This idea is the feeder for the Genetic Algorithms and Simulation Environments Workshop Idea. In short, we may be able to code algorithms for many human judgments and decisions by looking at training scenarios, focusing on the heuristics used by humans and the environmental cues that drove those heuristics. This approach for encoding judgments and decisions into machine learning relies on the 'fast-and-frugal' model of judgment and decision making proposed by Gerd Gigenrenzer.
Gigenrenzer argues that humans largely rely on heuristics to form many judgments and make many decisions. Some heuristics are "hard-wired" into our evolved psychologies (fight-or-flight), and others are learned (social conventions). Gigenrenzer proposes that judging the appropriateness of the judgments and decisions calls for looking at the "ecological fitness" of the heuristics and the environment in which they were formed, then comparing this to the actual environment.
Gigenrenzer argues that this approach to judgment is decision making is the best at handling complex and uncertain environments, where outcomes cannot be optimized.