., Chiu, Y. Y., & Chiu, Y. C. (2023). Humanoid agents: Platform for simulating human-like generative agents. arXiv preprint arXiv:2310.05418.	EMNLP	Virtual	Individual social behavior	python
SocioDojo	Cheng, J., & Chin, P. (2024). Sociodojo: Building lifelong analytical agents with real-world text and time series. In The Twelfth International Conference on Learning Representations.	ICLR	Real	Individual social behavior	python
	Liu, R., Yang, R., Jia, C., Zhang, G., Zhou, D., Dai, A. M., ... & Vosoughi, S. (2023). Training socially aligned language models in simulated human society. arXiv preprint arXiv:2305.16960.	Arxiv	Virtual	Individual social behavior
Out of one, many	Argyle, L. P., Busby, E. C., Fulda, N., Gubler, J. R., Rytting, C., & Wingate, D. (2023). Out of one, many: Using language models to simulate human samples. Political Analysis, 31(3), 337-351.	Political Analysis	Virtual	Individual social behavior
	Hämäläinen, P., Tavast, M., & Kunnari, A. (2023, April). Evaluating large language models in generating synthetic hci research data: a case study. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (pp. 1-19).	CHI	Virtual	Individual social behavior
Mind meets machine	Dhingra, S., Singh, M., Vaisakh, S. B., Malviya, N., & Gill, S. S. (2023). Mind meets machine: Unravelling gpt-4’s cognitive psychology. BenchCouncil Transactions on Benchmarks, Standards and Evaluations, 3(3), 100139.	Arxiv	Virtual	Individual social behavior
	Binz, M., & Schulz, E. (2023). Using cognitive psychology to understand GPT-3. Proceedings of the National Academy of Sciences, 120(6), e2218523120.	PNAS	Virtual	Individual social behavior
	Elyoseph, Z., Hadar-Shoval, D., Asraf, K., & Lvovsky, M. (2023). ChatGPT outperforms humans in emotional awareness evaluations. Frontiers in Psychology, 14, 1199058.	Front. Psychol.	Virtual	Individual social behavior
	Li, X., Li, Y., Joty, S., Liu, L., Huang, F., Qiu, L., & Bing, L. (2022). Does gpt-3 demonstrate psychopathy? evaluating large language models from a psychological perspective. arXiv preprint arXiv:2212.10529.	Arxiv	Virtual	Individual social behavior
	Xie, C., & Zou, D. (2024). A Human-Like Reasoning Framework for Multi-Phases Planning Task with Large Language Models. arXiv preprint arXiv:2405.18208.	Arxiv	Virtual	Individual social behavior
User-Sim	Yoon, S. E., He, Z., Echterhoff, J. M., & McAuley, J. (2024). Evaluating Large Language Models as Generative User Simulators for Conversational Recommendation. arXiv preprint arXiv:2403.09738.	NAACL	Virtual	Individual social behavior	python

Social Domain II: Economic System

Individual Economic Behavior

Name	Paper	Venue	Environment	What to Simulate	Code
	Horton, J. J. (2023). Large language models as simulated economic agents: What can we learn from homo silicus? (No. w31122). National Bureau of Economic Research.	Arxiv	Virtual	Individual ecomonic behavior
	Chen, Y., Liu, T. X., Shan, Y., & Zhong, S. (2023). The emergence of economic rationality of GPT. Proceedings of the National Academy of Sciences, 120(51), e2316205120.	PNAS	Virtual	Individual ecomonic behavior
	Geerling, W., Mateer, G. D., Wooten, J., & Damodaran, N. (2023). ChatGPT has aced the test of understanding in college economics: Now what?. The American Economist, 68(2), 233-245.	The American Economist	Virtual	Individual ecomonic behavior
The wall street neophyte	Xie, Q., Han, W., Lai, Y., Peng, M., & Huang, J. (2023). The wall street neophyte: A zero-shot analysis of chatgpt over multimodal stock movement prediction challenges. arXiv preprint arXiv:2304.05351.	Arxiv	Virtual	Individual market behavior
	Faria-e-Castro, M., & Leibovici, F. (2024). Artificial Intelligence and Inflation Forecasts (No. 2023-015).		Virtual	Individual market behavior
	Bybee, L. (2023). Surveying Generative AI's Economic Expectations. arXiv preprint arXiv:2305.02823.	Arxiv	Virtual	Individual market behavior

Interactive Economic Behavior

Name	Paper	Venue	Environment	What to Simulate	Code
	Phelps, S., & Russell, Y. I. (2023). Investigating emergent goal-like behaviour in large language models using experimental economics. arXiv preprint arXiv:2305.07970.	Arxiv	Virtual	Game theory
	Akata, E., Schulz, L., Coda-Forno, J., Oh, S. J., Bethge, M., & Schulz, E. (2023). Playing repeated games with large language models. arXiv preprint arXiv:2305.16867.	Arxiv	Virtual	Game theory
Suspicion-Agent	Guo, J., Yang, B., Yoo, P., Lin, B. Y., Iwasawa, Y., & Matsuo, Y. (2023). Suspicion-agent: Playing imperfect information games with theory of mind aware gpt-4. arXiv preprint arXiv:2309.17277.	Arxiv	Virtual	Game theory	Python

Economic System

Name	Paper	Venue	Environment	What to Simulate	Code
CompeteAI	Zhao, Q., Wang, J., Zhang, Y., Jin, Y., Zhu, K., Chen, H., & Xie, X. (2023). Competeai: Understanding the competition behaviors in large language model-based agents. arXiv preprint arXiv:2310.17512.	Arxiv	Virtual	Consumption market	Python
SABM	Han, X., Wu, Z., & Xiao, C. (2023). " Guinea Pig Trials" Utilizing GPT: A Novel Smart Agent-Based Modeling Approach for Studying Firm Competition and Collusion. arXiv preprint arXiv:2308.10974.	Arxiv	Virtual	Consumption market	Python
SABM	Nascimento, N., Alencar, P., & Cowan, D. (2023, September). Self-adaptive large language model (llm)-based multiagent systems. In 2023 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C) (pp. 104-109). IEEE.	Arxiv	Virtual	Consumption market
AucArena	Chen, J., Yuan, S., Ye, R., Majumder, B. P., & Richardson, K. (2023). Put your money where your mouth is: Evaluating strategic planning and execution of llm agents in an auction arena. arXiv preprint arXiv:2310.05746.	Arxiv	Virtual	Auction market	python

Physical Domain

Name	Paper	Venue	Environment	What to Simulate	Code
LM-NAV	Shah, D., Osiński, B., & Levine, S. (2023, March). Lm-nav: Robotic navigation with large pre-trained models of language, vision, and action. In Conference on robot learning (pp. 492-504). PMLR.	CoRL	Real	Navigation behavior	python
NLMap	Chen, B., Xia, F., Ichter, B., Rao, K., Gopalakrishnan, K., Ryoo, M. S., ... & Kappler, D. (2023, May). Open-vocabulary queryable scene representations for real world planning. In 2023 IEEE International Conference on Robotics and Automation (ICRA) (pp. 11509-11522). IEEE.	ICRA	Real	Navigation behavior
	Zou, H., Zhao, Q., Bariah, L., Bennis, M., & Debbah, M. (2023). Wireless multi-agent generative ai: From connected intelligence to collective intelligence. arXiv preprint arXiv:2307.02757.	ICRA	Real	Wireless network users
	Cui, C., Ma, Y., Cao, X., Ye, W., & Wang, Z. (2024). Drive as you speak: Enabling human-like interaction with large language models in autonomous vehicles. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (pp. 902-909).	CVPR	Real	Vehicle drivers
GITM	Zhu, X., Chen, Y., Tian, H., Tao, C., Su, W., Yang, C., ... & Dai, J. (2023). Ghost in the minecraft: Generally capable agents for open-world environments via large language models with text-based knowledge and memory. arXiv preprint arXiv:2305.17144.	Arxiv	Real	Tool-usage in sandbox game

Cyber Domain

Name	Paper	Venue	Environment	What to Simulate	Code
WebAgent	Gur, I., Furuta, H., Huang, A. V., Safdari, M., Matsuo, Y., Eck, D., & Faust, A. A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis. In The Twelfth International Conference on Learning Representations.	ICLR	Real	Human behaviors in Web
Mind2Web	Deng, X., Gu, Y., Zheng, B., Chen, S., Stevens, S., Wang, B., ... & Su, Y. (2024). Mind2web: Towards a generalist agent for the web. Advances in Neural Information Processing Systems, 36.	Neurips	Real	Human behaviors in Web	python
WebArena	Zhou, S., Xu, F. F., Zhu, H., Zhou, X., Lo, R., Sridhar, A., ... & Neubig, G. (2023). Webarena: A realistic web environment for building autonomous agents. arXiv preprint arXiv:2307.13854.	ICLR	Real	Human behaviors in Web	python
ChoiceMates	Park, J., Min, B., Ma, X., & Kim, J. (2023). Choicemates: Supporting unfamiliar online decision-making with multi-agent conversational interactions. arXiv preprint arXiv:2310.01331.	Arxiv	Real	Human behaviors in Web
RecAgent	Wang, L., Zhang, J., Chen, X., Lin, Y., Song, R., Zhao, W. X., & Wen, J. R. (2023). Recagent: A novel simulation paradigm for recommender systems. arXiv preprint arXiv:2306.02552.	Arxiv	Virtual	Interaction with recommender system
Agent4Rec	Zhang, A., Chen, Y., Sheng, L., Wang, X., & Chua, T. S. (2024, July). On generative agents in recommendation. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1807-1817).	SIGIR	Virtual	Interaction with recommender system

Hybrid Domain

Name	Paper	Venue	Environment	What to Simulate	Code
GABM-Epidemic	Williams, R., Hosseinichimeh, N., Majumdar, A., & Ghaffarzadegan, N. (2023). Epidemic modeling with generative agents. arXiv preprint arXiv:2307.04986.	Arxiv	Virtual	Epidemic spreading	python
Generative Agents	Park, J. S., O'Brien, J., Cai, C. J., Morris, M. R., Liang, P., & Bernstein, M. S. (2023, October). Generative agents: Interactive simulacra of human behavior. In Proceedings of the 36th annual acm symposium on user interface software and technology (pp. 1-22).	UIST	Virtual	Sandbox social life	Python
WarAgent	Hua, W., Fan, L., Li, L., Mei, K., Ji, J., Ge, Y., ... & Zhang, Y. (2023). War and peace (waragent): Large language model-based multi-agent simulation of world wars. arXiv preprint arXiv:2311.17227.	Arxiv	Real	War simulation	Python
EconAgent	Li, N., Gao, C., Li, Y., & Liao, Q. (2023). Large language model-empowered agents for simulating macroeconomic activities. arXiv preprint arXiv:2310.10436.	ACL	Real	Macroeconomics	Python
UGI	Xu, F., Zhang, J., Gao, C., Feng, J., & Li, Y. (2023). Urban generative intelligence (ugi): A foundational platform for agents in embodied city environment. arXiv preprint arXiv:2312.11813.	Arxiv	Real	Human behaviors in real-world city