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Salganik, M. J., Dodds, P. S., & Watts, D. J. (2022). Experimental study of inequality and unpredictability in an artificial cultural market. Edward Elgar Publishing. https://www.elgaronline.com/view/book/9781789909432/book-part-9781789909432-32.xml Cite

Stefano Balietti, Lise Getoor, Daniel G. Goldstein, Duncan J. Watts. (2021, December 22). Reducing opinion polarization: Effects of exposure to similar people with differing political views | PNAS. https://www.pnas.org/doi/abs/10.1073/pnas.2112552118 Cite

Almaatouq, A., Becker, J., Bernstein, M., Botto, R., Bradlow, E., Damer, E., Duckworth, A., Griffiths, T., Hartshorne, J., Law, E., Lazer, D., Liu, M., Matias, J. N., Rand, D., Salganik, M., Satlof-Bedrick, E., Schweitzer, M., Shirado, H., Suchow, J., … Yin, M. (2021). Scaling up experimental social, behavioral, and economic science. OSF Preprints. https://doi.org/10.31219/osf.io/wksv8 Cite Download

Abdullah Almaatouq, Mohammed Alsobay, Ming Yin, and Duncan J. Watts. (2021, September 21). Task complexity moderates group synergy. https://doi.org/10.1073/pnas.2101062118 Cite Download

Konitzer, T., Allen, J., Eckman, S., Howland, B., Mobius, M., Rothschild, D., & Watts, D. J. (2021). Comparing Estimates of News Consumption from Survey and Passively Collected Behavioral Data. Public Opinion Quarterly, 85(S1), 347–370. https://doi.org/10.1093/poq/nfab023 Cite Download

Hosseinmardi, H., Ghasemian, A., Clauset, A., Mobius, M., Rothschild, D. M., & Watts, D. J. (2021). Examining the consumption of radical content on YouTube. Proceedings of the National Academy of Sciences, 118(32), e2101967118. https://doi.org/10.1073/pnas.2101967118 Cite Download

Allen, J., Mobius, M., Rothschild, D. M., & Watts, D. J. (2021). Research note: Examining potential bias in large-scale censored data. Harvard Kennedy School Misinformation Review. https://doi.org/10.37016/mr-2020-74 Cite Download

Jake M. Hofman, Duncan J. Watts, Susan Athey, Filiz Garip, Thomas L. Griffiths, Jon Kleinberg, Helen Margetts, Sendhil Mullainathan, Matthew J. Salganik, Simine Vazire, Alessandro Vespignani & Tal Yarkoni. (2021, June 30). Integrating explanation and prediction in computational social science | Nature. https://www.nature.com/articles/s41586-021-03659-0 Cite

Abigail Z. Jacobs, Duncan J. Watts. (2021, April 16). A Large-Scale Comparative Study of Informal Social Networks in Firms. https://doi.org/10.1287/mnsc.2021.3997 Cite Download

Duncan J. Watts, David M. Rothschild, Markus Mobius. (2021, April 9). Measuring the news and its impact on democracy. https://doi.org/10.1073/pnas.1912443118 Cite Download

Almaatouq, A., Becker, J., Houghton, J. P., Paton, N., Watts, D. J., & Whiting, M. E. (2021). Empirica: a virtual lab for high-throughput macro-level experiments. Behavior Research Methods, 53(5), 2158–2171. https://doi.org/10.3758/s13428-020-01535-9 Cite Download

Lifchits, G., Anderson, A., Goldstein, D. G., Hofman, J. M., & Watts, D. J. (2021). Success stories cause false beliefs about success. Judgment and Decision Making, 16(6), 1439–1463. https://econpapers.repec.org/article/jdmjournl/v_3a16_3ay_3a2021_3ai_3a6_3ap_3a1439-1463.htm Cite

DAVID M. J. LAZER, ALEX PENTLAND, DUNCAN J. WATTS, SINAN ARAL, SUSAN ATHEY, NOSHIR CONTRACTOR, DEEN FREELON, SANDRA GONZALEZ-BAILON, GARY KING, HELEN MARGETTS, ALONDRA NELSON, MATTHEW J. SALGANIK, MARKUS STROHMAIER, ALESSANDRO VESPIGNANI, CLAUDIA WAGNER. (2020, August 28). Computational social science: Obstacles and opportunities. https://www.science.org/doi/abs/10.1126/science.aaz8170 Cite

Evaluating the fake news problem at the scale of the information ecosystem. (2020, April 3). https://doi.org/10.1126/sciadv.aay3539 Cite Download

Measuring the predictability of life outcomes with a scientific mass collaboration | PNAS. (2020, March 30). https://www.pnas.org/doi/abs/10.1073/pnas.1915006117 Cite

Almaatouq, A., Yin, M., & Watts, D. J. (2020). Collective problem-solving of groups across tasks of varying complexity. https://psyarxiv.com/ra9qy/download?format=pdf Cite

Lazer, D. M., Pentland, A., Watts, D. J., Aral, S., Athey, S., Contractor, N., Freelon, D., Gonzalez-Bailon, S., King, G., & Margetts, H. (2020). Computational social science: Obstacles and opportunities. Science, 369(6507), 1060–1062. Cite

Meyer, M. N., Heck, P. R., Holtzman, G. S., Anderson, S. M., Cai, W., Watts, D. J., & Chabris, C. F. (2019). Reply to Mislavsky et al.: Sometimes people really are averse to experiments. Proceedings of the National Academy of Sciences, 116(48), 23885–23886. https://doi.org/https://doi.org/10.1073/pnas.1914509116 Cite

Heck, P. R., Chabris, C., Watts, D. J., & Meyer, M. (2019). Sometimes People Dislike Experiments More than They Dislike Their Worst Conditions: Within-Subjects Evidence for “Experiment Aversion” and the A/B Effect. https://psyarxiv.com/jmxgc/download Cite

Risi, J., Sharma, A., Shah, R., Connelly, M., & Watts, D. J. (2019). Predicting history. Nature Human Behaviour, 3(9), 906–912. https://www.nature.com/articles/s41562-019-0620-8 Cite

Meyer, M. N., Heck, P. R., Holtzman, G. S., Anderson, S. M., Cai, W., Watts, D. J., & Chabris, C. F. (2019). Objecting to experiments that compare two unobjectionable policies or treatments. Proceedings of the National Academy of Sciences, 116(22), 10723–10728. Cite