opmodel

Instructions to execute the model

• Clone the github repo
• Create a copy of the Task_based_model_inputs and set the parameters as you want
• Run python -m opmodel.core.opmodel -w YOUR_SHEET_URL to run the model with best_guess parameters
• Run python -m opmodel.analysis.exploration_analysis -w YOUR_SHEET_URL to run a comparison of the model with aggressive, best_guess and conservative parameter choices
  • You can run python -m opmodel.analysis.exploration_analysis -w YOUR_SHEET_URL -t PARAMETER_NAME instead to produce a detailed comparison of what happens when you set said parameter at aggressive, best_guess and conservative, with all other parameters held at best_guess value
• Run python -m opmodel.analysis.sensitivity_analysis -w YOUR_SHEET_URL to see a high level comparison of what happens when you change each parameter between their aggressive, best_guess and conservative values, having all other parameters fixed at their best_guess value
  • The parameters will appear ordered by most sensitive to least sensitive. Concretely, the difference between the takeoff_length of the conservative and aggressive value, for a complex definition of takeoff_length
• Run python -m opmodel.analysis.mc_analysis -w YOUR_SHEET_URL to run a MC sampling
  • The aggressive, best guess and conservative values correspond to percentiles 5%, 50% and 95% of a distribution
  • You can adjust the correlation between parameters using the rank_correlation_between_buckets tab in the sheet
• Run python -m opmodel.analysis.timelines_report -w YOUR_SHEET_URL to run nine scenarios corresponding to the conservative, best_guess and aggresive choices, conditioned on short, best_guess and long AI timelines
  • The full_automation_requirement and flop_gap parameters are governed by the sheet Guess FLOP gap and timelines
• Run python -m opmodel.analysis.megareport -w YOUR_SHEET_URL to run the three previous analysis at once