The Wisdom of Polarized Crowds
نویسندگان
چکیده
As political polarization in the United States continues to rise, the question of whether polarized individuals can fruitfully cooperate becomes pressing. Although diversity of individual perspectives typically leads to superior team performance on complex tasks, strong political perspectives have been associated with conflict, misinformation and a reluctance to engage with people and perspectives beyond one’s echo chamber. It is unclear whether self-selected teams of politically diverse individuals will create higher or lower quality outcomes. In this paper, we explore the effect of team political composition on performance through analysis of millions of edits to Wikipedia’s Political, Social Issues, and Science articles. We measure editors’ political alignments by their contributions to conservative versus liberal articles. A survey of editors validates that those who primarily edit liberal articles identify more strongly with the Democratic party and those who edit conservative ones with the Republican party. Our analysis then reveals that polarized teams—those consisting of a balanced set of politically diverse editors—create articles of higher quality than politically homogeneous teams. The effect appears most strongly in Wikipedia’s Political articles, but is also observed in Social Issues and even Science articles. Analysis of article “talk pages” reveals that politically polarized teams engage in longer, more constructive, competitive, and substantively focused but linguistically diverse debates than political moderates. More intense use of Wikipedia policies by politically diverse teams suggests institutional design principles to help unleash the power of politically polarized teams.
منابع مشابه
Wised Semi-Supervised Cluster Ensemble Selection: A New Framework for Selecting and Combing Multiple Partitions Based on Prior knowledge
The Wisdom of Crowds, an innovative theory described in social science, claims that the aggregate decisions made by a group will often be better than those of its individual members if the four fundamental criteria of this theory are satisfied. This theory used for in clustering problems. Previous researches showed that this theory can significantly increase the stability and performance of...
متن کاملModeling Wisdom of Crowds Using Latent Mixture of Discriminative Experts
In many computational linguistic scenarios, training labels are subjectives making it necessary to acquire the opinions of multiple annotators/experts, which is referred to as ”wisdom of crowds”. In this paper, we propose a new approach for modeling wisdom of crowds based on the Latent Mixture of Discriminative Experts (LMDE) model that can automatically learn the prototypical patterns and hidd...
متن کاملWised Semi-Supervised Cluster Ensemble Selection: A New Framework for Selecting and Combing Multiple Partitions Based on Prior knowledge
The Wisdom of Crowds, an innovative theory described in social science, claims that the aggregate decisions made by a group will often be better than those of its individual members if the four fundamental criteria of this theory are satisfied. This theory used for in clustering problems. Previous researches showed that this theory can significantly increase the stability and performance of...
متن کاملIntuitive Biases in Choice versus Estimation: Implications for the Wisdom of Crowds
Although researchers have documented many instances of crowd wisdom, it is important to know whether some kinds of judgments may lead the crowd astray, whether crowds’ judgments improve with feedback over time, and whether crowds’ judgments can be improved by changing the way judgments are elicited. We investigated these questions in a sports gambling context (predictions against point spreads)...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- CoRR
دوره abs/1712.06414 شماره
صفحات -
تاریخ انتشار 2017