GEB-model/GEB
Mar 2025 – Mar 2025
1 Month active
Languages Used
PythonSQL
Technical Skills
Agent-Based ModelingBackend DevelopmentData AnalysisData EngineeringData LoadingData Modeling
Tierolf
PROFILE
Tierolf
During March 2025, Ltf200 enhanced the GEB-model/GEB repository by developing two core features focused on household data modeling and wealth-income realism. Leveraging Python and SQL, Ltf200 restructured household agent attributes to include age and education, introduced a population property for aggregated analysis, and consolidated data loading for improved simulation fidelity. The work replaced deterministic values with a lognormal distribution for wealth and income, using a combined WEALTH_INDEX to better reflect regional variation. This approach deepened the analytic clarity and credibility of household-level simulations, demonstrating strong skills in agent-based modeling, data engineering, and backend development within a complex modeling environment.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
4Total
Bugs
0
Commits
4
Features
2
Lines of code
224
Activity Months1
Your Network
14 people
Work History
March 2025
4 Commits • 2 Features
Mar 1, 2025March 2025 performance snapshot for GEB-model/GEB focused on data-model maturation and wealth/income realism to enhance simulation fidelity and analytic clarity. Delivered two major features with a clear path to cross-regional applicability and richer household analytics. Commits attached to the work include 1c66aca8e39b709f265971e512c77f619a6248eb, 3d085a0a43ac11559fff1f6bf5721b99ea2768b2, 2b246d98b52e55705122dc57334241e3f885e363, and d75596c2ee4f427e8da9795dcb7e7cd5dc232954.
4 Commits • 2 Features
Mar 1, 2025March 2025 performance snapshot for GEB-model/GEB focused on data-model maturation and wealth/income realism to enhance simulation fidelity and analytic clarity. Delivered two major features with a clear path to cross-regional applicability and richer household analytics. Commits attached to the work include 1c66aca8e39b709f265971e512c77f619a6248eb, 3d085a0a43ac11559fff1f6bf5721b99ea2768b2, 2b246d98b52e55705122dc57334241e3f885e363, and d75596c2ee4f427e8da9795dcb7e7cd5dc232954.
March 2025
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance75.0%
AI Usage25.0%
Skills & Technologies
Programming Languages
PythonSQL
Technical Skills
Agent-Based ModelingBackend DevelopmentData AnalysisData EngineeringData LoadingData ModelingData ProcessingNumerical SimulationProperty Implementation
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