Introduction: An inclusive model of economical-social interactions and its repercussions on Big Data analysis is presented. Many phenomenological topics are involved in this job, such as the idea of complexity, statistical human behavior and market structures. Complexity on social interactions is a polemic subject, and it is also a complicated phenomenon to deal with.
Aims / Objectives: This particular study is aimed to develop some proper mathematical model to justify the big data consuming economical framework with the proper social interactions. So that it can build some major key processes assessing several types of economical frames.
Study Design: Chain Phenomena Analysis.
Place and Duration of Study: University of Guadalajara, Physics Department, Data Science Group.
Results: Model exposition.
Conclusion: This study shows how, as long as time change currently, social interaction impact on economical framework has become bigger. Big Data tools to manipulate high volume levels of information from these interactions have been a strongest platform to analyse economical indicators, such as those which repercussions affects financial stock markets. This process is modelled in this article. A complete model could be a model that considers as social interactions more factors than just trading. Although we are working to applied this model and test it with concrete data bases and real examples. This set of scalar constants defined with κ might have empty entries waiting to be filled with experimental data and empirical tuning. Stock Markets one have a better option about predictions then a social interaction based model. We are not saying that this model puts aside regular methods for stock market predictions, but, perhaps this new approach would helps to this purpose.