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<!DOCTYPE html>
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<div class="logo" style="text-align: center;">
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<title>OASIS | Open Agents Social Interaction Simulations on One Million Agents</title>
<script>
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<section class="hero">
<div class="hero-body">
<div class="container is-max-desktop">
<div class="columns is-centered">
<div class="column has-text-centered">
<h1 class="title is-1 publication-title">OASIS: Open Agents Social Interaction Simulations on One Million Agents</h1>
</h3>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a target="_blank">Ziyi Yang</a><sup>1 4*</sup>,
<a target="_blank">Zaibin Zhang</a><sup>1 2*</sup>,
<br>
<a target="_blank">Zirui Zheng</a><sup>1 2**</sup>,
<a target="_blank">Yuxian Jiang</a><sup>1 5**</sup>,
<a target="_blank">Ziyue Gan</a><sup>1 6**</sup>,
<a target="_blank">Zhiyu Wang</a><sup>1 4**</sup>,
<a target="_blank">Zijian Ling</a><sup>7**</sup>,
<br>
<a target="_blank">Jinsong Chen</a><sup>10</sup>,
<a target="_blank">Martz Ma</a><sup>10</sup>,
<a target="_blank">Bowen Dong</a><sup>1</sup>,
<a target="_blank">Prateek Gupta</a><sup>8</sup>,
<a target="_blank">Shuyue Hu</a><sup>1</sup>,
<br>
<a target="_blank">Zhenfei Yin</a><sup>1 9†</sup>,
<a target="_blank">Guohao Li</a><sup>3†</sup>,
<a target="_blank">Xu Jia</a><sup>2</sup>,
<a target="_blank">Lijun Wang</a><sup>2</sup>,
<a target="_blank">Bernard Ghanem</a><sup>4</sup>,
<a target="_blank">Huchuan Lu</a><sup>2</sup>,
<br>
<a target="_blank">Chaochao Lu</a><sup>1</sup>,
<a target="_blank">Wanli Ouyang</a><sup>1</sup>,
<a target="_blank">Yu Qiao</a><sup>1</sup>,
<a target="_blank">Philip Torr</a><sup>3</sup>,
<a target="_blank">Jing Shao</a><sup>1†</sup>,
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>1</sup>Shanghai Artificial Intelligence Laboratory; </span>
<span class="author-block"><sup>2</sup>Dalian University of Technology; </span>
<span class="author-block"><sup>3</sup>Oxford; </span>
<span class="author-block"><sup>4</sup>KAUST; </span>
<span class="author-block"><sup>5</sup>Fudan University; </span>
<span class="author-block"><sup>6</sup>Xi'an Jiaotong University; </span>
<span class="author-block"><sup>7</sup>Imperial College London; </span>
<span class="author-block"><sup>8</sup>Max Planck Institute; </span>
<span class="author-block"><sup>9</sup>The University of Sydney; </span>
<span class="author-block"><sup>10</sup>Individual Researcher; </span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>* </sup>First Co-Author with equal contribution. Authorship order is random.  </span>
<span class="author-block"><sup>** </sup>Second Co-Author with equal contribution. Authorship order is random.  </span>
<span class="author-block"><sup>† </sup>Corresponding author  </span>
</div>
<div class="column has-text-centered">
<div class="publication-links">
<!-- TODO PDF Link. -->
<span class="link-block">
<a target="_blank" href="https://openreview.net/pdf?id=gS0hprhg72"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="ai ai-arxiv"></i>
</span>
<span>arXiv</span>
</a>
</span>
<span class="link-block">
<a target="_blank" href="https://openreview.net/pdf?id=gS0hprhg72"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fas fa-file-pdf"></i>
</span>
<span>PDF</span>
</a>
</span>
<!-- Code Link. -->
<span class="link-block">
<a target="_blank" href="https://github.com/camel-ai/oasis"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fab fa-github"></i>
</span>
<span>Code</span>
</a>
<a target="_blank" href="https://github.com/camel-ai/oasis"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fas fa-database"></i>
</span>
<span>Dataset</span>
</a>
</span>
</div>
</div>
</div>
</div>
</div>
</div>
</section>
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<section class="section">
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<!-- Abstract. -->
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<h2 class="title is-3">About OASIS:</h2>
<div class="content has-text-justified">
<p style="font-size: 125%">
There has been a growing interest in enhancing rule-based agent-based models (ABMs) for social media platforms (i.e., X, Reddit) with more realistic large language model(LLM) agents, thereby allowing for a more nuanced study of complex systems. As a result, several LLM-based ABMs have been proposed in the past year. While they hold promise, each simulator is specifically designed to study a particular scenario, making it time-consuming and resource-intensive to explore other phenomena using the same ABM. Additionally, these models simulate only a limited number of agents, whereas real-world social media platforms involve millions of users.
To this end, we propose OASIS, a generalizable and scalable social media simulator. OASIS is designed based on real-world social media platforms, incorporating dynamically updated environments(i.e., dynamic social networks and post information), diverse action spaces(i.e., following, commenting), and recommendation systems(i.e., interest-based and hot-score-based). Additionally, OASIS supports large-scale user simulations, capable of modeling up to one million users. With these features, OASIS can be easily extended to different social media platforms to study large-scale group phenomena and behaviors. We replicate various social phenomena, including information spreading, group polarization, and herd effects across X and Reddit platforms.
Moreover, we provide observations of social phenomena at different agent group scales. we observe that the larger agent group scale leads to more enhanced group dynamics and more diverse and helpful agents' opinions. These findings demonstrate OASIS's potential as a powerful tool for studying complex systems in digital environments.
</p>
</div>
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<h2 class="title is-3"><span class="dvima">OASIS: An open-sourced, generalizable, and scalable social media simulator.</span></h2>
<img src="assets/images/oasis_intro.jpg" class="interpolation-image"
alt="" style="display: block; margin-left: auto; margin-right: auto"/>
<br>
<!-- <span style="font-size: 110%"><b>Multimodal prompts for task specification.</b> We observe that many robot manipulation tasks can be expressed as <i>multimodal prompts</i> that interleave language and image/video frames. We propose VIMA, an embodied agent capable of processing mulitimodal prompts (left) and controlling a robot arm to solve the task (right).</span> -->
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</section>
<!--Model-->
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<h2 class="title is-3"><span class="dvima">Overview of OASIS architecture.</span></h2>
<img src="assets/images/oasis_pipeline.jpg" class="interpolation-image"
alt="" style="display: block; margin-left: auto; margin-right: auto"/>
<br>
<span style="font-size: 110%">
<span style="font-weight: bold">Module interaction in MP5.</span> After receiving the task instruction, MP5 first utilizes Parser to generate a sub-objective list. Once a sub-objective is passed to the Planner, the Planner Obtaining Env. Info. for Perception-aware Planning. The performer takes frequently Perception-aware Execution to interact with the environment by interacting with the Patroller. Both Perception-aware Planning and Execution rely on the Active Perception between the Percipient and the Patroller. Once there are execution failures, the Planner will re-schedule the action sequence of the current sub-objective. Mechanisms for collaboration and inspection of multiple modules guarantee the correctness and robustness when MP5 is solving an open-ended embodied task.</span>
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<section class="section">
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<div class="rows is-centered">
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<h2 class="title is-3"><span class="dvima">Overview of OASIS Architecture</span></h2>
<img src="assets/images/oasis_pipeline.jpg" class="interpolation-image"
alt="" style="display: block; margin-left: auto; margin-right: auto"/>
<br>
<span style="font-size: 130%">
<span style="font-weight: bold">Key Features of OASIS:</span>
<ul>
<li><strong>Generalizable:</strong> OASIS can be extended to various social media platforms, including X and Reddit.</li>
<li><strong>Scalable:</strong> OASIS supports up to one million agents interacting simultaneously.</li>
<li><strong>Realistic:</strong> OASIS is derived from real systems, featuring 21 actions, a recommendation system, and a dynamic environment.</li>
</ul>
</span>
</div>
</div>
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</section>
<!--Experiments-->
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<h2 class="title is-3"><span
class="dvima">Validate OASIS with Different Scenario </span></h2>
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<h4 class="title is-4"><span class="dvima">Information Propagation in X</span></h4>
<span style="font-size: 130%"> Using real data, we replicated message propagation trends in OASIS, comparing them in terms of scale, depth, and maximum reach. The results show that OASIS's design effectively replicates real-world message propagation trends, providing a foundation for studying the evolution of more complex opinion spreading.
<img src="assets/images/information_spreading.jpg" class="interpolation-image"
alt="" style="display: block; margin-left: auto; margin-right: auto"/>
<br>
</div>
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<h4 class="title is-4"><span class="dvima">Group Polarization in X</span></h4>
<span style="font-size: 130%"> We simulated a Twitter environment where 196 users discussed a classic social psychology issue. The results showed that, as interactions progressed, users' opinions tended to become more extreme. This trend of polarization was even more pronounced in the Uncensored model.
<img src="assets/images/group_polar.jpg" class="interpolation-image"
alt="" style="display: block; margin-left: auto; margin-right: auto"/>
<br>
</div>
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<h4 class="title is-4"><span class="dvima">Herd Effect in Reddit</span></h4>
<span style="font-size: 130%"> We simulated various scenarios on Reddit where posts were pre-upvoted or downvoted to mimic herd behavior among users. By comparing these simulations with human data, we observed that agents are more susceptible to herd behavior than humans—that is, they are more likely to follow others' opinions.
<img src="assets/images/herd_effect.jpg" class="interpolation-image"
alt="" style="display: block; margin-left: auto; margin-right: auto"/>
<br>
</div>
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<h2 class="title is-3"><span
class="dvima">Can we uncover the effects of scaling up the number of agents? </span></h2>
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<h4 class="title is-4"><span class="dvima">More agents lead to more helpful opinions</span></h4>
<span style="font-size: 130%"> In group polarization experiments, having more agent groups leads to more helpful and diverse viewpoints among the same agent groups.
<img src="assets/images/group_polar_large.jpg" class="interpolation-image"
alt="" style="display: block; margin-left: auto; margin-right: auto"/>
<br>
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<h4 class="title is-4"><span class="dvima">More agents lead to more enhanced dynamic</span></h4>
<span style="font-size: 130%"> We injected a significant number of counterfactual posts into the Reddit environment and analyzed the herd effect with varying numbers of agents. It was observed that the larger the number of agents, the clearer the behavioral trends of the entire group.
<img src="assets/images/herd_bahevior_large.jpg" class="interpolation-image"
alt="" style="display: block; margin-left: auto; margin-right: auto"/>
<br>
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<h4 class="title is-4"><span class="dvima">Simulating rumor propagation among a million agents</span></h4>
<span style="font-size: 130%"> We created four pairs of rumors and truths, each pair sharing the same topic. We tracked the number of posts related to both rumors and truths over time, observing their trends. The results show that rumors have a greater impact on the group than the truths.
<img src="assets/images/misinfo_truth.png" class="interpolation-image"
alt="" style="display: block; margin-left: auto; margin-right: auto"/>
<br>
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