Christoph Riedl is the Joseph G. Riesman assistant professor for Information Systems and Network Science at the D’Amore-McKim School of Business at Northeastern University. He hold a joint appointment with the College of Computer & Information Science and is a core faculty member at the Network Science Institute. He is a fellow at the Institute for Quantitative Social Science (IQSS) at Harvard. He is recipient of a Young Investigator Award (YIP) from the Army Research Office (ARO) for his work on social networks in collaborative decision-making. Before joining Northeastern University he was a post-doctoral fellow at Harvard Business School and IQSS. He received a PhD in Information Systems from Technische Universität München (TUM), Germany in 2011, a MSc in Information Systems in 2007, and a BSc in Computer Science in 2006. His work has been funded by NSF and published in leading journals including Organization Science, Management Science, Information Systems Research, Academy of Management Discoveries, and the Journal of the Royal Society Interface.

His research interests are to understand how social and economic networks shape collaboration and decision-making on the individual, group, and community level. He is known for his scholarship on how crowdsourcing and “wisdom of the crowd” mechanisms can be designed and managed to achieve innovative outcomes. His work has focused on the open problem of how to design and manage teams, and to harness the wisdom of the crowd and advance the undestanding of how social influence and information diffusion in networks shape outcomes on a global scale.

He uses computational social science methods including large-scale (digital) experiments, computer simulation (agent-based models), digital trace data, and ideas from behavioral economics and game theory.

Christoph Riedl teaches courses on Business Analytics, Network Economics, and Information Systems.

About the Lab


The Collaborative Social Systems Lab, directed by Christoph Riedl, explores collaboration in distributed environments: how can individuals solve challenging global tasks in social networks from only local, distributed interactions? We use agent-based modeling, conduct lab and field experiments, and analyze large datasets to study how networked interactions influences human behavior, strategies, and success.

Latest Publications


Learning from Mixed Signals in Online Innovation Communities

Organization Science, in press.