Research Of Probability Judgment Under Uncertain Condition Based On Multiple-cue Probability Learning (MCPL)

Human exists in this world successfully and becomes the leader of the world, which is closely related to people's choices and judgments. Exploring people how to make judgments under complicated conditions attract many scholars. The development of rationality idea (from unbounded rationality to bounded rationality) reflects that the progress of science has giant influences on people's cognition. As for more and more people paying attention to probability judgment under uncertain conditions, many scientists were engaged in researches of this field. After Bayes Theory, Bounded Rationality and Heuristics were introduced into this realm, the research about probability had a lot of changes.With the development of Science and cognitive psychology, people recognize that our ability is limited, and Bounded rationality is a good example of it. Facing vast information, people have to accept some selectively and implement heuristics to make decisions. After 1980, heuristics began the topic which many psychologists were very interested in, the research of Kahneman etc is most representative. Undoubtedly, heuristics idea is not perfect, so some psychologists put forward some new theories, such as Support Theory, ESAM and Simple Heuristics.This research, based on multiple-cue probability learning and ESAM, is for interpreting people how to make decision under complicated conditions, whether their judgment will change under various cue structure and what the inside mechanism is. The simulation of uncertain conditions depends on controlling cues' states (cues' presence, cues' absence and its unknown). With 3 experiments, I find that judgment under complete information condition is different from judgment under partial information condition evidently. I also find some factors influencing people' judgment and demonstrate that people will make decision with the help of the method of infer-mean when some cues are unknown.