1 research outputs found
Anchoring Bias in Online Voting
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, as well
as 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 \emph{anchoring bias} is validated via
extensive comparisons with null models, and numerically speaking, the extent of
bias decays with interval voting number 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 judgements, auctions, and so on.Comment: 5 pages, 4 tables, 5 figure