Funded PhD Research

Joshua Lewis

Extreme Aversion in Estimation and Retail Price Setting

Operations, Information, & Decisions Department; Faculty Adviser: Joseph Simmons

Retailers must estimate how much their customers would pay for their goods, how frequently they should restock, how much more they can sell of one good than an alternative, and much more. Consequently, estimation biases can adversely affect a retailer’s bottom line.

In our research, we investigate how the implicit or explicit range of possible values that people consider when making their estimates can influence and hence bias those estimates. We hypothesize that people try to avoid making estimates that they perceive as unusually extreme. This form of response conservatism could lead retailers to undercharge for expensive goods and over charge for cheap goods. Thus far, we have identified two manifestations of this effect.

First, extending the range of possible values for people to consider makes their estimates more extreme. Second, estimates of differences (e.g., estimates of the difference between the prices of two goods) are less extreme than the difference between equivalent separate estimates (e.g., the difference between estimates of the prices of the two goods) because the range of values which people consider for differences is more restrictive. We can moderate this bias by imposing an equivalent response scale for both forms of estimate.

Our next goal is to extend these findings into more retail-relevant domains, identify boundary conditions for our effects and develop interventions to improve retail-related estimates.


Publications, Presentations & Awards