Abstract :
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% CI: 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.
