Main topics of this chapter include (1) utilities of modeling; (2) explication of assumptions; (3) stochastic instruments; (4) Gaussian theory; (5) contingency; (6) uncertainty; and a variety of types of models relevant to social processes, including Brownian motion (arithmetic and geometric), Poisson-point modeling, Wiener process, Markov process, directed driving-force models, and long-memory process models, followed by factors of model selection. The descriptions of types of modeling begin elementally, then advance, and while some mathematical notation is used, it is followed by narrative explication. The emphasis in these discussions is on selection of a “best” model of a stream of outcomes (time-series data) of a generative process, where “best” is judged by relevance, effectivity, and efficiency in capturing both nonlinear and linear features of the time-series. Cross-sectional analysis is recognized but subordinated to the primary task of understanding the processual information contained in the stream of outcomes.