Camel-AI Open Source OASIS: A Next Generation Simulator for Realistic Social Network Dynamics with a Million Agents

Social media platforms have revolutionized human interaction, creating dynamic environments where millions of users exchange information, form communities, and influence each other. These platforms, including x and Reddit, are not just communication tools, but have become critical ecosystems for understanding modern social behaviors. Simulating such intricate interactions is vital for studying misinformation, group polarization, and herding behavior. Computational models provide researchers with a cost-effective and scalable way to analyze these interactions without conducting resource-intensive real-world experiments. But, Creating models that replicate the scale and complexity of social networks remains a significant challenge.

The main issue in modeling social networks is to capture the diverse behaviors and interactions of millions of users in a dynamic network. Traditional agent-based models (ABM) fail to represent complex behaviors such as context-based decision making or the influence of dynamic recommendation algorithms. Furthermore, existing models are often limited to small-scale simulations, typically involving only hundreds or thousands of agents, which restricts their ability to imitate large-scale social systems. These limitations prevent researchers from fully exploring phenomena such as how misinformation spreads or how group dynamics evolve in online environments. These limitations highlight the need for more advanced and scalable simulation tools.

Existing methods for simulating social media interactions often lack essential features such as dynamic user networks, fine-grained recommender systems, and real-time updates. For example, most ABMs rely on pre-programmed agent behaviors, which do not reflect the nuanced decision making seen in real-world users. Furthermore, current simulators are often platform-specific and designed to study isolated phenomena, making them impractical for broader applications. They often cannot scale beyond a few thousand agents, leaving researchers unable to examine the behaviors of millions of users interacting simultaneously. The absence of scalable and versatile models has been a major obstacle in the advancement of social network research.

Researchers from Camel-ai, Shanghai artificial intelligence Laboratory, Dalian University of technology, Oxford, KAUST, Fudan University, Xi'an Jiaotong University, Imperial College London, Max Planck Institute and the University from Sydney developed <a target="_blank" href="https://github.com/camel-ai/oasis”>OASISa next-generation social media simulator designed with scalability and adaptability to address these challenges. OASIS is based on modular components, including an environment server, a recommender system (RecSys), a timing engine, and an agent module. It supports up to a million agents, making it one of the most complete simulators. This system incorporates dynamically updated networks, diverse action spaces, and advanced algorithms to replicate the dynamics of real-world social networks. By integrating data-driven methods and open source frameworks, OASIS provides a flexible platform for studying phenomena on platforms like x and Reddit, allowing researchers to explore topics ranging from information spread to herding behavior.

The OASIS architecture emphasizes both scale and functionality. The functions of some of the components are the following:

• Your environment server is the backbone and stores detailed user profiles, historical interactions, and social connections.
• The recommender system personalizes content visibility using advanced algorithms such as TwHIN-BERT, which processes user interests and recent activities to rank posts.
• Time Engine governs user activation based on hourly probabilities, simulating realistic patterns of online behavior.

These components work together to create a simulation environment that can adapt to different platforms and scenarios. Switching from x to Reddit requires minimal module adjustments, making OASIS a versatile tool for social media research. Its distributed computing infrastructure ensures efficient handling of large-scale simulations, even with up to a million agents.

In experiments modeling information propagation in x, OASIS achieved a normalized RMSE of approximately 30%, demonstrating its ability to align with real diffusion trends. The simulator also replicated group polarization, showing that agents tend to adopt more extreme opinions during interactions. This effect was particularly pronounced in the uncensored models, where agents used more extreme language. Additionally, OASIS revealed unique insights, such as that the herd effect is more evident in agents than in humans. Agents consistently followed negative tendencies when exposed to poorly treated feedback, while humans showed a stronger critical focus. These findings underscore the potential of the simulator to uncover both expected and novel patterns of social behavior.

With OASIS, larger agent groups lead to richer and more diverse interactions. For example, when the number of agents increased from 196 to 10,196, the diversity and usefulness of user responses improved significantly, with a 76.5% increase in perceived usefulness. At an even larger scale of 100,196 agents, user interactions became more varied and meaningful, which illustrates the importance of scalability in the study of group behavior. Additionally, OASIS demonstrated that misinformation spreads more effectively than truthful information, especially when the rumors are emotionally provocative. The simulator also showed how isolated user groups form over time, providing valuable insights into the dynamics of online communities.

Key findings from the OASIS research include:

1. OASIS can simulate up to one million agents, far exceeding the capabilities of existing models.
2. It supports multiple platforms, including x and Reddit, with modular components that can be easily adjusted.
3. The simulator replicates phenomena such as group polarization and herd behavior, providing a deeper understanding of these dynamics.
4. OASIS achieved a normalized RMSE of 30% in information propagation experiments, closely aligning with real-world trends.
5. He showed that rumors spread faster and more widely than truthful information in large-scale simulations.
6. Larger agent groups improve the diversity and usefulness of responses, emphasizing the importance of scale in social network studies.
7. OASIS distributed computing enables efficient management of simulations, even with millions of agents.

In conclusion, OASIS is a breakthrough in simulating the dynamics of social networks, offering scalability and adaptability. OASIS addresses existing model limitations and provides a robust framework for studying complex-scale interactions. LLM's integration with rules-based agents precisely mimics the behaviors of up to a million users on platforms like x and Reddit. Its ability to replicate complex phenomena, such as information spread, group polarization, and herd effects, provides researchers with valuable insights into modern social ecosystems.

Verify he Paper and <a target="_blank" href="https://github.com/camel-ai/oasis” target=”_blank” rel=”noreferrer noopener”>GitHub page

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Sana Hassan, a consulting intern at Marktechpost and a dual degree student at IIT Madras, is passionate about applying technology and artificial intelligence to address real-world challenges. With a strong interest in solving practical problems, he brings a new perspective to the intersection of ai and real-life solutions.