Previous research has shown that individuals appear to underinfer from strong singals and overinfer from weak signals. This paper studies how individuals react to changes in signal strength. We hypothesise that individuals under and overreact to changes in signals. We test this hypothesis using an experimental setting and a naturalistic setting in which individuals react to signals about future outcomes. We suggest this behaviour can explain a number of phenomena in economics, including the underdog effect.
This paper examines how sequential contrast effects, the tendency to judge stimuli relative to preceding experiences, influence online ratings. While laboratory studies have documented contrast effects, field evidence remains limited by non-random ordering. We overcome this challenge by exploiting settings where ordering is either random or plausibly orthogonal to preferences. We identify persistence in rating outcomes using data on hundreds of millions of reviews left in online settings, including Amazon product reviews and Google Maps reviews. Preliminary results suggest positive contrast effects: after rating reviews highly, individuals tend to rate reviews higher than if followed by a lower rating. These findings have important implications for the reliability of online rating systems and help reconcile conflicting evidence between laboratory and field studies of sequential judgments.
We study how firms update their reference points when information is revealed to a market in a setting where firms are set to lose millions of pounds for deviating from rational decision making: football player transfers. We collect data every football player transfer made by firms in the ‘Big Five’ football leagues over a ten-year period and control for variation in transfer fees with data on club finances, player performances, player histories, and player injuries. Our preliminary results suggest that superstar transfers do not shift reference points and do not distort prices in this market.
We study how managers adjust their selection behaviour based on the precision of beliefs about the future returns of workers. We consider the role of ambiguity aversion on how individual decision makers deviate from Bayesian updating through aversion to variance in belief distributions. We introduce a model of belief updating which accommodates ambiguity aversion. We then propose estimating this with a structural choice model. This model is able to separate ambiguity aversion from other behavioural biases.