It is well know that mathematical modelling in social sciences, particularly when concepts originally rooted in natural sciences are used, is, from methodological point of view, a touchy subject since the problem of reductionism can appear in this context. This paper addresses such a subject for its main objective is to discuss how the entropy concept, originally physical one, can generally be used in modelling, especially in the domain of social sciences. The way how this topic is approached in this paper is based on using the Jaynes maximum entropy principle saying that from all probability distributions, which can be used to describe/model the distribution issue analysed and which are compatible with an information available, is to prefer one which results from maximising the Shannon information entropy since only this one is least biased. In such a way the principle turns out to be a statistical inference principle not bound to purely physical domain, having a high degree of generality and avoiding a danger of reductionism. Really, the Jaynes principle, besides physics itself, found a broad application in a non-physical domain. In this paper the application on modelling spatial organisation of society is demonstrated, specifically on the derivation of maximum entropy models of spatial interaction describing the movement of people, commodities, etc. between different places and areas.