Search Strategies in Multiattribute choice using Bayesian Belief Updating

This project examines how people learn strategies of multi-attribute decision making in an unfamiliar environment where they must learn two important properties of cues: the discriminability (i.e., the proportion of occasions where a cue has different values for a pair of options) and validity (i.e., the probability a cue identifies the correct option when a discrimination occurs). In the past, most researchers have looked at how known or guessable values of discriminability and validity relate to search and stopping decision rules. We try to understand how humans formulate search and stopping rules when they do not know the underlying discriminability and validity of cues, but must learn these over time. We model behavior using a Bayesian model where beliefs of the underlying validity and discriminability of cues are updated based on every observation made by the participant (Mistry, Lee, & Newell, 2016). We use the beliefs about discriminability and validity obtained from the Bayesian model to define different search strategies that participants might use in the task. In order to link beliefs to search strategies, we use sampling procedures where samples are drawn from the belief distributions and used to order cues for search. We test our models on data collected from human subjects and show that the modeling results intuitively map onto behavioral findings from the experiment