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This post (and accompanying app) sat in my "unpublished" folder for a long time, until I have rediscovered it lately and worked out few things which bugged me in this approach.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.

So for our final post on the trust and suspicion model [1], and also for our last post this year (happy new year by the way), we add a trivial extension to this model. Namely we have rebuilt the apps you have already seen, where you can mix both update rules (model types) and have them acting at different intensities. While this does not add much to the model, but it allows to explore even more complex social scenarios.

Recently one of the well-known researchers in the field has proposed an interesting opinion dynamics model, which aims to explain polarization and group formation in human societies [1]. His model is based on two antipodal concepts in human interactions: trust and suspicion.

In this post we present you with an app, which uses \( 100 \) agents. Obviously, it would not be reasonable to enter \( \mathbf{D} \) by hand for such number of agents. So we have used random strategy to fill in \( \mathbf{D} \).

On December 5th and 6th Vilnius university has closed its doors. No lectures were held, no science was done on these days in Vilnius university.

While our posts are prepared beforehand, we symbolically skip this week's post to share the text which had completely replaced Vilnius university website on those days. This text, which also explains the reasons behind the protest follows:

#TheLastPriority

As the Government is failing to keep promises of additional funding for the increase of lecturers, researchers and non-academic staff salaries from 1 January , Vilnius University suspends its activities on December 5-6 and announces these requirements:

1. Allocate necessary funds in 2020 state budget so that researchers’ and lecturers’ salaries would be increased by at least 20 %.
2. The wage of academics and highly qualified administrative staff should be at least 50 % higher than Lithuania’s average wage by the year 2023
   • Research and teaching assistants must earn 50 to 100 % more than Lithuania‘s average wage
   • Researchers, assistant professors must earn 200 to 250 % more than Lithuania’s average wage
   • Senior researchers, associate professors must earn 300 to 400 % more than Lithuania’s average wage
   • Research professors, professors must earn 350 to 400 % more than Lithuania’s average wage
   • Bonuses shouldn’t amount more than 10 % of an annual wage
   • Wages must be indexed considering economic growth
3. Allocate necessary funds in 2020 state budget so that the salaries of administrative staff would meet labour market’s median
4. Allocate necessary funds in 2020 state budget in order to increase students’ incentive scholarships
5. Have a sustainable strategy of research projects funding which would meet the 3 % of Lithuania’s GDP as recommended by European Commission and OECD
6. Include researchers and other public sector‘s representatives in forming the state‘s annual budget process
7. Recognize the value of research and studies in forming the nation‘s welfare
8. Form a clear, transparent and balanced public sector’s wage policy based on competences and responsibilities