Agent-Based Information Gain Simulation
Explore how MCP facilitates information flow in ātangible systems
Information Gain Over Time
This chart shows how information flows through the system over multiple rounds of interaction.
Trust-Information Correlation
This chart demonstrates the relationship between trust levels and information gain in the system.
Teleological Alignment Dashboard
This dashboard shows how well the system aligns with human-centered goals across multiple dimensions.
Teleological Network
This network visualization shows how agents and outcomes are connected in the system.
Teleological Convergence
This chart shows how the system converges toward teleological outcomes over time.
System Metrics
Key metrics showing the health of the ātangible system after simulation.
Agent Details
Information about each agent in the system and their knowledge gain.
Meta-Agent Impact
This chart compares information gain with and without the meta-agent (MCP platform).
Network Visualization Mockups
These static mockups illustrate more complex aspects of information flow in ātangible systems.
Agent Network and Information Flow
This visualization shows how information flows between different agents in the system. Thicker lines represent higher information transfer rates, while node size indicates the amount of knowledge each agent possesses.
Cross-Domain Information Transfer
This visualization demonstrates how information from one domain (e.g., housing) influences outcomes in connected domains (e.g., education, healthcare). The MCP framework facilitates these cross-domain connections, creating meta-level information gain.
Before/After MCP Implementation
This side-by-side comparison shows information distribution before and after introducing meta-agents (MCP platforms). Note how information is more evenly distributed and uncertainty is reduced after implementation.
