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.

It appears that socio-economical regularities may be understood not only by using physical or mathematical intuition or even by using the ideas from social sciences. It appears that even philologists may have something to say about our socio-economical. For example the tendency to willingness to save money for the future may be dependent on if our mother-tongue is futureless or not (do we say "It rain tomorrow" or "It will rain tomorrow"? e.g., German may be seen as a futureless language, while Lithuanian and English have strongly differing future tense). Below you will find two videos - one made by eBay Deals, which provides a broad outlook, and another recorded by TED, in which K. Chen presents his research, - which present this kind of research in more detail.

In this talk, published on ted.com website, Philip Evans discusses the current developments of contemporary business models and gives some insights into how the Big data will influence the future business.