title: Integration of Social Information by Human Groups.
abstract: We consider a situation in which individuals search for accurate decisions without direct feedback on their accuracy, but with information about the decisions made by peers in their group. The "wisdom of crowds" hypothesis states that the average judgment of many individuals can give a good estimate of, for example, the outcomes of sporting events and the answers to trivia questions. Two conditions for the application of wisdom of crowds are that estimates should be independent and unbiased. Here, we study how individuals integrate social information when answering trivia questions with answers that range between 0% and 100% (e.g., "What percentage of Americans are left-handed?"). We find that, consistent with the wisdom of crowds hypothesis, average performance improves with group size. However, individuals show a consistent bias to produce estimates that are insufficiently extreme. We find that social information provides significant, albeit small, improvement to group performance. Outliers with answers far from the correct answer move toward the position of the group mean. Given that these outliers also tend to be nearer to 50% than do the answers of other group members, this move creates group polarization away from 50%. By looking at individual performance over different questions we find that some people are more likely to be affected by social influence than others. There is also evidence that people differ in their competence in answering questions, but lack of competence is not significantly correlated with willingness to change guesses. We develop a mathematical model based on these results that postulates a cognitive process in which people first decide whether to take into account peer guesses, and if so, to move in the direction of these guesses. The size of the move is proportional to the distance between their own guess and the average guess of the group. This model closely approximates the distribution of guess movements and shows how outlying incorrect opinions can be systematically removed from a group resulting, in some situations, in improved group performance. However, improvement is only predicted for cases in which the initial guesses of individuals in the group are biased.
authors.affiliation: Department of Mathematics, Uppsala University, Institute for Futures Studies, Stockholm.
authors.forename: Boris
authors.initials: B
authors.lastname: Granovskiy
authors.affiliation: Department of Psychological and Brain Sciences, Indiana University.
authors.forename: Jason M
authors.initials: JM
authors.lastname: Gold
authors.affiliation: Department of Mathematics, Uppsala University, Institute for Futures Studies, Stockholm.
authors.forename: David J T
authors.initials: DJ
authors.lastname: Sumpter
authors.affiliation: Department of Psychological and Brain Sciences, Indiana University.
authors.forename: Robert L
authors.initials: RL
authors.lastname: Goldstone
mesh_terms: D000465:Algorithms; D003071:Cognition / Q000502:physiology*; D003441:Crowding / Q000523:psychology; D063045:Crowdsourcing / Q000379:methods; D003657:Decision Making / Q000502:physiology*; D003858:Dependency, Psychological; D030141:Feedback, Psychological / Q000502:physiology; D006801:Humans; D007206:Individuality; D007600:Judgment / Q000502:physiology; D016743:Mental Competency / Q000523:psychology; D008962:Models, Theoretical; D011639:Public Opinion; D012919:Social Behavior; D013334:Students / Q000523:psychology / Q000706:statistics & numerical data
keywords: Collective behavior; Human decision making; Polarization; Social information; Wisdom of crowds