Exactly seven years ago we have launched second iteration of the Physics of Risk website under the flag of the EU structural fund project Science of business and society (lt. Mokslas verslui ir visuomenei; see the article). The second iteration was different from the first one as it was powered by WordPress (instead of Drupal) and was multilingual (instead of being available only in Lithuanian).

Today I (Aleksejus) launch a third iteration of the Physics of Risk. There will be some changes as we will replace WordPress with a static site generator Pelican.

At the heart of economics lies two fundamental concepts, to which most of the other concepts are related. These two concepts are supply and demand. From every day life we are rather familiar with these two concepts - we demand goods and services, while industry supplies them. While industry demands labor and we supply it. But this familiarity to most of us is rather superficial, thus in this text we will try to provide a simple academical introduction into these concepts and their role in economics.

It is intended that this text will serve as an introduction into a series of posts on how and why free market does (not) work.

In 2014 Australian programmer started "social experiment" - he allowed thousands of viewers on Twitch to play computer game. Thousands of people could write into common chat to decide what the player controlled character in Pokemon Red will do - will he move up or down? Will he fish in the fond or ride a bicycle? What pokemon would he catch? Having in mind online troll culture, one could not have expected much, but in 16 days the game was completed.

Recently one programmer working at Amazon entrusted Twitch viewers with his lifetime savings of 50 thousand US dollars. This project already runs for about two weeks and so far the internet did not waste his savings...

This curiosity could be viewed on StockStream Twitch channel. But you can not only watch, but also influence the trades that will be executed. This can be done during session hours of New York Stock Exchange (workdays from 15:30 to 22:00 in Lithuanian time).

Last time when we wrote about Lotka-Volterra equations, we have mentioned Verhulst's model of population dynamics, given by

\begin{equation} \mathrm{d} x = r x ( 1 – x ) \mathrm{d} t, \end{equation}

which describes temporal evolution of ecological systems with limited supply of food. The model has parameter \( r \) which controls the availability of food to the population. It would be natural to ask how the availability of food impacts the dynamics of model? Does the population converge? If so, then to which state? May be the population does not converge? How do the answers to these questions change based on model parameter? More on this and the interesting mathematics in this Numberphile video.

Imagine during your routine checkup a doctor randomly suggests you to take a test, which would tell (with 99% accuracy) if you have a rather rare disease (only 0.1% people have it). Is it worth to be curious? What would you do if you test positive? Panic? Shock? The test is 99% accurate!

Now imagine that you have 1000 people. Only one of them (0.1%) will have this disease and there is a rather significant probability that he will test positive. Likely 10 people, out of 999 healthy people, will also test positive (1%). So in the end you have 11 positive results and only 1 sick person in the group. So actually there is only 9% chance that you actually have this disease.

This "paradox" is not a mathematical paradox, nor its a mathematical trick. It is actually Bayesian trap! More on it in the following video by Veritasium.