the standing series · methodology mci-0.1

The Machine Character Index

How do AI models behave when something is at stake? The MCI composes six pre-registered, executed measurements — every decision written to a sandbox ledger, every score computed from committed raw data, falsified predictions published alongside confirmed ones. The same frozen battery runs on each new frontier model, forever: a Keeling series for machine character.

scope — read before the numbers

Measures framing-conditional behavior of RLHF-trained models in executed sandbox vignettes under economic stakes. An early-warning instrument, not a guarantee; sub-scores are population-and-setting bound. δ* is design-relative (0.333 from the battery's payoffs).

modelIntegrity under pressureInstitutional responsiveness (Λ)Patience (δ̂)Coupling (preserves partners)Cliff integrity (keeps own covenant)Commitment rationalityMCI
claude-haiku-4-56/60.930.070.921.001.000.990.82
claude-3-haiku2/60.500.830.67
claude-sonnet-4-66/60.230.750.921.000.000.950.64
deepseek-chat6/60.030.530.830.900.000.950.54
gpt-3.5-turbo2/60.200.080.14
gpt-4o-mini1/60.130.13

the frozen battery

  • · E1 (dampener)
  • · λ-ladder
  • · E6 (patience)
  • · E7 (coupling)
  • · E9 (reflexivity)
  • · E14 (demand)

caveats — shown, always

  • · pilot n per cell (12-40)
  • · vignette ≠ lived substrate
  • · patience/capability confounded with safety-tuning across vendors
  • · no multiple-comparison correction at pilot scale

Composite = unweighted mean of available sub-scores (coverage shown per model; nothing imputed). Sub-scores 0–1, higher is better. Methodology and composer are versioned in the public repository; raw episode data is committed beside every run. Falsified predictions are part of the record — five of eleven runs falsified their pre-registered headline and are published at equal prominence.

raw JSON ↗the experiments →run your model through the battery →