Here we will briefly present another classic agent-based model - Axelrod's model [1]. In his article Axelrod argues that culture is formed through social interactions, that it is "something people learn from each other". Hence he presents a model which describes competition between cultural traits via social interactions between the agents.

More than four years ago I (Aleksejus) started a series of posts reviewing with most well known kinetic exchange models. Recently I had to refresh my memory on this topic as a colleague (Julius) suggested an idea how to obtain power-law from the constant exchange model.

Last week Nobel prize laureate Robert Shiller delivered an open lecture at Life Sciences Center, Vilnius University. His talk involves some topics extremely relevant in the scope of Physics of Risk. Including the "epidemic" approach to economics and the importance of narration to humans as species.

It is well known that "there are three kinds of lies: lies, damned lies, and statistics." Yet if and only if the speaker and the audience are not careful enough to make a blunder. Previously we have already linked a video about the most well known and counter-intuitive Bayes theorem and now we share another video discussing another well known statistics paradox.

As described in One Minute Physics video below, Simpson's paradox occurs when you have data set, which contains multiple groups. When analyzing groups separately one trend could be observed (the more you earn the less happy you become), while after combining the groups the trend disappears or is reversed.

Currently I am waiting for the Research Council of Lithuanian post-doctoral project final results. Though interim results are already known, but RCL still needs to decide where they will cut. This situation starts up my existensial thinking on what is good science and what is not. I believe that at least partialy it is well covered by the following Numberphile video.

In this video Cliff Stoll presents one of the homeworks he gave his students to test their ability to notice subtleties and if they are self-conscious (able to check their own errors) enough. This qualities are somewhat important to produce a good piece of scientific work.