leverage-sims

Code, data and results for the paper Losing Leverage: A Game-Theoretic Simulation of Power After Full Automation (Andrei Taranu, writing as Eight Rice, 2026).

The paper models what happens to a population's standing, and to its survival, once AI and robots make it economically and militarily unnecessary to the people who rule it. It combines three things:

• historical base rates;
• a game-theoretic Monte Carlo simulation of six world blocs from 2026 to 2075;
• a pre-specified cross-check against a calibrated judgment model.

All results are conditional on the assumptions stated in the paper.

A companion video essay tells the same scenario as a story, for a general audience. It narrates the scenario rather than reporting the results: Automation and the RIGGED endgame.

Layout

• paper/: the paper (paper.md), the full parameter list, the brake-search protocol and its results (with the Jev elicitation scripts in paper/jev_brakes/), and the citation checks.
• models/: simulation code.
  • m8_v4.py: the six-bloc model (GPU via PyTorch, CPU fallback).
  • integrate_v4.py: the integrated scenario tree, the main results and the structural variants.
  • m9_lever.py: the decentralized-network test grid.
  • run_brakes.py and run_greenfield.py: the brake variants and the matched comparison of the structural changes.
  • check_brakes_identity.py and check_greenfield_identity.py: check that the model is bit-identical when the new parts are switched off.
  • run_v6_final.sh: runs the full final configuration.
  • m1 to m7: the upstream stage models whose outputs feed the scenario tree.
  • jev_grid/: the judgment-model elicitation.
• results/: output files.
  • Final model (brakes, dedicated core loop, compute feedback, wider physical floors): integrated_v6_final.json, m8_v6_final.json, lever_grid_v6_final.json, greenfield_v6_matched.json.
  • Earlier configurations kept for comparison: brakes only (integrated_v5_brakes.json, brakes_v5_variants.json, lever_grid_brakes.json, m8_v5_brakes.json) and no brakes (integrated_v4.json, m8_v4.json, lever_grid.json).
• research/: evidence notes behind the parameter priors.
• figures/: figures used in the paper.

Reproducing

Requirements: Python 3.11 or later, numpy, scipy, matplotlib, and PyTorch (CUDA optional).

cd models
# final model (M8_FINAL=1): all runs in sequence
bash run_v6_final.sh
# brakes only
M8_BRAKES=final INT_OUT=integrated_v5_brakes.json python integrate_v4.py
M8_BRAKES=final LV_TAG=_brakes python m9_lever.py
# everything off (reproduces the earliest comparison results exactly)
python m8_v4.py
python integrate_v4.py
python m9_lever.py

The seed is fixed at 20260930. On a consumer GPU:

• the integrated run takes about 25 minutes;
• the network grid takes about 2 hours.

GPU and CPU runs agree within simulation error but not draw for draw, because their random streams differ.

License

MIT. See LICENSE.

Citation

Taranu, A. (Eight Rice) (2026). Losing Leverage: A Game-Theoretic Simulation of Power After Full Automation. Zenodo. https://doi.org/10.5281/zenodo.23111345