Most people hate math and (I would like to claim that) they hate statistics even more, because statistics are often harder to understand than everyday geometry, compound interest or any other everyday math problem. Yet understanding statistics is vitally important for many everyday decisions. While we can't fix the larger problem in just one post, we can suggest an interesting talk by Jennifer Rogers given at the Royal Institution about statistical lies and how not to fall for them.

Long time ago theoretical background of the cobweb model has somewhat troubled me. So I wanted to explore my doubts. At the time I didn't have any idea how to do this properly, but recently I think I have figured it out.

In this post I will construct quite simple agent-based model of the price formation in the free market. This time the approach works, at least in part, and from these we can uncover hidden assumption made in the classical cobweb model.

Emergence and growth of streaming platforms reshapes the face of modern economy. This can potentially result in better tomorrow for all of us, or we could return to the middle ages. For a deeper discussion see the following video by Wisecrack.

Two years ago I have covered a classical price discovery model known as the cobweb model. While the premise of the model is rather simple, its internal logic seemed flawed to me. Yet at that time I didn't know how to properly explore its flaws.

A bit later on I started exploring order book models and one of the simplest ones, the efficient market marker model, caught my eye and reminded me that I want to explore some issues regarding the cobweb model.

This post (and accompanying app) sat in my "unpublished" folder for a long time, until I have rediscovered it lately and worked out few things which bugged me in this approach. So here, in this post, I will present you with an interactive app, which combines both of these models. I will also provide some discussion on why the combination of these models doesn't work as expected.

The topic we have started last year, opinion dynamics, has carried us through all of the 2019 and will likely remain an important topic in 2020. It is quite interesting and still developing research direction, which has potential to innovate social and political sciences as well as physics (at least in regards of understanding open systems) and computer science.

As can be seen in the figure below, we have published 39 posts (the same number as in 2018) and 22 interactive models, which is our new best result. I am not completely sure if I can keep this tempo up, especially as my postdoctoral project will soon end, but lets hope for the best.

Fig 1.The number of posts written in English and still available on this iteration of Physics of Risk. The wide bars represent total number of posts for each year since 2010, while the narrower bars represent a number of posts with 'Interactive models' tag.

So, for 2020 I plan to keep on writing about opinion dynamics, especially because I haven't yet written about new results I published during my postdoctoral research project. Some of these results will likely lead us to a related topic of human mobility. I would also like to write about some economic models, which would highlight the dark side of the free market.