Its a nice exposition of using python for a simulation, but it has little bearing on economics.
The idea that fields have certain "ways of knowing" something was interesting to me - that for economics, its about the analytic solution of an equilibrium.
My own field has something similar - generally speaking, the "hierarchy of evidence" starts with single case reports at the bottom, and then way up at the top are meta-analyses of large randomized clinical trials. As a modeler, I was once asked by a clinician where models (be they analytical or simulation) fit in that framework, and the only answer I could give was "along side it". Analytic results are Capital-T True in a way that even RCTs aren't, but are only true in the universe laid out by the model itself, for example.
It was also just an interesting read as models in my field have been migrating from analytic (or numerical) solutions to equilibrium problems toward more simulation and agent based models, and that same "Yes, but how do you know it's right" type questions are coming up.
This simulation includes the concept propagation of disease, again in a sort of "Maximally-Minimalist" way. While it's suitable in the sense that it fits with the rest of simulation, I've identified it as something that I am going to have to learn about before I extend that part of the simulation beyond what's already been described.
So I was wondering if you might know of a resource which presents various minimalist models of disease propagation that you could recommend. I'm not epidemiologist, I'm actually retired and studying economics as they pertain to environmental systems, so I'm not actually looking for anything that extremely complex or compute intensive... I'm just looking for something slightly more detailed that what I've got now and perhaps describes a step-by-step method towards modeling that's actually realistic.
It's a textbook on agent-based models written by an ecologist (where an awful lot of disease modeling is done). I believe there's a cheaper paperback version on Amazon, and they use NetLogo, which is pretty gentle computationally.
It is true that economists have an unjustified preference for analytic solutions over numeric solutions.
However, the main reason to avoid simulations, is that the assumption of rational agents is not very amenable to the typical approach of simulation. Any simulation that imposed the assumption of rationality would look very different to the simulations that are usually done.
For example, rational agents make decisions based on future prices. I might decide to build a factory, because I expect positive profits given the future prices of inputs and outputs. But I can only know this if I have myself modeled the entire economy. Economists do this using backward induction. They solve for equilibrium in the future, and work backwards.
Another more basic example is that rational agents naturally form markets. Buying/selling of goods is much more accurately described by a perfect market, than by some random matching with random prices.
So this is a valid contribution to economics in the form of an inadequate dynamic model.
With that line of reasoning, don't you feel that anything is a "valid contribution to economics" ?
Hedge funds very definitely watch the weather projections.
So perhaps the discrete diffusion simulation being discussed just doesn't look like what an economist regards as a "good" model.
[NB I've spent a lot of time writing simulations in the industrial domain - where the simulated result is generally primary and the logical structure is a means to that end].
(I'm an economist).
It would be more interesting to look at where the agents end up from their starting positions. Besides looking at wealth inequality, one could look at "consistency" or whatever you call it.