Our increasingly complex environment and rapidly evolving technologies require decision makers who efficiently process multiple data streams from disparate sources while providing sound judgments. The judgment and decision-making scientific literature has significantly increased over the past two decades as sensors collect more information about the decision maker and surrounding environment, new techniques for assessing human judgment effectiveness have been developed, and increased computing power allows researchers to examine tactical-level decisions in greater detail and strategic-level decisions over longer timeframes. Decision theories are broadly classified into two categories: normative, which explains how decisions ought to be made, and descriptive, which explains how decisions actually are made. Furthermore, descriptive theories may also be predictive, such as Signal Detection Theory (Green & Swets, 1996) or Judgment Analysis (Cooksey, 1996). Within the scientific literature, various approaches to developing predictive decision theories include: comparing human behavior to an ideal model (Edwards, 1954), assessing judgments based on rationality in a complex uncertain environment (Simon, 1955), using expertise for the recognition-primed approach (Klein, 1997), considering cognitive heuristics and biases in human judgment (Kahneman, Slovic, & Tversky, 1982), and modeling human behavior using a cognitive algebra approach (Hammond, 1955). This presentation will provide an overview of the latter, and how Probabilistic Functionalism and the cognitive algebraic approach has been extensively used across domains such as law enforcement, medical diagnosis, education, and public policy development.