Coherence achieved at one scale can propagate and amplify coherence at other scales, creating cascading effects both upward and downward. Local stability and alignment can enable larger-scale coherence, while large-scale coherence can, in turn, stabilize and enhance coherence at smaller scales.
This cascading process is not automatic — it depends on the presence of effective reflection and feedback mechanisms (such as Mirror Closure) that allow coherence to transfer across boundaries. When these mechanisms are present, coherence becomes contagious across scales. When they are absent, fragmentation at one scale tends to spread to others.
Consequences that now become visible: • Individual psychological coherence can contribute to collective and civilizational coherence (and vice versa), but only when there are strong feedback and reflection channels between scales. • Many societal problems arise when coherence mechanisms break down between scales — for example, when individual or group optimization actively undermines larger-scale coherence. • In AI systems, coherence achieved within individual models may or may not cascade to multi-agent or human-AI systems, depending on the quality of cross-scale reflection mechanisms. • The most resilient systems (biological, social, or technological) tend to have strong coherence transfer mechanisms operating across multiple scales simultaneously. • This theorem suggests that efforts to improve coherence should strategically target “leverage points” where improvements can cascade most effectively across scales.