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
Cải thiện Elasticsearch trong bài toán semantic search sử dụng phương pháp Sentence Embeddings

Cải thiện Elasticsearch trong bài toán semantic search sử dụng phương pháp Sentence Embeddings Trong bài viết này mình sẽ sử dụng pretrain model SimCS

Vo Van Phuc 18 Nov 25, 2022
Linear programming solver for paper-reviewer matching and mind-matching

Paper-Reviewer Matcher A python package for paper-reviewer matching algorithm based on topic modeling and linear programming. The algorithm is impleme

Titipat Achakulvisut 66 Jul 05, 2022
LCG T-TEST USING EUCLIDEAN METHOD

This project has been created for statistical usage, purposing for determining ATL takers and nontakers using LCG ttest and Euclidean Method, especially for internal business case in Telkomsel.

2 Jan 21, 2022
Official PyTorch implementation of Time-aware Large Kernel (TaLK) Convolutions (ICML 2020)

Time-aware Large Kernel (TaLK) Convolutions (Lioutas et al., 2020) This repository contains the source code, pre-trained models, as well as instructio

Vasileios Lioutas 28 Dec 07, 2022
A Python package implementing a new model for text classification with visualization tools for Explainable AI :octocat:

A Python package implementing a new model for text classification with visualization tools for Explainable AI 🍣 Online live demos: http://tworld.io/s

Sergio Burdisso 285 Jan 02, 2023
Uncomplete archive of files from the European Nopsled Team

European Nopsled CTF Archive This is an archive of collected material from various Capture the Flag competitions that the European Nopsled team played

European Nopsled 4 Nov 24, 2021
KoBERT - Korean BERT pre-trained cased (KoBERT)

KoBERT KoBERT Korean BERT pre-trained cased (KoBERT) Why'?' Training Environment Requirements How to install How to use Using with PyTorch Using with

SK T-Brain 1k Jan 02, 2023
Multilingual finetuning of Machine Translation model on low-resource languages. Project for Deep Natural Language Processing course.

Low-resource-Machine-Translation This repository contains the code for the project relative to the course Deep Natural Language Processing. The goal o

Andrea Cavallo 3 Jun 22, 2022
An official implementation for "CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval"

The implementation of paper CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval. CLIP4Clip is a video-text retrieval model based

ArrowLuo 456 Jan 06, 2023
Beyond Paragraphs: NLP for Long Sequences

Beyond Paragraphs: NLP for Long Sequences

AI2 338 Dec 02, 2022
Contains descriptions and code of the mini-projects developed in various programming languages

TexttoSpeechAndLanguageTranslator-project introduction A pleasant application where the client will be given buttons like play,reset and exit. The cli

Adarsh Reddy 1 Dec 22, 2021
Lattice methods in TensorFlow

TensorFlow Lattice TensorFlow Lattice is a library that implements constrained and interpretable lattice based models. It is an implementation of Mono

504 Dec 20, 2022
A Facebook Messenger Chatbot using NLP

A Facebook Messenger Chatbot using NLP This project is about creating a messenger chatbot using basic NLP techniques and models like Logistic Regressi

6 Nov 20, 2022
A Multi-modal Model Chinese Spell Checker Released on ACL2021.

ReaLiSe ReaLiSe is a multi-modal Chinese spell checking model. This the office code for the paper Read, Listen, and See: Leveraging Multimodal Informa

DaDa 106 Dec 29, 2022
PORORO: Platform Of neuRal mOdels for natuRal language prOcessing

PORORO: Platform Of neuRal mOdels for natuRal language prOcessing pororo performs Natural Language Processing and Speech-related tasks. It is easy to

Kakao Brain 1.2k Dec 21, 2022
WikiPron - a command-line tool and Python API for mining multilingual pronunciation data from Wiktionary

WikiPron WikiPron is a command-line tool and Python API for mining multilingual pronunciation data from Wiktionary, as well as a database of pronuncia

213 Jan 01, 2023
Neural text generators like the GPT models promise a general-purpose means of manipulating texts.

Boolean Prompting for Neural Text Generators Neural text generators like the GPT models promise a general-purpose means of manipulating texts. These m

Jeffrey M. Binder 20 Jan 09, 2023
This is an incredibly powerful calculator that is capable of many useful day-to-day functions.

Description 💻 This is an incredibly powerful calculator that is capable of many useful day-to-day functions. Such functions include solving basic ari

Jordan Leich 37 Nov 19, 2022
BeautyNet is an AI powered model which can tell you whether you're beautiful or not.

BeautyNet BeautyNet is an AI powered model which can tell you whether you're beautiful or not. Download Dataset from here:https://www.kaggle.com/gpios

Ansh Gupta 0 May 06, 2022
voice2json is a collection of command-line tools for offline speech/intent recognition on Linux

Command-line tools for speech and intent recognition on Linux

Michael Hansen 988 Jan 04, 2023