Author:
Bogert Eric,Schecter Aaron,Watson Richard T.
Abstract
AbstractAlgorithms have begun to encroach on tasks traditionally reserved for human judgment and are increasingly capable of performing well in novel, difficult tasks. At the same time, social influence, through social media, online reviews, or personal networks, is one of the most potent forces affecting individual decision-making. In three preregistered online experiments, we found that people rely more on algorithmic advice relative to social influence as tasks become more difficult. All three experiments focused on an intellective task with a correct answer and found that subjects relied more on algorithmic advice as difficulty increased. This effect persisted even after controlling for the quality of the advice, the numeracy and accuracy of the subjects, and whether subjects were exposed to only one source of advice, or both sources. Subjects also tended to more strongly disregard inaccurate advice labeled as algorithmic compared to equally inaccurate advice labeled as coming from a crowd of peers.
Publisher
Springer Science and Business Media LLC
Reference48 articles.
1. Schaeffer, J. et al. Checkers is solved. Science 317, 1518–1522 (2007).
2. Silver, D. et al. Mastering Chess and Shogi by self-play with a general reinforcement learning algorithm. arXiv (2017).
3. Dockrill, P. In just 4 hours, Google’s AI mastered all the chess knowledge in history. Science Alert (2017).
4. Brown, N. & Sandholm, T. Superhuman AI for multiplayer poker. Science 365, 885–890 (2019).
5. Brin, S. & Page, L. The anatomy of a large-scale hypertextual Web search engine. In Proceedings of the Seventh International Conference on World Wide Web 107–117 (1998).
Cited by
53 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献