Research Log 03 / Effective Information and the Persistence of Meaning
- junichihakamaki
- May 3
- 1 min read
“Resonance Efficiency η(t)”
This log introduces a distinction that becomes necessary once communication is scaled.
Shannon’s information theory defines how symbols are transmitted. It measures uncertainty and optimizes signal transfer under noise. In that framework, meaning is deliberately excluded. A message can be perfectly transmitted without being meaningfully received.
This separation becomes visible when communication increases, but coherence does not.
To address this, we introduce a second layer—not to replace information theory, but to sit alongside it.
Let I(t) represent incoming information. Not all of it contributes to meaning.
We define:
Ieff(t) = η(t) × I(t)where η(t)∈[0,1] represents the efficiency with which information becomes meaningful.
High input does not guarantee high effect. Misalignment, noise, timing, and context reduce conversion.
Meaning is therefore not a passive outcome of communication. It must be maintained.
Let M(t) denote the state of shared meaning over time.
Its change can be described as:
dM/dt=η(t) I(t)−λ(t) M(t)
Here λ(t) represents the rate at which entropy erodes coherence.
This expresses a simple condition:
Meaning persists only when effective input exceeds entropic loss.
The implication is structural.
More communication does not necessarily produce more understanding. In many cases, increased volume raises entropy faster than it increases coherence.
What matters is not how much is said, but how much becomes aligned.
This log marks a transition from transmission to persistence.
The problem is no longer how information moves, but whether meaning survives that movement.
If you are interested in a more detailed explanation, please go to my Substack.
“Meaning Field M(t)”
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