PyWorld3 is a Python implementation of the World3 model

Last update: Dec 14, 2022

Related tags

Overview

The World3 model revisited in Python

Install & Hello World3
How to tune your own simulation
Licence
How to cite PyWorld3 with Bibtex
References & acknowledgment

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 "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

    @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

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

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

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

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) :

opened by alan-man 4

PyWorld3 is a Python implementation of the World3 model

Related tags

Overview

Install and Hello World3

How to tune your own simulation

Licence

How to cite PyWorld3 with Bibtex

References and acknowledgment

Comments

No output files using "example_world3_standard.py"

Missing requirement for cbr in Population

29th order

Improved usability with Bokeh

Additional time series data

Adding a plot of the historic population

Releases(v1.1)

v1.1(Aug 12, 2021)

v1.0(Feb 16, 2021)

Owner

Charles Vanwynsberghe

In this project, we aim to achieve the task of predicting emojis from tweets. We aim to investigate the relationship between words and emojis.

Transformers Wav2Vec2 + Parlance's CTCDecodeTransformers Wav2Vec2 + Parlance's CTCDecode

[AAAI 21] Curriculum Labeling: Revisiting Pseudo-Labeling for Semi-Supervised Learning

A Python 3.6+ package to run .many files, where many programs written in many languages may exist in one file.

Trankit is a Light-Weight Transformer-based Python Toolkit for Multilingual Natural Language Processing

Codes for coreference-aware machine reading comprehension

A programming language with logic of Python, and syntax of all languages.

Shirt Bot is a discord bot which uses GPT-3 to generate text

Tensorflow Implementation of A Generative Flow for Text-to-Speech via Monotonic Alignment Search

Auto translate textbox from Japanese to English or Indonesia

Count the frequency of letters or words in a text file and show a graph.

构建一个多源（公众号、RSS）、干净、个性化的阅读环境

SpeechBrain is an open-source and all-in-one speech toolkit based on PyTorch.

Transformers-regression - Regression Bugs Are In Your Model! Measuring, Reducing and Analyzing Regressions In NLP Model Updates

State of the art faster Natural Language Processing in Tensorflow 2.0 .

Code and datasets for our paper "PTR: Prompt Tuning with Rules for Text Classification"

Dense Passage Retriever - is a set of tools and models for open domain Q&A task.

GCRC: A Gaokao Chinese Reading Comprehension dataset for interpretable Evaluation

Code for EMNLP'21 paper "Types of Out-of-Distribution Texts and How to Detect Them"

Multi-Scale Temporal Frequency Convolutional Network With Axial Attention for Speech Enhancement