A key problem for cognitive science and artificial life alike is how information, symbols, representations and the like can arise from a purely dynamical system of many components. Also interesting for computational social science is how information technology enables the collective organization of societies. We have contributed to the study of such problems with both theoretical and applied projects. On the theoretical side, we have developed the concepts of selected self-organization, biosemiotics, and material representations. On the applied front, we are interested in using design principles from nature, particularly from biological systems dealing with information and memory, to improve information technology and to study collective intelligence. Our projects range from the development of recommender systems to the study of social conflict and political polarization.
We have developed adaptive recommendation systems for digital libraries, movie databases, and biomedical concepts. These methods are based on complex networks, information theory, models of cognitive categorization, and Hebbian learning.More
With the availability of large-scale data about many domains ranging from molecular biology to social interactions, Network Science has made a substantial leap in providing predictive models of many complex social problems. Using social media data (Instagram, Twitter, and ChaCha), we are studying collective social behavior in health-related problems (human reproductive behavior, drug interaction), social unrest situations, political science, etc.More
We have worked in various mathematical models of uncertainty such as Fuzzy Set Theory , the Dempster-Shafer Theory of Evidence (DST), and information dynamics. We have applied these methodologies to cognitive science, machine learning, complex systems, and neuroscience.More