Economics Insights @ CBS

A consumer's decision to rely on a friend to act as an agent depends, in part, on beliefs about the friend's knowledge. Three studies examine the role of motivational and cognitive biases in estimating friends' personalized knowledge (e.g., knowledge of one's movie preferences). Results show that estimates of close friends' knowledge are less accurate than those of less close friends for personalized but not for impersonal knowledge.

Raghunathan and Pham (1999) observed that, although of the same valence, states of anxiety and sadness have distinct effects on decision making. Results from two new experiments confirm that anxiety triggers a preference for options that are more rewarding and comforting. Our results also indicate that these effects are driven by an affect-as-information process, and are most pervasive when the source of anxiety or sadness is not salient.

Hospital diagnostic facilities, such as magentic resonance imaging centers, typically provide service to several diverse patient groups: outpatients, who are scheduled in advance; inpatients, whose demands are generated randomly during the day; and emergency patients, who must be served as soon as posssible. Our analysis focuses on two inter-related tasks: designing the outpatient appoitnment schedule, and establishing dynamic priority rules for admitting patients into service.

We develop discrete choice models that account for parameter driven preference dynamics. Choice model parameters may change over time because of shifting market conditions or due to changes in attribute levels over time or because of consumer learning. In this paper we show how such preference evolution can be modeled using hierarchial Bayesian state space models of discrete choice. The main feature of our approach is that it allows for the simultaneous incorporation of multiple sources of preference and choice dynamics.