Automated Scientific Modeling from Observed Data and its Application to Socio-Psychology

The knowledge-based automated modeling framework such as CML can be applied only to the systems where their valid background knowledge is available . The conventional model equation discovery systems such as BACON require experimental environments to acquire their necessary data . The mathematical techniques, e.g ., linear system identification and neural network fitting, presume the classes of equations to model a given observed data set . The study reported in this paper proposes a novel method to discover an admissible model equation from a given set of observed data while the equation is ensured to reflect first principles governing the objective system. The power of the proposed method comes from the use of the scale.types of the observed quantities, a mathematical property of identity and quasibi-variate fitting which identify the admissible solutions from the given data set . Its principles and automated algorithm are described with moderately complex examples, and its practicality is demonstrated through the real application to a socio-psychological modeling task .