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.



Christoph Riedl

assistant professor

Chris is Assistant Professor for Information Systems at the D’Amore McKim School of Business. He employs business analytics and data science to investigate research questions about group-decision making, network science, and social media, and develops novel computational approaches to study collective intelligence mechanisms.

Post-docs and Students

Jaemin Lee

postdoctoral Fellow

Jaemin Lee is a postdoctoral fellow at Northeastern University's Network Science Institute and D’Amore-McKim School of Business. As a social scientist who studies intergroup conflicts in social networks, Dr. Lee is interested in how the principle of homophily and social diffusion shape group polarization, antagonism, and sanctions. The crux of his research program entails two substantive contexts: political polarization and adolescent social networks. He uses computational simulations, survey data, and field experiments to identify the conditions under which interpersonal influence gives rise to the growing political divide in both social media and the general population in America. He also specializes in analyzing and modeling longitudinal network data to examine how social constraints such as gendered norms and racial clustering introduce complexities to segregation dynamics in school. Before coming to Northeastern, Lee completed his sociology PhD program at Duke University and received a BA and MA in sociology from Yonsei University.

Michael Foley

third year phd student

Michael's broad research interests lie in the overlap between complex systems and the social sciences. In particular, he is interested in how rational local decisions and interactions can produce unintended and emergent system behavior. Michael has a B.S. and M.S. in Mathematics from the University of Vermont, where he did research in computational finance and agent based modeling. Currently, he is working with Chris Riedl to research the effect of different communication networks on a group's ability to solve problems.

Brennan Klein

second year phd student

Brennan received his BA in Cognitive Science and Psychology from Swarthmore College in 2014. While attending Swarthmore, Brennan’s research revolved primarily around perception and cognition. He has presented at Vision Science Society’s Annual Conference (2012, 2013, 2014) is a co-author on several cognitive science papers. Before starting his PhD, Brennan spent a year in Silicon Valley trying to start a startup while working as a social network data analyst (June 2014 - May 2015).

Praveen Ningappa

masters student

Praveen received his undergraduate degree from India in the field of Electronics & Communication. Currently he is pursuing a
master’s degree at Northeastern University in Information Systems. His area of specialization is Front End Design and Development. Praveen's research interest is in developing an User Interface which provides a better user experience even for visually challenged users.

Jake Moody

undergraduate student

Jake is an undergraduate student at the D'Amore-McKim School of Business concentrating in Finance and Entrepreneurship. His research interests are in business analytics and digital marketing.

Christina Sirabella

undergraduate student

Christina is an undergraduate student in the College of Arts, Media, and Design studying Communications with minors in Women's Studies and Sociology. She is interested in exploring the social and cultural impact of digital networks and technologies.

Tina Lee

undergraduate student

Tina is an undergraduate student at the College of Computer and Information Science majoring in computer science. Her research interests are in human-computer interaction and artificial intelligence.


Social network processes in collaborative decision-making

Principal Investigator: Chris Riedl
Funding source: ARO

Teams and collaborations are increasingly important as a means of harnessing diverse and complementary skills. This project studies how team can be organized efficiently and how emergent properties and processes within a team affect team performance. This project makes important contributions to our fundamental understanding of collaborative decision­-making and the organization of work in general.

How do we best organize agents to solve difficult problems? Should they compete or collaborate? If they collaborate, who should collaborate with whom? While organizing problem­-solving work as a competition can provide strong incentives to exert high levels of effort and allows exploring multiple solutions in parallel, collaboration can allow learning from others and leverage synergies. The nature of the problem to be solved, information sharing structures among agents attempting to solve the problem, and characteristics and knowledge of agents can all affect which mode of organizing agents performs best.

This project blends theories and methods from the social sciences with computational methods from computer science and mathematical modeling. Specifically, we study the tradeoffs between competition and collaboration through experiments and agent-­based modelling. We implement a series of experiments and simulations in which agents/individuals attempt to self­-organize and solve complex tasks. Agents/individuals are exposed to a variety of treatments modifying (a) the amount and type of information shared, (b) the structure of the underlying communication network (which agent can communicate with which other agents; i.e., network topology), and (c) the heterogeneity of agents. This research identifies social network processes within and across groups of competing and collaborating individuals and determines their impact on practices and performance of individuals, teams, and organizations. This research makes important contributions to the literature on collaborative work and collective decision-­making. This research supports the changing nature of work by providing insights into individual and organizational factors that drive collective decision­-making and collaborative networks applied to solve complex and dynamic problems. This research contributes to a better understanding of the changing nature of work in which large groups of agents operate as self­-organized systems.

Understanding online creative collaboration over multidimensional networks

Principal Investigator: Chris Riedl
Funding source: NSF

The massive amounts of available digital trace data enable studies of population-level human interaction on an unprecedented scale. This project offers groundbreaking insights into how multidimensional network configurations shape the success of value-creation processes within crowdsourcing systems and online communities. Furthermore, this project offers new computational social science approaches to theorizing and researching the roles of social structure and influence within technology-mediated value creation processes.

This is a study of the structure and dynamics of Internet-based collaboration.  The project seeks groundbreaking insights into how multidimensional network configurations shape the success of value-creation processes within crowdsourcing systems and online communities.  The research also offers new computational social science approaches to theorizing and researching the roles of social structure and influence within technology-mediated communication and cooperation processes.  The findings will inform decisions of leaders interested in optimizing all forms of collaboration in fields such as open-source software development, academic projects, and business.  System designers will be able to identify interpersonal dynamics and develop new features for opinion aggregation and effective collaboration.  In addition, the research will inform managers on how best to use crowdsourcing solutions to support innovation and marketing strategies including peer-to-peer marketing to translate activity within online communities into sales.

This research will analyze digital trace data that enable studies of population-level human interaction on an unprecedented scale. Understanding such interaction is crucial for anticipating impacts in our social, economic, and political lives as well as for system design. One site of such interaction is crowdsourcing systems - socio-technical systems through which online communities comprised of diverse and distributed individuals dynamically coordinate work and relationships.  Many crowdsourcing systems not only generate creative content but also contain a rich community of collaboration and evaluation in which creators and adopters of creative content interact
among themselves and with artifacts through overlapping relationships such as affiliation, communication, affinity, and purchasing.  These relationships constitute multidimensional networks and create structures at multiple levels.  Empirical studies have yet to examine how multidimensional networks in crowdsourcing enable effective large-scale collaboration.

The data derive from two distinctly different sources, thus providing opportunities for comparison across a range of online creation-oriented communities.  One is a crowdsourcing platform and ecommerce website for creative garment design, and the other is a platform for participants to create innovative designs based on scrap materials.  This project will analyze both online community activity and offline purchasing behavior. The data provide a unique opportunity to understand overlapping structures of social interaction driving peer influence and opinion formation as well as the offline economic consequences of this online activity.  This study contributes to the literature by (1) analyzing multidimensional network structures of interpersonal and socio-technical interactions within these socio-technical systems, (2) modeling how success feeds back into value-creation processes and facilitates learning, and (3) developing methods to predict the economic success of creative products generated in these contexts.  The application and integration of various computational and statistical approaches will provide significant dividends to the broader scientific research community by contributing to the development of technical resources that can be extended to other forms of data-intensive inquiry.  This includes documentation about best practices for integrating methods for classification and prediction; courses to train students to perform large-scale data analysis; and developing new theoretical approaches for understanding the multidimensional foundations of cyber-human systems.