Automated learning of models for domain theories

A computer implemented method for determining a model, the method comprising:

• (a) identifying, by one or more computing devices, a schema that defines a possible causal element of a particular type of behavior;
• (b) determining, by the one or more computing devices, one or more concepts provided in the schema, and one or more sub-concepts for each concept, each concept and sub-concept being associated with uncalibrated weights and a functional form which represent a logical relationship between them;
• (c) determining, by the one or more computing devices, multiple models from the one or more concepts and the one or more sub-concepts;
• (d) calibrating, by the one or more computing devices, the multiple models using representative data collected through a monitoring channel from a real-world source, wherein the representative data is inaccessible to a user;
• (e) updating the multiple models with weights to determine an optimal model amongst a plurality of calibrated models;
• (f) determining, by the one or more computing devices, the optimal model amongst the plurality of calibrated models, wherein determining the optimal model includes adaptively learning the optimal model by repeating (c) and (d) to converge on the optimal model;
• and
• (g) transmitting, by the one or more computing devices, the optimal model to a user interface to thereby support measurement of marketing effectiveness in response to one or more stimuli in a marketplace.