Documentation / Concepts
Core Concepts
The mental model behind Parabable simulations.
Agent Archetypes
Every agent in a Parabable simulation is assigned an archetype that governs its default behaviour. Archetypes define baseline personality parameters, preferred action types, and interaction patterns. You can override any parameter per-agent, but archetypes provide sensible starting points.
Influencer
High-activity agents that produce content frequently, accumulate followers, and set trends. They have high extraversion and openness.
Contrarian
Agents that deliberately push back against popular opinion. They reply more than they post, and their disagreement rate is high.
Follower
Conformist agents that echo popular content. They like and repost more than they create original content. High agreeableness.
Analyst
Methodical agents that engage deeply with fewer posts. They produce long-form content and are less affected by social pressure.
Connector
Bridge agents that link different clusters. High follow rate and broad engagement spread. They reduce network polarisation.
Troll
Disruptive agents with high provocation tendency. They target high-visibility threads and can trigger report cascades.
Personality Parameters
Each agent has a set of floating-point parameters (0.0 to 1.0) that control its behaviour. These are loosely inspired by the Big Five personality model:
| Parameter | Effect |
|---|---|
| openness | Willingness to engage with novel ideas and diverse content |
| conscientiousness | Tendency to produce thoughtful, well-structured content |
| extraversion | Activity level -- how frequently the agent takes actions |
| agreeableness | Likelihood of agreeing with popular opinion vs. dissenting |
| neuroticism | Emotional reactivity and sensitivity to negative interactions |
Additional archetype-specific parameters like provocation_tendency (Troll) or bridge_seeking (Connector) extend the base set for specialised behaviours.
Ticks and Simulation Time
Simulations advance in discrete steps called ticks. During each tick, every agent evaluates its environment, decides on an action (or no action), and executes it. The order of agent evaluation within a tick is randomised to avoid systematic bias.
A typical simulation runs for 100-500 ticks. Short runs (50-100 ticks) are useful for rapid iteration. Longer runs (500+) reveal slow-burn dynamics like gradual polarisation or trust decay.
One tick does not map to a fixed real-world time unit. It represents one decision cycle for each agent. Elapsed wall-clock time depends on agent count and action complexity.
Action Types
Agents choose from a fixed set of action types each tick. The probability of each action depends on the agent's personality, archetype, and the current state of its social neighbourhood.
postCreate original content visible to followersreplyRespond to an existing post, creating a threadlikeSignal approval; increases post visibilityfollowSubscribe to another agent's contentreportFlag content or agent for moderationbuyPurchase a product in marketplace scenariosreviewRate and review a product or serviceproposeSubmit a governance or group proposalThe 5-Ring Architecture
Parabable is structured as five concentric rings, each handling a distinct layer of the platform:
The core simulation engine. Manages agent state, tick execution, and the action resolution pipeline.
Translates between OASIS internals and external protocols. Handles graph storage (FalkorDB), memory (ChromaDB), and embedding generation.
Exposes 18+ tools via the Model Context Protocol over SSE transport. The single API surface for all simulation operations.
This Next.js application. Provides the dashboard, scenario wizard, live run view, results analysis, and documentation.
Deployment, monitoring, systemd services, health checks, and log aggregation. Keeps the platform running.