When we started to work on "Physics of Risk" Java platform was extremely popular way to show interactive material to readers online. Though this technology is still rather popular, but there is a significant challenge for amateurs working with it. Since the previous glorious days Java platform has become less secure and thus more paranoid. As the time moves on newer Java Runtime Environment versions start require applets to be certified. Certificates are not that cheap and thus not accessible to amateur programers.

The good news is that HTML5 standard has becoming more popular on web and that it also offers presentation of interactive content via Javascript. Thus we plan to migrate most of the old Java applets to this emerging technology. These new HTML5 applets should work in all modern web browsers as the implementation of HTML5 by the most popular modern browsers was started a few years ago. Therefore we do not expect any problems occurring to you, our readers.

In the mean time, we present a list of articles, which include old Java applets, and also provide a detailed instruction on how to launch Java applets.

Previously we have already mentioned that there are three main statistical features of networks. The most problematic of them appears to be clustering as random networks do not naturally form local, tightly interconnected, communities. In the previous text we discussed a simple model, which produces highly clustered scale-free networks. The problem is that it is rather hard to relate that model to any real complex system. In this text we will attempt to solve this problem.

Average shortest path (sometimes network diameter), degree distribution and clustering are the three main network characteristics. Path lengths tend to be small in random network models (average shortest path and network diameter grows as \( \ln N \) or slower). Power-law degree distribution can be obtained from the Barabasi-Albert and some other models. But clustering appears to be trickier to reproduce together with the previous two. In this text we will discuss what clustering actually is and how to obtain it in random network model.

Previously we have already discussed how scale-free network could form in social systems. We have used the fact that people tend to meet new people through their friends, thus edge redirection models are perfect example of a simple social network formation model. But scale-free networks are observed in very diverse systems, thus it is highly probable that many different mechanisms for scale-free network generation may exist [1, 2]. In this text we will discuss how intrinsic random nature and tendency to optimize causes scale-free topologies to emerge in computer networks - how and why "luck and reason" may be behind the observed complexity [2].

Probably everyone has at least once have been stuck in the traffic jam. But most probably not everyone had thought about the possible relationship between the complexity science and traffic. For quite a long time it was thought that traffic jams can be caused by noticeable events on the road - car crashes, road works and etc. But in the recent decades number of cars in the streets grew rapidly and it was noticed that sometimes traffic jams form without any obvious reasons. In this text we present a simple traffic model by Nagel and Schreckenberg [1], which predicts traffic jams occurring due to small errors made by drivers themselves.