[1] D. Acemoglu, A. Makhdoumi, A. Malekian, and A. Ozdaglar. Learning From Reviews: The Selection Effect and the Speed of Learning. *Econometrica*, 2022. Working paper available since 2017.
[2] S. Agrawal and N. Goyal. Analysis of Thompson Sampling for the multi-armed bandit problem. In 25nd Conf. on Learning Theory (COLT), 2012.
[3] S. Agrawal and N. Goyal. Analysis of thompson sampling for the multi-armed bandit problem. In Conference on learning theory, pages 39–1. JMLR Workshop and Conference Proceedings, 2012.
[4] S. Agrawal and N. Goyal. Near-optimal regret bounds for thompson sampling. J. of the ACM, 64(5):30:1-30:24, 2017. Preliminary version in AISTATS 2013.
[5] J. Audibert and S. Bubeck. Regret Bounds and Minimax Policies under Partial Monitoring. J. of Machine Learning Research (JMLR), 11:2785-2836, 2010. Preliminary version in COLT 2009.
[6] P. Auer, N. Cesa-Bianchi, and P. Fischer. Finite-time analysis of the multiarmed bandit problem. Machine Learning, 47(2-3):235-256, 2002.
[7] P. Auer, N. Cesa-Bianchi, Y. Freund, and R. E. Schapire. The nonstochastic multiarmed bandit problem. SIAM J. Comput., 32(1):48-77, 2002. Preliminary version in 36th IEEE FOCS, 1995.
[8] V. Bala and S. Goyal. Learning from neighbours. Review of Economic Studies, 65(3):595-621, 1998.
[9] A. V. Banerjee. A simple model of herd behavior. Quarterly Journal of Economics, 107: 797-817, 1992.
[10] N. Barberis and R. Thaler. A survey of behavioral finance. In Handbook of the Economics of Finance, volume 1, pages 1053-1128. Elsevier, 2003.
[11] H. Bastani, M. Bayati, and K. Khosravi. Mostly exploration-free algorithms for contextual bandits. Management Science, 67(3):1329-1349, 2021. Working paper available on arxiv.org since 2017.
[12] M. Bateson. Optimistic and pessimistic biases: a primer for behavioural ecologists. Current Opinion in Behavioral Sciences, 12:115-121, 2016.
[13] M. Bayati, N. Hamidi, R. Johari, and K. Khosravi. Unreasonable effectiveness of greedy algorithms in multi-armed bandit with many arms. In 33rd Advances in Neural Information Processing Systems (NeurIPS), 2020.
[14] S. Bikhchandani, D. Hirshleifer, and I. Welch. A Theory of Fads, Fashion, Custom, and Cultural Change as Informational Cascades. Journal of Political Economy, 100(5):992-1026, 1992.
[15] A. Bohren and D. N. Hauser. Learning with Heterogeneous Misspecified Models: Characterization and Robustness. *Econometrica*, 89(6):3025–3077, Nov 2021.
[16] P. Bolton and C. Harris. Strategic Experimentation. *Econometrica*, 67(2):349-374, 1999.
[17] S. Bubeck and N. Cesa-Bianchi. Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems. Foundations and Trends in Machine Learning, 5(1):1-122, 2012. Published with Now Publishers (Boston, MA, USA). Also available at https://arxiv.org/abs/1204.5721.
[18] E. C. Chang, editor. Optimism and pessimism: Implications for theory, research, and practice. American Psychological Association, 2000. See https://www.apa.org/pubs/books/431754A for the table of contents.
[19] Y.-K. Che and J. Horner. Recommender systems as mechanisms for social learning. Quarterly Journal of Economics, 133(2):871-925, 2018. Working paper since 2013, titled 'Optimal design for social learning'.
[20] B. Chen, P. I. Frazier, and D. Kempe. Incentivizing exploration by heterogeneous users. In Conf. on Learning Theory (COLT), pages 798-818, 2018.
[21] M. H. DeGroot. Reaching a Consensus. Journal of the American Statistical Association, 69 (345):118-121, 1974.
[22] A. V. den Boer and B. Zwart. Simultaneously learning and optimizing using controlled variance pricing. Management Science, 60(3):770-783, 2014.