Representing and Predicting Everyday Behavior

TL;DR: This work uses distributed semantic models to obtain vector representations for behaviors, and combines these with demographic and psychographic data, to build supervised, deep neural network models of behavioral propensities for a representative sample of the US population.

Abstract: The prediction of everyday human behavior is a central goal in the behavioral sciences. However, efforts in this direction have been limited, as (1) the behaviors studied in most surveys and experiments represent only a small fraction of all possible behaviors, and (2) it has been difficult to generalize data from existing studies to predict arbitrary behaviors, owing to the difficulty in adequately representing such behaviors. Our paper addresses each of these problems. First, by sampling frequent verb phrases in natural language and refining these through human coding, we compile a dataset of nearly 4,000 common human behaviors. Second, we use distributed semantic models to obtain vector representations for our behaviors, and combine these with demographic and psychographic data, to build supervised, deep neural network models of behavioral propensities for a representative sample of the US population. Our best models achieve high accuracy rates when predicting propensities for novel (out-of-sample) participants as well as novel behaviors. This work lays the foundation for new predictive theories of everyday behavior, improving the generality and naturalism of research in the behavioral sciences.