Analysis and generalization of anchoring effects in rate estimation using a Bayesian update model

Abstract

In recent years, the Bayesian brain hypothesis has been proposed in the field of neuroscience, which states that Bayesian updating is used in decision making based on uncertain input stimuli. In behavioral economics, the anchoring effect has been known as a phenomenon in which judgments and estimates are influenced by numbers presented in advance, but Turner et al. showed that the phenomenon can be explained by Bayesian updating. Ozawa et al. found that the Turner et al. model assuming a normal distribution had problems when the amount of knowledge was small, and proposed a model combining logarithmic preprocessing and a t-distribution model extending the normal distribution to solve the problems. The result of Ozawa et al. is a model limited to size estimation that takes positive real numbers, but proportion estimation is also well known as a target where the anchoring effect occurs. Therefore, we hypothesize that the general anchoring model can be explained by changing the preprocessing appropriately and examine whether the model with the logit function as the preprocessing in the case of proportion estimation is consistent with the experimental results.