Christoph Riedl

Education

• PhD Information Systems, Technische Universität München
• MSc Information Systems, Technische Universität München
• BSc Computer Science, Technische Universität München

Selected Publications

• Wan, A., Riedl, C., & Lazer, D. (2025). “Diffusion of Complex Contagions is Shaped by a Tradeoff Between Reach and Reinforcement.” Proceedings of the National Academy of Sciences (PNAS), 122(28) e2422892122.
• Grad, T., Riedl, C., & Kilduff, G. (2025). “When Rivalry Backfires: How Individual Skill and Risk of Status Loss Moderate the Effects of Rivarly on Performance.” Management Science, in press.
• Riedl, C. (2024). “How to Use AI to Build Your Company's Collective Intelligence.” Harvard Business Review, October, preprint.
• Riedl, C., Grad, T., & Lettl, C. (2024). “Competition and Collaboration in Crowdsourcing Communities: What happens when peers evaluate each other?” Organization Science, 35(6), 1957-2332.
• Riedl, C., Hutter, K., Füller, J., Tellis, G. (2024). “Cash or Non-Cash? Unveiling Ideators' Incentive Preferences in Crowdsourcing Contests,” Journal of Management Information Systems, 41(2), 487-514.
• Fulker, Z., Riedl, C. (2024). “Cooperation in the Gig Economy: Insights from Upwork Freelancers,” Proceedings of the ACM on Human Computer Interaction, 8(CSCW1), 37.
• Kiron, D., Altman, E.J., Riedl, C. (2023). “Workforce Ecosystems and AI,” Brookings Institute, in press.
• Westby, S. & Riedl, C. (2023). Collective Intelligence in Human-AI Teams: A Bayesian Theory of Mind Approach.  In Proceedings of the 37th AAAI Conference on Artificial Intelligence (2023). arXiv:2208.11660.
• Woolley, A.W., Chow, R., Mayo, A., Riedl, C., Chang, J. W. (2022). “Collective attention and collective intelligence: The role of hierarchy and team gender composition,” Organization Science, 34(3), 1315–1331.
• Riedl, C., Malone, T.W., Woolley, A.W. (2021). “The Collective Intelligence of Remote Teams,” MIT Sloan Management Review, October, 2021, article.
• Riedl, C., Kim, Y.J., Gupta, P., Malone, T.W., Woolley, A.W. (2021). “Quantifying Collective Intelligence in Human Groups,” Proceedings of the National Academy of Sciences (PNAS), 118 (21) e2005737118.
• Balietti, S., Riedl, C. (2021). “Incentives, competition, and inequality in markets for creative production,” Research Policy, 50(4), 104212.
• Fulker, Z., Forber, P., Smead, R., Riedl, C. (2021). “Spite is Contagious in Dynamic Networks,” Nature Communications, 12(260).
• Riedl, C., Woolley, A. (2020). “Successful Remote Teams Communicate in Bursts,” Harvard Business Review, October, 2020.
• Fraiberger, S., Sinatra, R., Resch, M., Riedl, C., Barabási, A.L. (2018). “Quantifying Reputation and Success in Art” (shared last author with ALB). Science, 362(6416), 825-829.
• Riedl, C., Seidel, V. (2018). “Learning from Mixed Signals in Online Innovation Communities,” Organization Science, 29(6), 1010-1032.
• Riedl,* C., Woolley, A.W. (2017). “Teams vs. Crowds: A Field Test of the Relative Contribution of Incentives, Member Ability, and Emergent Collaboration to Crowd-Based Problem Solving Performance,” Academy of Management Discoveries, 3(4), 382-403.
• Boudreau, K., Guinan, E., Lakhani, K., Riedl, C. (2016). “Looking Across and Looking Beyond the Knowledge Frontier: Intellectual Distance and Resource Allocation in Science,” Management Science, 62(10), 2765-2783.
• Blohm, I., Riedl, C., Füller, J., Leimeister, J.M. (2016). “Rate or Trade? Identifying Winning Ideas in Open Idea Sourcing,” Information Systems Research, 27(1), 27-48.

Selected Presentations

• “Collective Intelligence in Human-AI Teams: A Bayesian Theory of Mind Approach,” Harvard Business School, invited talk, February, 2023.
• “Video Moves You,” invited talk CODE@MIT, Sloan School of Business, Cambridge, MA November 10th 2023.
• “How Human-Agent Teams Will Revolutionize the Future of Work,” 2020 INGRoup conference, Seattle, WA.
• Invited talk “Avoiding the Bullies: How High-Ability Agents Promote Cooperation in Social Networks”, IMT Lucca, March 12^th, 2020.

Research & Teaching Interests

Riedl's research interests are in collective intelligence and human-AI interaction. Riedl teaches courses on AI and Machine Learning, Digital Business Transformation, and Network Economics.

Services to the Profession

• Member Editorial Review Board for Academy of Management Discoveries.
• Core faculty, Network Science Institute.
• Core faculty, Center for Digital Humanities & Computational Social Science (NUlab)
• Visiting Fellow, Institute for Quantitative Social Science (IQSS), Harvard University

Awards & Recognition

• Walsh Professorship (D'Amore-McKim School of Business, 2021-2023)
• Research grant over $1,500,000 by Army Research Lab (ARL), 2019-2022
• INFORMS TIMES Best Paper Award, runner-up, 2021
• Most Novel Paper Award, Strategic Management Society Annual Conference 2020
• Best Teacher Award (D'Amore-McKim School of Business, 2018)
• Best Paper Award of the Academy of Management Discoveries, finalist 2018