Research Areas
Collective Intelligence & Human-Agent Teams
We investigate how team composition and network structure affect synchronization, collaboration, and performance. Integral to this work is the understanding that teams may be structured in many different ways, composed of parties with differing characteristics. This may include a number of possible connections between scientists, artificially intelligent agents, digital platform users, coworkers, and so on. Our current and past work in this area evaluates team hierarchy, team gender composition, human-agent teaming, virtual teams, and collective intelligence more broadly. Most recently we have focused on studies of nonverbal and verbal synchronization using the free-energy principle, the effect of team structure and incentives on problem-solving performance, productive collaborations between scientists and AI, and progressive performance of online teams via virtual meeting room environments and virtual AI agents.
Lab Members: Zach Fulker, V Lange, Sam Westby, Kristen Flaherty
Key Publications:
We investigate how team composition and network structure affect synchronization, collaboration, and performance. Integral to this work is the understanding that teams may be structured in many different ways, composed of parties with differing characteristics. This may include a number of possible connections between scientists, artificially intelligent agents, digital platform users, coworkers, and so on. Our current and past work in this area evaluates team hierarchy, team gender composition, human-agent teaming, virtual teams, and collective intelligence more broadly. Most recently we have focused on studies of nonverbal and verbal synchronization using the free-energy principle, the effect of team structure and incentives on problem-solving performance, productive collaborations between scientists and AI, and progressive performance of online teams via virtual meeting room environments and virtual AI agents.
Lab Members: Zach Fulker, V Lange, Sam Westby, Kristen Flaherty
Key Publications:
- “Competition and Collective Intelligence: The Moderating Effect of Sex Composition” (with Anita Woolley, Rosalind Chow, Anna Mayo, Jin Wook Chang). Working paper.
- “Quantifying Collective Intelligence in Human Groups” (with Young Ji Kim, Pranav Gupta, Thomas W. Malone and Anita Woolley). Working paper.
- Riedl, C., & Woolley, A. (2020). Successful remote teams communicate in bursts. Harvard Business Review, article.
- Balietti, S., Klein, B., & Riedl, C. (2020). Optimal design of experiments to identify latent behavioral types. Experimental Economics, in press. Preprint at arXiv, replication data.
- Riedl, C., Woolley, A. (2017). Teams vs. Crowds: A Field Test of the Relative Contribution of Incentives, Member Ability, and Collaboration to Crowd-Based Problem Solving Performance. Academy of Management Discoveries, 3(4), 382-403, article, open access PDF, code.
Games on Dynamic Networks
We use agent-based modeling and human subject experiments to better understand the evolution of cooperation, conflict, and spite. We specifically focus on the area of dynamic networks: how do behavioral strategies change when individuals can choose their interaction partner. In one stream of research, we investigate how preferential interaction can alter the spread of behaviors through a coevolution of network and strategy. We often rely on reinforcement learning as a biologically plausible mechanism to model how individuals may update who they want to interact with. That way, individuals can endogenously learn to visit the other individuals that produce the most favorable outcomes. This simple and realistic dynamic network has produced several interesting insights that can not be introduced in traditional random-mixing models. Our current work in this domain has extended to include the effect of incentives and dynamic group structures on contest strategy.
Lab Members: Zach Fulker, Nunzio Lore, Michael Foley
Key Publications:
We use agent-based modeling and human subject experiments to better understand the evolution of cooperation, conflict, and spite. We specifically focus on the area of dynamic networks: how do behavioral strategies change when individuals can choose their interaction partner. In one stream of research, we investigate how preferential interaction can alter the spread of behaviors through a coevolution of network and strategy. We often rely on reinforcement learning as a biologically plausible mechanism to model how individuals may update who they want to interact with. That way, individuals can endogenously learn to visit the other individuals that produce the most favorable outcomes. This simple and realistic dynamic network has produced several interesting insights that can not be introduced in traditional random-mixing models. Our current work in this domain has extended to include the effect of incentives and dynamic group structures on contest strategy.
Lab Members: Zach Fulker, Nunzio Lore, Michael Foley
Key Publications:
- Fulker, Z., Forber, P., Smead, R., Riedl, C. (2021). Spite is Contagious in Dynamic Networks. Nature Communications, 12(260), replication data, article, open access PDF.
- “Avoiding the Bullies: Resilience of Cooperation among Unequals” (with Mike Foley, Rory Smead and Patrick Forber). Working paper.
- Foley, M., Forber, P., Smead, R., Riedl, C. (2018). Conflict and Convention in Dynamic Networks. Journal of the Royal Society Interface, 15(140), 20170835, article, open access PDF. Companion website with interactive visualization to provide explorable explanations and replication code.
Social Influence in Networks and Organizations
We investigate how social interactions and personal biases influence the spread of ideas and knowledge in networks. Our work includes the use of large-scale field experiments and field data to investigate questions around social influence, social learning, and diffusion of innovation. We have also undertaken large scale data analysis to better understand the relationship between network position, reputation, and success with a specific focus on the world of art.
Lab Members: Christoph Riedl
Key Publications:
We investigate how social interactions and personal biases influence the spread of ideas and knowledge in networks. Our work includes the use of large-scale field experiments and field data to investigate questions around social influence, social learning, and diffusion of innovation. We have also undertaken large scale data analysis to better understand the relationship between network position, reputation, and success with a specific focus on the world of art.
Lab Members: Christoph Riedl
Key Publications:
- “Complex Contagions as a Seeding Strategy in Viral Marketing” (with Jaemin Lee and David Lazer). Working paper.
- Riedl, C., Bjelland, J., Canright, G,. Iqbal, I. Engø-Monsen, K., Qureshi, T. Sundsøy, P.R., Lazer, D. (2018). Product Diffusion Through On-Demand Information-Seeking Behavior. Journal of the Royal Society Interface, 15(139) 20170751, article, open access PDF, replication code and data.
- Fraiberger, S., Sinatra, R., Resch, M., Riedl, C., Barabási, A.L. (2018). Quantifying Reputation and Success in Art. Science, 362(6416), 825-829, article, open access PDF, replication data and code.
- 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, article, open access PDF.
Crowds in Organizations
We seek to better understand the behaviors of crowds, or decentralized groups of individuals working to solve a problem. Through the use of data collected from real crowdsourced organizational tasks, we are working to understand the behaviors of crowd participants, such as sabotage and rivalry, to better elaborate the strength and weaknesses of this approach to problem solving. This knowledge is used to help organizations better understand and leverage the potential benefits of crowdsourcing.
Lab Members: Christoph Riedl, Zach Fulker
Key Publications:
We seek to better understand the behaviors of crowds, or decentralized groups of individuals working to solve a problem. Through the use of data collected from real crowdsourced organizational tasks, we are working to understand the behaviors of crowd participants, such as sabotage and rivalry, to better elaborate the strength and weaknesses of this approach to problem solving. This knowledge is used to help organizations better understand and leverage the potential benefits of crowdsourcing.
Lab Members: Christoph Riedl, Zach Fulker
Key Publications:
- “Driven to Out-Code: Rivalry and Competition amongst Knowledge Workers” (with Tom Grad and Gavin Kilduff). Working paper.
- Riedl, C., Seidel, V., Woolley, A., Kane, G. (2020). Make Your Crowd Smart. Sloan Management Review, 61(4), Summer 2020, article, open access PDF.
- Riedl, C., Seidel, V. (2018). Learning from Mixed Signals in Online Innovation Communities. Organization Science, 29(6), 1010-1032, article, open access PDF.
- 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, article, open access PDF.
