By Aki-Hiro Sato
Applied data-centric social sciences objective to improve either method and useful purposes of varied fields of social sciences and companies with wealthy info. in particular, within the social sciences, an unlimited volume of information on human actions will be worthy for realizing collective human nature. during this e-book, the writer introduces numerous mathematical thoughts for dealing with an incredible quantity of information and analysing collective human behaviour. The booklet is constituted of data-oriented research, with mathematical tools and expressions used for facing info for a number of particular difficulties. the basic philosophy underlying the booklet is that either mathematical and actual ideas are decided by way of the needs of knowledge research. This philosophy is proven all through exemplar stories of numerous fields in socio-economic structures. From a data-centric standpoint, the writer proposes an idea which could swap people’s minds and lead them to commence considering from the foundation of information. a number of targets underlie the chapters of the ebook. the 1st is to explain mathematical and statistical equipment for info research, and towards that finish the writer delineates equipment with real facts in each one bankruptcy. the second one is to discover a cyber-physical hyperlink among facts and data-generating mechanisms, as info are continually supplied by way of a few type of data-generating method within the actual international. The 3rd aim is to supply an impetus for the ideas and method set forth during this e-book to be utilized to socio-economic systems.
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Extra resources for Applied Data-Centric Social Sciences: Concepts, Data, Computation, and Theory
The degree of this paradox is related to the distinction between implicit and explicit knowledge. This is deeply related to a transition process from the implicit to the explicit and emergent properties. We can assume or infer that there is a mechanism of a phenomenon, however, we do not have a concrete way to observe the phenomenon. In this case, we face this situation. What type of methodology do we have available to bring out these emergent properties? (UU) Unknown Unknown (No Model and No Data): It is very difficult to imagine what kinds of events might be included here.
Natural language processing (NLP) provides methods that enable computers to derive meaning from human or natural language input. NLP tasks include automatic symmetrisation, discourse analysis, machine translation, morphological segmentation and named entity recognition, information retrieval (IR) and information extraction (IE). For example, the automatic symmetrisation is to produce a readable summary from a chunk of text. The discourse analysis is a work to identifying the discourse structure of connected text.
The model parameters are estimated by using the maximum likelihood procedure. The models are evaluated based on an information criterion, and an adequate model should be selected by maximising the information criterion. Several types of information criteria have been proposed . The Akaike Information criterion is one of them. The Bayesian information criterion is also often used. The information criterion is defined from the maximum log-likelihood value and a penalty function in terms of the number of parameters and/or the number of observations (see Sect.