This repository has the code for A Model of Product Awareness and Industry Life Cycles
The derivation document has the complete set of equations implemented for the model, where all equation numbers in the code refer to this document.
Online Examples (no installation)
Using the Binder technology, the following links will execute the package notebooks in your browser
- All Notebooks
- solving-large-linear-odes.ipynb: Methods for solving large systems of equations and markov chains (e.g., 100million by 100million)
- transition-multiple-cohorts-example-staggered-with-forgetting-experiments.ipynb: Transition dynamics example demonstrating multiple cohorts
- demand-function-with-multiple-cohorts.ipynb: Solving the demand function for multiple cohorts
Installation for Local Use
Follow the instructions to install Julia and Jupyter
Open the Julia REPL (see the documentation above) and then install the package (by entering package mode) with
] add https://github.com/jlperla/Perla1.jl.git
There are several ways you can run the notebooks after installation
Using the built-in Jupyter is straightforward. In the Julia terminal
using Perla1, IJulia notebook(detached=true, dir=dirname(dirname(pathof(Perla1))))
Alternatively, to use a separate Jupyter installation you may have installed with Anaconda,
using Perla1 cd(dirname(dirname(pathof(Perla1)))) ; jupyter lab
where the last step runs your
jupyter labin the shell.
Note In either case, the first time the
usingit will be very slow
The code for individual examples and experiences are in the
NOTE: When using the notebooks for the first time, it will be very slow as the package and its dependencies are all compiled.