EXP-PARTICLE-NULL-R1

series member

Particle Null Model — rep 1

L1 level 1 runs

new

premortem

mock

real

metrics

analyze

review

krit

done

In plain language

This experiment tested if a simple text generator, without understanding meaning, could produce results similar to a more advanced language model. The goal was to see if complex language patterns could arise from basic statistical rules. However, the experiment was not able to draw any conclusions because it did not have enough data.

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).