Learning What to Want: Context-Sensitive Preference Learning.
We have developed a method for learning relative preferences from histories of choices made, without requiring an intermediate utility computation. Our method infers preferences that are rational in a psychological sense, where agent choices result from Bayesian inference of what to do from observable inputs. We further characterize conditions on choice histories wherein it is appropriate for modelers to describe relative preferences using ordinal utilities, and illustrate the importance of the influence of choice history by explaining all major categories of context effects using them. Our proposal clarifies the relationship between economic and psychological definitions of rationality and rationalizes several behaviors heretofore judged irrational by behavioral economists.
Dept of Psychology, UC San Diego, La Jolla, CA, United States of America.
Nisheeth
N
Srivastava
Depts of Psychology & Computer Science, University of Minnesota, Minneapolis, MN, United States of America.
Paul
P
Schrater
D001499:Bayes Theorem; D002755:Choice Behavior; D003657:Decision Making; D004193:Discrimination Learning; D006801:Humans; D007600:Judgment; D008960:Models, Psychological; D015233:Models, Statistical; D011908:Rationalization