https://github.com/bancaditalia/abcredit.jl
A fast and simple to use Julia implementation of the macroeconomic model described in [Assenza, Delli Gatti, Grazzini (2015)]
Science Score: 36.0%
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○Scientific vocabulary similarity
Low similarity (15.5%) to scientific vocabulary
Keywords
Repository
A fast and simple to use Julia implementation of the macroeconomic model described in [Assenza, Delli Gatti, Grazzini (2015)]
Basic Info
- Host: GitHub
- Owner: bancaditalia
- License: agpl-3.0
- Language: Julia
- Default Branch: main
- Homepage: https://bancaditalia.github.io/ABCredit.jl/
- Size: 10 MB
Statistics
- Stars: 16
- Watchers: 5
- Forks: 3
- Open Issues: 2
- Releases: 4
Topics
Metadata Files
README.md
The ABC of macroeconomic agent-based modelling
A fast and easy to use Julia implementation of the model described in Emergent dynamics of a macroeconomic agent based model with capital and credit. The package can be used to simulate the original model or as a base for extensions. If you are not familiar with Julia and the way in which multiple dispatch allows for powerful extensions, don't hesitate to get in touch!
Installation
julia
using Pkg
Pkg.add("ABCredit")
Quick example
```julia using ABCredit
W = 1000 # number of workers F = 100 # number of consumption firms N = 20 # number of capital firms
model = ABCredit.initialise_model(W, F, N)
T = 1000 # number of epochs
d = ABCredit.runonesim!(model, T) ```
To plot the results of the simulation, install the Plots package via Pkg.add("Plots") and then run
```julia using Plots
plot(d.Y_real) ```
Citing ABCredit
If you found this package useful for your research, please cite (one of) the following publication(s): the research works that made it possible to develop and release ABCredit.jl.
In this work, we extend ABCredit.jl with reinforcement learning agents:
bib
@inproceedings{brusatin2024simulating,
title={Simulating the economic impact of rationality through reinforcement learning and agent-based modelling},
author={Brusatin, Simone and Padoan, Tommaso and Coletta, Andrea and Delli Gatti, Domenico and Glielmo, Aldo},
booktitle={Proceedings of the 5th ACM International Conference on AI in Finance},
pages={159--167},
year={2024}
}
In this work, we calibrate ABCredit.jl efficiently using reinforcement learning to select search methods (the calibration code is here):
bib
@inproceedings{glielmo2023reinforcement,
title={Reinforcement Learning for Combining Search Methods in the Calibration of Economic ABMs},
author={Aldo Glielmo and Marco Favorito and Debmallya Chanda and Domenico Delli Gatti},
booktitle={Proceedings of the Fourth ACM International Conference on AI in Finance},
pages={305--313},
year={2023}
}
Disclaimer
This package is an outcome of a research project. All errors are those of the authors. All views expressed are personal views, not those of Bank of Italy.
* Credits to Sara Corbo for the logo, echoing the logo of BeforeIT but with warmer colors.
Owner
- Name: Banca d'Italia
- Login: bancaditalia
- Kind: organization
- Location: Rome
- Repositories: 1
- Profile: https://github.com/bancaditalia
GitHub Events
Total
- Issues event: 1
- Watch event: 7
- Issue comment event: 1
- Push event: 9
- Fork event: 2
Last Year
- Issues event: 1
- Watch event: 7
- Issue comment event: 1
- Push event: 9
- Fork event: 2
Issues and Pull Requests
Last synced: 6 months ago
Packages
- Total packages: 1
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Total downloads:
- julia 1 total
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 4
juliahub.com: ABCredit
A fast and simple to use Julia implementation of the macroeconomic model described in [Assenza, Delli Gatti, Grazzini (2015)]
- Homepage: https://bancaditalia.github.io/ABCredit.jl/
- Documentation: https://docs.juliahub.com/General/ABCredit/stable/
- License: AGPL-3.0
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Latest release: 0.1.3
published over 1 year ago
Rankings
Dependencies
- actions/checkout v2 composite
- julia-actions/julia-buildpkg v1 composite
- julia-actions/julia-runtest v1 composite
- julia-actions/setup-julia v1 composite