Machine Learning Enabled Computable General Equilibrium (CGE) - Salt Lake City#
Description
The “Machine Learning Enabled Computable General Equilibrium (CGE) - Salt Lake City” analysis merges advanced machine learning with traditional CGE models to offer unprecedented insights into the economic impacts of disaster scenarios on Salt Lake City. Trained on a comprehensive dataset of numerous simulated disasters and their economic effects, this hybrid approach excels in predicting the intricate dynamics of the city’s economy under various crises.
A computable general equilibrium (CGE) model is based on fundamental economic principles. A CGE model uses multiple data sources to reflect the interactions of households, firms, and relevant government entities as they contribute to economic activity. The model is based on (1) utility-maximizing households that supply labor and capital, using the proceeds to pay for goods and services (both locally produced and imported) and taxes; (2) the production sector, with perfectly competitive, profit-maximizing firms using intermediate inputs, capital, land, and labor to produce goods and services for both domestic consumption and export; (3) the government sector that collects taxes and uses tax revenues in order to finance the provision of public services; and (4) the rest of the world.
The output of this analysis are CSV files with domestic supply, gross income, before- and post-disaster factor demand and household count.
Contributors
Science: Charles Nicholson, Nushra Zannat, Hwayoung Jeon, Tao Lu, Harvey Cutler, Anita Pena
Implementation: NCSA IN-CORE Dev Team
Input parameters
key name |
type |
name |
description |
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Output File Name prefix |
Sets the file name prefix for output files. |
Input datasets
key name |
type |
name |
description |
---|---|---|---|
|
Capital shocks |
Building states to capital |
Output datasets
key name |
type |
name |
description |
---|---|---|---|
|
Supply results |
A dataset containing domestic supply results (format: CSV). |
|
|
Gross income |
A dataset of resulting gross income (format: CSV). |
|
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Factor demand |
A dataset of factor demand before disaster (format: CSV). |
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Factor demand |
A dataset of factor demand after disaster (format: CSV). |
|
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Household count |
A dataset of household count (format: CSV). |
(* required)
Execution
code snippet:
# Create ML enabled Salt Lake City CGE Model
ml_enabled_cge = MlEnabledCgeSlc(client)
# Set analysis input datasets
ml_enabled_cge.load_remote_input_dataset("sector_shocks", sector_shocks)
# Set any optional analysis parameters
ml_enabled_cge.set_parameter("result_name", "slc_7_region")
# Run Salt Lake City CGE model analysis
ml_enabled_cge.run_analysis()
full analysis: ml_enabled_slc_cge.ipynb