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Information is very important. Information is also very complicated, making that people have no common understanding and conclusion for the nature of it up today. There are too many papers and some books to describe information; however it is rather difficult to find the description and analysing for the whole history of information from the advent of human beings to the present day. Two parts of information in prehistoric period and the time interred divinization are described. Every part is separated according to several succeeded stages for description. It is near impossible to describe in detail such entire historical facts of information in human society in a paper, so the description and discussion is focused on their comprehensiveness and integrity. By knowing and analysing all these solid historical facts of information, some relative issues e.g. “did information age really exist in the development of material civilization in human society” can be recognized easily.

Ever increasing scope and complexity of regulations and other rules that govern human society emphasise importance of the inspection of compliance to those rules. Often-used approaches to the inspection of compliance suffer from drawbacks such as overly idealistic assumptions and narrowness of application. Specifically, inspection models are frequently limited to situations where inspected entity has to comply with only one rule. Furthermore, inspection strategies regularly overlook some useful and available information such as varying costs of compliance to different rules. This article presents an agent-based model for inspection of compliance to many rules, which addresses abovementioned drawbacks. In the article, crime economic, game-theoretic and agent- based modelling approaches to inspection are briefly described, as well as their impact on the model. The model is described and simulation of a simplified version of the model is presented. The obtained results demonstrate that inspection strategies which take into account rules’ compliance costs perform significantly better than random strategies and better than cycle-based strategies. Additionally, the results encourage further, wider testing and validation of the model.