“Learning to Hesitate”, 2021-06-22 (; similar):
We investigate how people make choices when they are unsure about the value of the options they face and have to decide whether to choose now or wait and acquire more information first.
In an experiment, we find that participants deviate from optimal information acquisition in a systematic manner. They acquire too much information (when they should only collect little) or not enough (when they should collect a lot). We show that this pattern can be explained as naturally emerging from Fechner cognitive errors. Over time participants tend to learn to approximate the optimal strategy when information is relatively costly.
[Keywords: search, decision under uncertainty, information, optimal stopping, real option]
…We design a controlled situation where individuals have to choose between 2 alternatives with uncertain payoffs. Before making a choice, they have the opportunity to wait and collect additional (costly) pieces of information which help them get a better idea of the likely alternatives’ payoffs. The design of the experiment allows us to precisely identify the optimal sequential sampling strategy and to assess whether participants are able to approximate it.
We find that participants deviate in systematic ways from the optimal strategy. They tend to hesitate too long and oversample information when it is relatively costly, and therefore when the optimal strategy is to collect only little information. On the contrary, they tend to undersample information when it is relatively cheap, and therefore when the optimal strategy is to collect a lot of information. We show that this pattern of oversampling and undersampling can be explained as the result of Fechner cognitive errors which introduce stochasticity in decisions about whether or not to stop. Cognitive errors create a risk to stop at any time by mistake. When the optimal level of information to acquire is high, DMs should continue to sample information for a long time. As a consequence, errors are likely to lead to stop too early, and therefore to undersampling. When the optimal level of evidence to acquire is low, DMs should stop sampling early. In that case, cognitive errors are more likely to lead to fail to stop early enough, and therefore to oversampling. The deviations we observe, lead participants to lose 10–25% of their potential payoff. However, participants learn to get closer to the optimal strategy over time, as long as information is relatively costly.
View PDF: