We invite you to listen to Hannah Fry interview given to Numberphile channel, in which she talks a bit about using math to predict crime.

Homo economicus (lat. economic man) is a very convenient concept, which is used to simplify many economical problems. Yet this concept may not always work. One of the simplest examples were the idea of homo economicus fails is so-called Minority game.

Imagine that each workday evening you may either go home or visit a bar. At home you will always be able to spend some quality time, but at the bar you might be able to have more fun. Yet the bar has only a limited space for visitors. For the sake of simplicity let us assume that it may conveniently fit in just about half of your coworkers. Each of your coworkers faces the same dilemma as you - go home or visit the bar? Thus you have to make the same choice as minority of you coworkers.

When do people start to understand concept of "fairness"? When do they start to act accordingly? Game theory and related sociological research show that kids show signs of understanding "fairness" at the same time as they learn to count. Take that humanitarian sciences! ;)

For more information see the following DNews video.

Previously we already wrote about a work of our colleague, Julius Ruseckas, in which he proposed an elementary model, which reproduces q-Gaussian distribution. Recently we introduced temporal dynamics into that static model [1]. In this text we briefly discuss the dynamical version of the model.

Let us remind you that the correlated spin model describes possible configurations of the spin chain. In this spin chain neighboring spins are usually coaligned, meaning that nearby spins point in the same direction, though there are fixed number of cases \( d-1 \) (in this text \( d \) has slightly different meaning) where spins are antialigned. Consequently we have \( d \) distinct domains inside of which spins are aligned. We are interested in the total spin, \( M = \sum_i s_i \), of such system.

Sebastian Wernicke talks about how data science helps Amazon and Netflix to make decisions about creating their TV shows. The process itself is not without its intricacies, which are covered in this TED talk. We invite you to watch it!