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How OASIS Works

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System Architecture

OASIS (Open Agent Social Interaction Simulations) is a comprehensive framework for simulating social media environments with AI agents. At its core, OASIS consists of several integrated components that work together to create realistic social media simulations:

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Core Components

  1. Platform: The central infrastructure that simulates the social media environment (Twitter-like or Reddit-like). It manages user accounts, content, social relationships, and engagement metrics.

  2. Agents: LLM-powered users that interact within the platform. Each agent has a unique profile and decision-making process driven by large language models.

  3. Actions: A diverse set of operations agents can perform, such as creating posts, commenting, liking, following, and more.

  4. Recommendation System: Algorithms that determine what content appears in each agent’s feed, similar to real social media platforms.

  5. Simulation Engine: The orchestration layer that controls the progression of time, activates agents, and manages the overall simulation flow.

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Operational Flow

Here’s how OASIS operates in a typical simulation:

  1. Initialization:

    • The platform is created with specific settings (Twitter-like or Reddit-like)
    • Agent profiles are loaded from CSV or JSON files
    • LLM models are configured for agent decision-making
    • Available actions and recommendation systems are defined

  2. Simulation Cycle:

    • For each simulation step:
      • Time advances according to the simulation clock
      • A subset of agents performs actions based on a predefined action list as an intervention
      • The recommendation system refreshes content feeds
      • Active agents observe their current state (posts with comments from the recommendation system)
      • Active agents decide what actions to take based on LLM reasoning
      • The platform processes these actions and updates the environment

  3. Agent Decision-Making:

    • Each agent receives an observation of their current state
    • The LLM model processes this observation along with the agent’s profile
    • The model decides which action the agent should take
    • The agent executes the chosen action on the platform

  4. Platform Updates:

    • The platform processes all agent actions
    • Social relationships are updated (following/followers)
    • Content engagement metrics are recalculated
    • Recommendation algorithms determine new content for user feeds

  5. Data Collection:

    • All actions and interactions are logged in the database
    • Researchers can analyze this data to study social phenomena

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Scale and Performance

OASIS is designed to scale up to one million agents, enabling large-scale studies of social interactions. To achieve this scale:

  • The system uses efficient database operations for storing and retrieving data
  • Multiple LLM instances can be deployed for load balancing
  • Concurrent request limiting prevents overloading LLM services
  • Time acceleration allows simulating longer periods in less real time

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Customization Options

OASIS provides extensive customization options:

  • Platform Types: Choose between Twitter-like or Reddit-like environments
  • Recommendation Algorithms: Configure how content is distributed to agents
  • Agent Profiles: Define diverse user demographics and personalities
  • Available Actions: Control which social actions agents can perform
  • Model Selection: Use different LLM backends for agent decision-making

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Integration with LLMs

OASIS leverages large language models through the CAMEL framework to power agent decision-making:

  • Support for OpenAI models (GPT-4, GPT-3.5)
  • Integration with local open-source models via VLLM
  • Load balancing across multiple model instances
  • Customizable prompting for agent reasoning

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Data Analysis

The simulation data is stored in a SQLite database, allowing for comprehensive analysis:

  • Track the spread of information across the network
  • Analyze group formation and polarization
  • Study the effects of recommendation algorithms on user behavior
  • Examine emergent social phenomena

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Use Cases

OASIS can be applied to a wide range of research and development scenarios:

  • Social media platform design and testing
  • Content moderation policy evaluation
  • Information spread and misinformation studies
  • Consumer behavior and marketing research
  • Community formation and group dynamics analysis

By simulating realistic social media environments at scale, OASIS provides a powerful tool for understanding complex social phenomena without the ethical concerns of experimenting on real users.