Anchoring bias in online voting

Abstract

Voting online with explicit ratings could largely reflect people's preferences and objects' qualities, but ratings are always irrational, because they may be affected by many unpredictable factors like mood, weather and other people's votes. By analyzing two real systems, this paper reveals a systematic bias embedding in the individual decision-making processes, namely people tend to give a low rating after a low rating, as well as a high rating following a high rating. This so-called anchoring bias is validated via extensive comparisons with null models, and numerically speaking, the extent of bias decays with voting interval in a logarithmic form. Our findings could be applied in the design of recommender systems and considered as important complementary materials to previous knowledge about anchoring effects on financial trades, performance judgments, auctions, and so on.