Center for Collective Dynamics of Complex Systems at Binghamton University


CoCo is an interdisciplinary Organized Research Center (ORC) at Binghamton University that studies the collective dynamics of various types of interacting agents as complex systems. Its goals are to:
  • 1) Advance our understanding about the collective dynamics of physical, biological, social, and engineered complex systems through scientific research,
  • 2) Promote interdisciplinary collaboration among faculty and students in different schools and departments, and
  • 3) Translate the understanding to products and processes which will improve the well-being of people at regional, state, national and global scales
With the active participation of faculty members with diverse backgrounds, CoCo has been playing a key role in producing several new interdisciplinary research projects since 2007 as an informal research group. It officially became a designated ORC of the University on July 1st, 2015.
There is a mailing list run by CoCo for general discussions on complex systems. To subscribe, please contact Hiroki Sayama

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Complex systems are networks of many components with nonlinear interactions which arise and evolve through self-organization, such that the system is neither completely regular nor completely random, permitting the development of emergent behavior. These properties can be found in many real-world systems, e.g., gene regulatory networks in a cell, physiological systems, brains and other neural systems, food webs, stock markets, the Internet, and social networking systems. We study their structural/dynamical properties to obtain general, cross-disciplinary implications and applications.


Social Dynamics: The utilization and extension of agent-based modeling, evolutionary theory, game theory, and network theory to model, analyze and improve the behaviors of social systems.Current research topics include agent-based modeling of socio-economical dynamics of the Greater Binghamton area; modeling leadership, team performance and organizational decision making; and evolution of cooperative/competitive strategies in social systems.

Network Dynamics: The utilization and extension of complex network theory to explore the connectivity between elements, growth and self-organization, and dynamical evolution of various complex networks. Current research topics include modeling power grids as multiplex networks; distributed control mechanisms for adaptive power grids; and application of network analysis to psychological data.

Swarm Dynamics:The investigation of collective behavior and pattern formation in massive populations of biological or biomimetic autonomous agents. Current research topics include theoretical investigation of morphogenetic collective systems; design and evaluation of hierarchical heterogeneous particle swarm optimization; and automated modeling of termite behaviors.

   ◦ Robustness and adaptation in morphogenetic collective systems
   ◦ Complexity measures and concept learning
   ◦ Evolutionary perspective on collective decision making
   ◦ Teaching social complexity and multidisciplinary team building
   ◦ Linking youth and community through Information Technology
   ◦ Network analysis of real-world multi-dimensional social relationships
   ◦ Modeling and predicting state-topology coevolution of complex adaptive networks
   ◦ Self-organization of large-scale heterogeneous self-propelled particle swarms

Dr. John Bay

Watson School Dean's Office, Binghamton University
An Epidemiological Model for Control of Complex Systems via Information-Sharing: Opportunities for Research
(January 25, 2017)


Dr. Mohammad Abdullah Al-Mamun

Cornell University
Modeling Infectious Diseases: A Multidisciplinary Approach
(February 8, 2017)

X. Chen, J. Guo, J. W. Park and M. Ruiz Blondet

CCPA/SoM/Psychology, Binghamton University
The Development and Maintenance of Social Networks in Classroom Settings
(February 22, 2017)


Dr. Hui Yang

Pennsylvania State University
Mining Dynamic Recurrences in Nonlinear and Nonstationary Systems for Feature Extraction, Process Monitoring and Fault Diagnosis
(March 8, 2017, 11:00am-12:00pm)

Dr. Changqing Cheng

SSIE, Binghamton University
Graph Theoretic Approach for Gait Characterization towards Detection of Dementia
(March 22, 2017)


Dr. Daehan Won

SSIE, Binghamton University
(March 29, 2017)

Haifeng Wang

ISE, Binghamton University
An Ensemble Machine Learning Approach for Robust Cancer Diagnosis
(April 5, 2017)


Dr. Gourab Ghoshal

University of Rochester
Urban Socioeconomic Patterns Revealed through Morphology of Travel Routes
(April 19, 2017, 11:00am-12:00pm)