The “Hot Stove Effect” pertains to an asymmetry in error corrections that affects a learner who estimates the quality of an option based on his or her experience with the option: errors of overestimation of the quality of an option are more likely to be corrected than errors of underestimation. In this chapter, we describe a “Collective Hot Stove Effect” which characterizes the dynamics of collective valuations rather than individual quality estimates. We analyze settings in which the collective valuation of options is updated sequentially based on additional samples of information. We focus on cases where the collective valuation of an option is more likely to be updated when it is higher than when it is lower. Just as the law-of-effect implies a Hot Stove Effect for individual learners, a Collective Hot Stove Effect emerges: errors of overestimation of the quality of an object by the collective valuation are more likely to be corrected than errors of underestimation. We test the unique predictions of our model in an online experiment and test assumptions and predictions of our model in analyses of large datasets of online ratings from popular websites (Amazon.com, Yelp.com, Goodreads.com, Weedmaps.com) comprising more than 160 million ratings.