Compare Simulations
How much difference do Web4 mechanisms make? Compare simulation runs side-by-side to see how T3, ATP, and karma carry-forward affect outcomes across life cycles.
Cross-Life Learning — self-aware learning across lifetimes. Agents discover what works through experience: “quality contributions earn more ATP” or “transparency rebuilds trust faster.” Each new life builds on lessons from the last. (The technical term is EP — Cross-Life Learning.)
We've loaded the most revealing comparison to start — Web4 vs no Web4, same agents. Scroll down to see the results, or pick a different question below.
Mechanisms matter more over time
With Web4 trust mechanisms, agents diverge from baseline after ~20 actions. Early behavior looks identical — the feedback loops need time to compound.
Rules create order; learning creates wisdom
Society rules produce immediate cooperation. Cross-life learning produces slower but more adaptable agents. The strongest societies combine both.
Generalists start slower but reach further
Agents learning 5 domains at once build trust more slowly — but their combined expertise across domains eventually surpasses single-domain specialists.
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How to read the charts
Trust Trajectory: Shows how T3 evolves over time. The trust threshold (0.5) marks where behavior transitions from reactive to intentional. Above this threshold, agents exhibit coherent patterns.
ATP Trajectory: Tracks the ATP attention budget. ATP decreases with actions and increases with valuable contributions. The crisis threshold (20) marks when agents face resource pressure.
Volatility: Measures behavioral consistency. Low volatility indicates stable patterns; high volatility suggests crisis/recovery dynamics or experimental behavior.
Synchronized Hovering: Mouse over any chart to see values at that tick across all simulations. This reveals divergence points where different parameters led to different outcomes.