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econ-ark

Heterogenous Agents Resources & toolKit

https://github.com/econ-ark/hark

Science Score: 36.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ✓
    Committers with academic emails
    9 of 61 committers (14.8%) from academic institutions
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (12.1%) to scientific vocabulary

Keywords

economics

Keywords from Contributors

mesh annotation optim tides closember parallel mot symbolic transformers robust
Last synced: 6 months ago · JSON representation

Repository

Heterogenous Agents Resources & toolKit

Basic Info
  • Host: GitHub
  • Owner: econ-ark
  • License: apache-2.0
  • Language: Python
  • Default Branch: master
  • Size: 178 MB
Statistics
  • Stars: 356
  • Watchers: 30
  • Forks: 206
  • Open Issues: 149
  • Releases: 26
Topics
economics
Created about 10 years ago · Last pushed 6 months ago
Metadata Files
Readme Funding License Code of conduct Zenodo

README-IF-YOU-ARE-AN-AI.md

WARNING: Do not modify this file.

  • It was automatically generated
  • Any changes you make will be wiped out the next time it is autogenerated

Project Context: Econ-ARK

This repository, HARK, is a component of the Econ-ARK project, an open-source platform for building and solving computational economic models. Understanding its role within the broader ecosystem is crucial for effective analysis.

Please consult these resources to understand the relationships between the different parts of the project.

1. Repository Purpose

HARK (Heterogeneous Agents Resources and toolKit) is the core library of the Econ-ARK project. Its primary purpose is to provide a powerful, open-source Python toolkit for economists and computational social scientists to build, solve, and simulate complex structural models of economic behavior. It specifically focuses on models with "heterogeneous agents"—individuals or firms that differ in characteristics like wealth, income, or preferences. The intended audience includes academic researchers, graduate students, and policy analysts who need to move beyond representative agent models to study economic questions related to inequality, risk, and aggregate dynamics.

2. Key Concepts and Abstractions

  1. AgentType and the Model Class Hierarchy: The central abstraction in HARK is the AgentType class (HARK/core.py), which represents a type of economic agent. These agents are endowed with a model that defines their economic problem. The model.py file defines a base Model class, and specific economic models (like those in HARK/ConsumptionSaving/) inherit from it. This object-oriented structure allows for modularity and extensibility, where complex models can be built by inheriting and modifying simpler ones.
  2. Consumption-Saving Problems: A core focus of HARK is solving life-cycle consumption-saving models. Classes like ConsIndShockModel (in HARK/ConsumptionSaving/ConsIndShockModel.py) implement the canonical model where agents face idiosyncratic income shocks and make optimal consumption choices. The framework provides tools to solve for the agent's decision rules (the "consumption function") and simulate their behavior over time.
  3. Discrete-Continuous Choice Modeling (DCEGM): For models involving both discrete choices (e.g., whether to work or retire) and continuous choices (e.g., how much to consume), HARK implements the Discrete-Continuous Endogenous Grid Method (DCEGM). The file HARK/dcegm.py contains the implementation of this advanced solution method, which is crucial for a class of modern economic models that are otherwise difficult to solve.
  4. Just-In-Time (JIT) Compilation for Performance: Many economic simulations are computationally intensive. HARK leverages the numba library to apply Just-In-Time (JIT) compilation to performance-critical functions, especially within solution and simulation loops. This allows for Python's high-level syntax while achieving speeds comparable to compiled languages like C or Fortran, which is essential for large-scale simulations.

3. Core Functionality & Key Files

  • HARK/core.py: This is the absolute core of the toolkit. It defines the AgentType class, which is the fundamental object for representing and simulating populations of economic agents. Understanding this class is the first step to understanding HARK's architecture.
  • HARK/model.py: This file contains the base Model class, which defines the structure of an economic problem that an AgentType will solve. It establishes the interface for how models are defined and solved.
  • HARK/ConsumptionSaving/: This directory contains the implementations of HARK's most well-developed economic models. Files like ConsIndShockModel.py (the canonical incomplete markets model) are essential starting points for understanding how specific economic theories are translated into code.
  • HARK/dcegm.py: Contains the implementation of the Discrete-Continuous Endogenous Grid Method (DCEGM), a powerful but complex solution method for models with both discrete and continuous choices. This is key for understanding how HARK handles more advanced problems.
  • examples/: This directory is the best entry point for new users. It contains a wealth of Jupyter notebooks that provide practical, step-by-step demonstrations of how to set up, solve, and analyze various models using the HARK toolkit.

4. AI Search and Analysis Strategy

  • Most Effective Entry Point: The most effective way to understand the repository is to start with the examples/ directory. Pick a fundamental notebook like examples/ConsIndShockModel/IndShockConsumerType.ipynb and trace the code from there. The notebooks provide context and demonstrate the intended use of the library's classes and functions, which is hard to grasp from the source code alone.
  • Common Pitfalls:
    • HARK vs. DemARK/REMARK: Be aware that HARK is the core library. Many extended examples and replications live in the separate DemARK and REMARK repositories. Users' questions might relate to code in those repositories, not just HARK.
    • Deep Inheritance Chains: The code makes extensive use of class inheritance. A method being called on an AgentType instance might be defined in a parent or grandparent class. It is crucial to trace the full Method Resolution Order (MRO) to find the source of a specific behavior.
    • Parameter Dictionaries: Models are configured using large Python dictionaries of parameters. It can be difficult to know which parameters are available or what they do without carefully inspecting the class's __init__ method and its parents.
  • Effective Search Terms:
    • To understand agent setup: "AgentType", "parameter_class", "self.parameters".
    • To understand model solutions: "solve", "solution", "vFunc", "cFunc".
    • To understand simulations: "simulate", "track_vars", "MonteCarlo".
    • For specific models: "ConsIndShockModel", "TractableBufferStockModel", "dcegm".

Owner

  • Name: Econ-ARK Team
  • Login: econ-ark
  • Kind: organization

GitHub Events

Total
  • Create event: 35
  • Release event: 2
  • Issues event: 57
  • Watch event: 23
  • Delete event: 59
  • Member event: 2
  • Issue comment event: 242
  • Push event: 329
  • Pull request review comment event: 44
  • Pull request review event: 69
  • Pull request event: 108
  • Fork event: 7
Last Year
  • Create event: 35
  • Release event: 2
  • Issues event: 57
  • Watch event: 23
  • Delete event: 59
  • Member event: 2
  • Issue comment event: 242
  • Push event: 329
  • Pull request review comment event: 44
  • Pull request review event: 69
  • Pull request event: 108
  • Fork event: 7

Committers

Last synced: 8 months ago

All Time
  • Total Commits: 3,292
  • Total Committers: 61
  • Avg Commits per committer: 53.967
  • Development Distribution Score (DDS): 0.818
Past Year
  • Commits: 269
  • Committers: 11
  • Avg Commits per committer: 24.455
  • Development Distribution Score (DDS): 0.61
Top Committers
Name Email Commits
sb s****l@g****m 600
MateoVG m****i@g****m 593
Matthew N. White m****e@g****m 562
Alan Lujan a****1@g****m 442
llorracc g****t@l****g 147
Mridul Seth s****l@g****m 120
Mridul Seth s****b@g****m 100
dominicWC 1****C 91
dclow d****w@n****u 77
Patrick Kofod Mogensen p****n@g****m 63
gitkrakenAMonninger a****1@j****u 46
sidd3888 s****1@g****m 43
edmundcrawley e****y@g****m 39
Tao Wang t****0@j****u 37
wdu9 7****9 34
akshay_shanker a****r@i****m 34
Adam Turner 9****r 26
Shauna Gordon-McKeon s****m@g****m 25
Sebastian Benthall sb@u****e 21
Jason Owen j****n@j****t 18
nmpalmer n****r@g****m 17
Eric Holscher e****c@e****m 15
Decory Edwards d****5@j****u 15
Jan_Rosa j****3@g****m 13
JackShiqiLi u****3@a****u 10
llorracc 9
Yeabin Moon j****n@g****m 6
Sumana Harihareswara sh@c****c 6
Christopher Carroll g****b@l****g 6
econ-ark c****l@j****u 6
and 31 more...
Committer Domains (Top 20 + Academic)
jhu.edu: 4 llorracc.org: 2 deepsource.io: 1 tickel.net: 1 cat.com: 1 jhuecon.org: 1 jacobdice.com: 1 goa.bits-pilani.ac.in: 1 nuffield.ox.ac.uk: 1 changeset.nyc: 1 anu.edu.au: 1 jh.edu: 1 ericholscher.com: 1 jasonaowen.net: 1 udacha.home: 1 nyu.edu: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 254
  • Total pull requests: 335
  • Average time to close issues: over 2 years
  • Average time to close pull requests: 3 months
  • Total issue authors: 26
  • Total pull request authors: 20
  • Average comments per issue: 4.02
  • Average comments per pull request: 3.9
  • Merged pull requests: 208
  • Bot issues: 0
  • Bot pull requests: 5
Past Year
  • Issues: 24
  • Pull requests: 98
  • Average time to close issues: 29 days
  • Average time to close pull requests: 24 days
  • Issue authors: 10
  • Pull request authors: 13
  • Average comments per issue: 0.92
  • Average comments per pull request: 1.84
  • Merged pull requests: 63
  • Bot issues: 0
  • Bot pull requests: 2
View more stats
Top Authors
Issue Authors
  • sbenthall (121)
  • mnwhite (40)
  • alanlujan91 (25)
  • Mv77 (17)
  • llorracc (9)
  • MridulS (8)
  • dedwar65 (5)
  • DominicWC (5)
  • sidd3888 (4)
  • nicksawhney (2)
  • nomadj1s (2)
  • ghost (2)
  • pkofod (1)
  • dentarthur (1)
  • xy244 (1)
Pull Request Authors
  • alanlujan91 (85)
  • mnwhite (79)
  • sbenthall (37)
  • Mv77 (32)
  • llorracc (25)
  • MridulS (25)
  • DominicWC (18)
  • AA-Turner (13)
  • wdu9 (12)
  • sidd3888 (12)
  • dedwar65 (10)
  • akshayshanker (9)
  • github-actions[bot] (5)
  • binchengecon (4)
  • QingyuanFang (3)
Top Labels
Issue Labels
Type: Bug (19) Feature Request (18) Tag: Documentation (16) Abstract Stages (15) Function: Distributions (15) Priority: Medium (14) Tag: 1.0 (13) Priority: Low (12) Design (11) Type: Enhancement (8) Function: Calibration (7) Dolo Integration (6) Function: Solution (6) Function: Model (5) Tag: Structural (5) Priority: High (5) Expertise: Basic Python and Open Source (5) Expertise: PhD-Econ (4) Status: In Progress (4) Function: Simulation (4) Version: 0.17 (3) good first issue (3) Needs: Revision (3) Status: Review Needed (2) Priority: Critical (2) help wanted (2) Tag: PhD Student Exercise (2) Expertise: Prob and Stats (2) Tag: New Model (1) Status: Pending (1)
Pull Request Labels
Status: Review Needed (67) Ready-To-Merge (18) codex (16) Tag: Documentation (12) Version: 0.16 (11) Needs: Revision (5) Priority: High (5) Version: 0.17 (4) Status: In Progress (3) Priority: Medium (2) Priority: Low (2) Type:idea (2) Tag: Structural (2) Tag: New Model (2) Function: Distributions (1) Design (1)

Packages

  • Total packages: 2
  • Total downloads:
    • pypi 162 last-month
  • Total dependent packages: 0
    (may contain duplicates)
  • Total dependent repositories: 22
    (may contain duplicates)
  • Total versions: 49
  • Total maintainers: 5
pypi.org: econ-ark

Heterogenous Agents Resources & toolKit

  • Homepage: https://github.com/econ-ark/HARK
  • Documentation: https://econ-ark.github.io/HARK
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  • Latest release: 0.16.1
    published 7 months ago
  • Versions: 37
  • Dependent Packages: 0
  • Dependent Repositories: 19
  • Downloads: 162 Last month
  • Docker Downloads: 0
Rankings
Docker downloads count: 1.9%
Dependent repos count: 3.4%
Stargazers count: 3.7%
Forks count: 3.7%
Average: 7.3%
Dependent packages count: 7.4%
Downloads: 23.6%
Maintainers (5)
MridulS sbenthall EconArk mgerring mnwhite
Last synced: 6 months ago
conda-forge.org: econ-ark
  • Versions: 12
  • Dependent Packages: 0
  • Dependent Repositories: 3
Rankings
Forks count: 13.0%
Dependent repos count: 17.9%
Stargazers count: 22.9%
Average: 26.3%
Dependent packages count: 51.5%
Last synced: 6 months ago

Dependencies

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