Simulation Controls
High coherence enables effective collective action. Low coherence leads to disorganized, individual behavior.
Data Readout
Active Bots
0
Foraging Success
0%
Efficiency Over Time
An interactive model of biologically-inspired, multi-scale collective intelligence operating as adaptive foragers within the Xenial Quantum Economy framework.
High coherence enables effective collective action. Low coherence leads to disorganized, individual behavior.
Active Bots
0
Foraging Success
0%
Efficiency Over Time
The simulation demonstrates key principles of the Xenial Quantum Economy. Click a concept to understand how it maps to what you're observing.
Each bot in the simulation represents a LevinBot—a self-organizing collective of biological cells. Its "foraging" is the act of navigating a problem space (the canvas) to achieve a goal (reaching the target). Its intelligence is not programmed top-down; it emerges from the simple rules of interaction between the bots, guided by the overall system coherence.
The "System Coherence" slider is a direct proxy for the Time Coefficient. A high TC creates a high-coherence environment where bots can effectively communicate and coordinate, leading to successful collective action. A low TC introduces "decoherence," causing the bots' collective intelligence to break down, making them unable to achieve their goal.
Each successful foraging run—a bot reaching a target—is a verifiable "Proof-of-Agency." It's a demonstration of transforming potential into a meaningful, goal-directed outcome. In the full XQE framework, this successful action could "mine" or generate a Live Information Token (LIT), representing the value created by the coherent action of the forager.