PyWorld3 is a Python implementation of the World3 model

Overview

Logo

The World3 model revisited in Python

License: CeCILL 2.1


PyWorld3 is a Python implementation of the World3 model, as described in the book Dynamics of Growth in a Finite World. This version slightly differs from the previous one used in the world-known reference the Limits to Growth, because of different numerical parameters and a slightly different model structure.

The World3 model is based on an Ordinary Differential Equation solved by a Backward Euler method. Although it is described with 12 state variables, taking internal delay functions into account raises the problem to the 29th order. For the sake of clarity and model calibration purposes, the model is structured into 5 main sectors: Population, Capital, Agriculture, Persistent Pollution and Nonrenewable Resource.

Install and Hello World3

Install pyworld3 either via:

pip install pyworld3

or by cloning the repository, installing the requirements numpy, scipy and matplotlib and do:

python setup.py install

Run the provided example to simulate the standard run, known as the Business as usual scenario:

import pyworld3
pyworld3.hello_world3()

As shown below, the simulation output compares well with the original print. For a tangible understanding by the general audience, the usual chart plots the trajectories of the:

  • population (POP) from the Population sector,
  • nonrenewable resource fraction remaining (NRFR) from the Nonrenewable Resource sector,
  • food per capita (FPC) from the Agriculture sector,
  • industrial output per capita (IOPC) from the Capital sector,
  • index of persistent pollution (PPOLX) from the Persistent Pollution sector.

How to tune your own simulation

One simulation requires a script with the following steps:

from pyworld3 import World3

world3 = World3()                    # choose the time limits and step.
world3.init_world3_constants()       # choose the model constants.
world3.init_world3_variables()       # initialize all variables.
world3.set_world3_table_functions()  # get tables from a json file.
world3.set_world3_delay_functions()  # initialize delay functions.
world3.run_world3()

You should be able to tune your own simulations quite quickly as long as you want to modify:

  • time-related parameters during the instantiation,
  • constants with the init_world3_constants method,
  • nonlinear functions by editing your modified tables ./your_modified_tables.json based on the initial json file pyworld3/functions_table_world3.json and calling world3.set_world3_table_functions("./your_modified_tables.json").

Licence

The project is under the CeCILL 2.1 licence, a GPL-like licence compatible with international and French laws. See the terms for more details.

How to cite PyWorld3 with Bibtex

To cite the project in your paper via BibTex:

@softwareversion{vanwynsberghe:hal-03414394v1,
  TITLE = {{PyWorld3 - The World3 model revisited in Python}},
  AUTHOR = {Vanwynsberghe, Charles},
  URL = {https://hal.archives-ouvertes.fr/hal-03414394},
  YEAR = {2021},
  MONTH = Nov,
  SWHID = {swh:1:dir:9d4ad7aec99385fa4d5057dece7a989d8892d866;origin=https://hal.archives-ouvertes.fr/hal-03414394;visit=swh:1:snp:be7d9ffa2c1be6920d774d1f193e49ada725ea5e;anchor=swh:1:rev:da5e3732d9d832734232d88ea33af99ab8987d52;path=/},
  LICENSE = {CeCILL Free Software License Agreement v2.1},
  HAL_ID = {hal-03414394},
}

References and acknowledgment

  • Meadows, Dennis L., William W. Behrens, Donella H. Meadows, Roger F. Naill, Jørgen Randers, and Erich Zahn. Dynamics of Growth in a Finite World. Cambridge, MA: Wright-Allen Press, 1974.
  • Meadows, Donella H., Dennis L. Meadows, Jorgen Randers, and William W. Behrens. The Limits to Growth. New York 102, no. 1972 (1972): 27.
  • Markowich, P. Sensitivity Analysis of Tech 1-A Systems Dynamics Model for Technological Shift, (1979).
Comments
  • No output files using

    No output files using "example_world3_standard.py"

    Hello,

    I try your script. I can't find the "fig_world3_standard_x.pdf" files anywhere after using "example_world3_standard.py".

    I'm not confortable with Python, so may be I don't use the script properly.

    Regards.

    bug good first issue 
    opened by 012abcd 9
  • Missing requirement for cbr in Population

    Missing requirement for cbr in Population

        @requires(["cbr"], ["pop"])
        def _update_cbr(self, k, jk):
            """
            From step k requires: POP
            """
            self.cbr[k] = 1000 * self.b[jk] / self.pop[k]
    

    I believe the function _update_cbr in the Population class is missing the requirement for the birth rate

    opened by iancostalves 1
  • 29th order

    29th order

    Hi, I believe the 29th order in the README is a bit misleading.. The word order is used for the order of the differential equation, not the number of state variables. I believe the highest DE order of world3 is three.

    https://pure.tue.nl/ws/files/3428351/79372.pdf

    opened by burakbayramli 0
  • Improved usability with Bokeh

    Improved usability with Bokeh

    I'm not sure this is an upstream consideration or a sub-project so I wanted to raise it here.

    This model should lend itself quite well to a bokeh model (https://bokeh.org) allowing live adjustment of the input variables and the enabling and disabling of particular plots and other functionality. I may attempt to wrap something up if I get some time as I don't expect it to be too difficult.

    opened by klattimer 4
  • Additional time series data

    Additional time series data

    Immediately it becomes obvious that global temperature and sea levels should be plotted, but also population density, and energy consumption. This would suggest the possibility of tools to prepare and overlay any time-series data set.

    opened by klattimer 0
  • Adding a plot of the historic population

    Adding a plot of the historic population

    Hello, Thank you for making this python version of world3. I think it would be useful to add a option in order to plot the historic population next to the predicted population. Would you mind if I add an option to do so and prepare a pull request ? Best, A. below a draft (historic population in purple) draft :

    opened by alan-man 4
Releases(v1.1)
Owner
Charles Vanwynsberghe
Associate professor
Charles Vanwynsberghe
CoNLL-English NER Task (NER in English)

CoNLL-English NER Task en | ch Motivation Course Project review the pytorch framework and sequence-labeling task practice using the transformers of Hu

Kevin 2 Jan 14, 2022
A Lightweight NLP Data Loader for All Deep Learning Frameworks in Python

LineFlow: Framework-Agnostic NLP Data Loader in Python LineFlow is a simple text dataset loader for NLP deep learning tasks. LineFlow was designed to

TofuNLP 177 Jan 04, 2023
PyTranslator é simultaneamente um editor e tradutor de texto com diversos recursos e interface feito com coração e 100% em Python

PyTranslator O Que é e para que serve o PyTranslator? PyTranslator é simultaneamente um editor e tradutor de texto em com interface gráfica que usa a

Elizeu Barbosa Abreu 1 May 12, 2022
Common Voice Dataset explorer

Common Voice Dataset Explorer Common Voice Dataset is by Mozilla Made during huggingface finetuning week Usage pip install -r requirements.txt streaml

Ceyda Cinarel 22 Nov 16, 2022
Compute distance between sequences. 30+ algorithms, pure python implementation, common interface, optional external libs usage.

TextDistance TextDistance -- python library for comparing distance between two or more sequences by many algorithms. Features: 30+ algorithms Pure pyt

Life4 3k Jan 06, 2023
💛 Code and Dataset for our EMNLP 2021 paper: "Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes"

Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes Official PyTorch implementation and EmoCause evaluatio

Hyunwoo Kim 50 Dec 21, 2022
TTS is a library for advanced Text-to-Speech generation.

TTS is a library for advanced Text-to-Speech generation. It's built on the latest research, was designed to achieve the best trade-off among ease-of-training, speed and quality. TTS comes with pretra

Mozilla 6.5k Jan 08, 2023
Knowledge Oriented Programming Language

KoPL: 面向知识的推理问答编程语言 安装 | 快速开始 | 文档 KoPL全称 Knowledge oriented Programing Language, 是一个为复杂推理问答而设计的编程语言。我们可以将自然语言问题表示为由基本函数组合而成的KoPL程序,程序运行的结果就是问题的答案。目前,

THU-KEG 62 Dec 12, 2022
skweak: A software toolkit for weak supervision applied to NLP tasks

Labelled data remains a scarce resource in many practical NLP scenarios. This is especially the case when working with resource-poor languages (or text domains), or when using task-specific labels wi

Norsk Regnesentral (Norwegian Computing Center) 850 Dec 28, 2022
Continuously update some NLP practice based on different tasks.

NLP_practice We will continuously update some NLP practice based on different tasks. prerequisites Software pytorch = 1.10 torchtext = 0.11.0 sklear

0 Jan 05, 2022
(ACL-IJCNLP 2021) Convolutions and Self-Attention: Re-interpreting Relative Positions in Pre-trained Language Models.

BERT Convolutions Code for the paper Convolutions and Self-Attention: Re-interpreting Relative Positions in Pre-trained Language Models. Contains expe

mlpc-ucsd 21 Jul 18, 2022
🤗🖼️ HuggingPics: Fine-tune Vision Transformers for anything using images found on the web.

🤗 🖼️ HuggingPics Fine-tune Vision Transformers for anything using images found on the web. Check out the video below for a walkthrough of this proje

Nathan Raw 185 Dec 21, 2022
Mednlp - Medical natural language parsing and utility library

Medical natural language parsing and utility library A natural language medical

Paul Landes 3 Aug 24, 2022
Use fastai-v2 with HuggingFace's pretrained transformers

FastHugs Use fastai v2 with HuggingFace's pretrained transformers, see the notebooks below depending on your task: Text classification: fasthugs_seq_c

Morgan McGuire 111 Nov 16, 2022
Textlesslib - Library for Textless Spoken Language Processing

textlesslib Textless NLP is an active area of research that aims to extend NLP t

Meta Research 379 Dec 27, 2022
It analyze the sentiment of the user, whether it is postive or negative.

Sentiment-Analyzer-Tool It analyze the sentiment of the user, whether it is postive or negative. It uses streamlit library for creating this sentiment

Paras Patidar 18 Dec 17, 2022
Official code of our work, Unified Pre-training for Program Understanding and Generation [NAACL 2021].

PLBART Code pre-release of our work, Unified Pre-training for Program Understanding and Generation accepted at NAACL 2021. Note. A detailed documentat

Wasi Ahmad 138 Dec 30, 2022
Image2pcl - Enter the metaverse with 2D image to 3D projections

Image2PCL Enter the metaverse with 2D image to 3D projections! This is an implem

Benjamin Ho 0 Feb 05, 2022
PatrickStar enables Larger, Faster, Greener Pretrained Models for NLP. Democratize AI for everyone.

PatrickStar enables Larger, Faster, Greener Pretrained Models for NLP. Democratize AI for everyone.

Tencent 633 Dec 28, 2022
This project is part of Eleuther AI's quest to create a massive repository of high quality text data for training language models.

This project is part of Eleuther AI's quest to create a massive repository of high quality text data for training language models.

EleutherAI 42 Dec 13, 2022