A Bayesian model that predicts the impact of Web searching on decision making

This study aimed to develop a model for predicting the impact of information access using Web searches, on human decision making. Models were constructed using a database of search behaviors and decisions of 75 clinicians, who answered questions about eight scenarios within 80 minutes in a controlled setting at a university computer laboratory. Bayesian models were developed with and without bias factors to account for anchoring, primacy, recency, exposure, and reinforcement decision biases. Prior probabilities were estimated from the population prior, from a personal prior calculated from presearch answers and confidence ratings provided by the participants, from an overall measure of willingness to switch belief before and after searching, and from a willingness to switch belief calculated in each individual scenario. The optimal Bayes model predicted user answers in 73.3% (95% Cl: 68.71 to 77.35%) of cases, and incorporated participants' willingness to switch belief before and after searching for each scenario, as well as the decision biases they encounter during the search journey. In most cases, it is possible to predict the impact of a sequence of documents retrieved by a Web search engine on a decision task without reference to the content or structure of the documents, but relying solely on a simple Bayesian model of belief revision.