Machine Learning Enabled Computable General Equilibrium (CGE) - Joplin

Description

The “Machine Learning Enabled Computable General Equilibrium (CGE) - Lumberton” analysis merges advanced machine learning with traditional CGE models to offer unprecedented insights into the economic impacts of disaster scenarios on Lumberton. 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
result_name	str	Output File Name prefix	Sets the file name prefix for output files.

Input datasets

key name	type	name	description
sector_shocks *	incore:capitalShocks	Capital shocks	Building states to capital shocks per sector.

Output datasets

key name	type	name	description
domestic-supply *	incore:Employment	Supply results	A dataset containing domestic supply results (format: CSV).
gross-income *	incore:Employment	Gross income	A dataset of resulting gross income (format: CSV).
pre-disaster-factor-demand *	incore:FactorDemand	Factor demand	A dataset of factor demand before disaster (format: CSV).
post-disaster-factor-demand *	incore:FactorDemand	Factor demand	A dataset of factor demand after disaster (format: CSV).
household-count *	incore:HouseholdCount	Household count	A dataset of household count (format: CSV).

(* required)

Execution

code snippet:

    # Create Machine Learning Enabled CGE Lumberton Model
    mlcgelumberton = MlEnabledCgeLumberton(client)

    sector_shocks = "6706b97443810e1298e8fbfc"

    mlcgelumberton.load_remote_input_dataset("sector_shocks", sector_shocks)
    # optional
    mlcgelumberton.set_parameter("result_name", "test_lumberton_mlcge_result")

    mlcgelumberton.run_analysis()

full analysis: ml_enabled_lumberton_cge.ipynb