(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 each 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 from a real-world source, wherein the calibration utilizes a limited-memory Broyden-Fletcher-Goldfarb-Shanno algorithm to iteratively evaluate a series of possible weight values for a given combination of sub-concepts;

(e) updating the uncalibrated weights with weights from the possible weight values 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 based on a model score that includes a determination of a model fit and a compliance with an expected attribution associated with an existing knowledge of the schema, wherein the model fit is calculated based on real-time data from the real-world source, and 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.