This summer, 3Blue1Brown ran a series of guest videos. One of them is particularly interesting for the fans of statistical physics. The video describes physical and mathematical details of the simulation of the phase change.

The second video goes into the mathematical analysis of the vapor-liquid model (which is notably very much like the Ising model we mention occasionally). It is available on the Spectral Collective Youtube channel.

Another summer has ended, and we are back to uncover and learn new things! With the start of the new academic season, I want to dedicate more time to a few research directions I have started but have not fully committed to.

Sadly, this means less time for Physics of Risk. While I do have a list of models and other curiosities to share with you (the readers of Physics of Risk blog), the time I can reasonably allocate to the blog grows scarcer. This year will be much slower in terms of the original and interactive content.

Thank you for reading the blog, and I wish everyone a productive and inspiring new academic year!

In October 2021, South American Institute of Fundamental Research of International Centre for Theoretical Physics hosted a workshop on Sociophysics. If you're looking to get started in doing opinion dynamics, this recording of the talks by the two leading researchers in the field, Katarzyna Sznajd-Weron and Maxi San Miguel, is a great place to begin. If you are interested, complete program and recordings of other talks are available on dedicated site

With this video we end this academic year and wish you relaxing summer holidays.

In a recent post, we have discussed how to obtain the stationary variance of the AR(2) process using Yule-Walker equations. While intuitively, it is trivial to see that the poll-delayed voter model is an AR(2) process, showing this formally is a bit more involved. Let us delve into this question further.

It is hard (or impossible) to directly obtain the analytical expression for the stationary distribution of the poll-delay voter model. But we can look at various possible approximations, with the Beta distribution being the prime suspect. To fit the Beta distribution (or any other two-parameter distribution), we need to know two stationary moments of the model. Deriving the stationary mean is a trivial problem, while deriving the stationary variance is more involved.

In this post, let us use the Yule-Walker equations to obtain the expression for stationary variance of AR(2) process.