EXP-PARTICLE-NULL-R0

✓ success

Particle Null Model — rep 0

L1 level 5 runs

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premortem

mock

real

metrics

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review

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In plain language

Researchers tested if simple text generators could mimic complex language patterns. They found that these basic models, without understanding meaning, could not produce the expected results. Therefore, the experiment was inconclusive.

What we found

UNDERPOWERED (N=1): No predictions scored

Predictions we made before running

0/3 confirmed

▶ Technical details

Research hypothesis

NULL MODEL: Markov chain text generators (no semantic processing) in pent70 topology. If F₀, VP_excess, tRAF closure match LLM runs, then organization = trivial network/statistical property, not semantic.

Experimental setup

Type: simple

Condition	Parameters
PARTICLE_LIVE	interaction: LIVE, n_agents: 5, n_rounds: 70, note: Markov chain agents in pent70 topology, persona: PERSONA

Factors: n_agents (5) × substrate (markov_bigram)

Parameters

model: markov_bigram
n_agents: 5
n_rounds: 70
n_runs_per_condition: 1
scheduler: round_robin
temperature: null

Trophic Ratios by Condition

Mean trophic ratio per agent across runs. Error bars = ±1 std dev. Higher TR = more upstream (exporter).

Series (5 experiments)