Social Computing and Computational Social Science

Social computing refers to human interactions via and with computing technologies, where humans actively provide inputs to influence computations and where computational outcomes shape individual lives and social groups. Computational social science is concerned with taking computational approaches to social sciences, particularly using computational methods to model, simulate, and analyse social phenomena. Through user studies, analysis of large datasets, and design and deployment of new systems, these emerging fields seek to understand and influence the behavior of these systems and their users.

Groups and Researchers in this Field

Human-Centric Machine Learning

Manuel Gomez Rodriguez is interested in developing machine learning and large-scale data mining methods for analysis and modeling of large real-world networks and processes that take place over them. His research comprises several dimensions: developing models of these networks and processes, assessing their theoretical properties and limitations; developing machine learning algorithms to fit the models and computational methods to influence processes over networks; and validating models and methods on gigabite- and terabyte-scale real-world datasets. Ultimately, he aims to provide computational tools with applications in a variety of domains, e.g. social and information sciences, economics, decision theory, causality, and epidemiology. Read more

Manuel Gomez Rodriguez

MPI-SWS, Faculty
Personal Website

Social Computing

Krishna Gummadi heads the Social Computing research group at the Max Planck Institute for Software Systems. He is broadly interested in understanding and building networked and distributed computer systems. Currently, the group's research focuses on social computing systems: an emerging class of societal-scale human-computer systems that facilitate interactions and knowledge exchange between individuals, organizations, and governments in our society. A few examples include social networking sites like Facebook, blogging and microblogging sites like LiveJournal and Twitter, and content sharing sites like YouTube, among many others. Through user studies, examining data, and building systems, the group aims to understand, predict, and control the behavior of their constituent human users and computer systems. Read more

Krishna Gummadi

MPI-SWS, Faculty
Personal Website

Social Foundations of Computation

Moritz Hardt is a scientific director at the Max Planck Institute for Intelligent Systems, where he leads the Social Foundations of Computation Department. His research contributes to the scientific foundations of machine learning and algorithmic decision making with a focus on social questions. His research interests span four areas: (1) Applying machine learning in social and economic contexts, (2) formulating social and dynamic actions as mathematical models, (3) examining the validity and reliability of statistical methods and the construction of datasets within scientific communities, and (4) the pursuit of normative goals, and in particular, how to formulate values and norms mathematically. Hardt is co-founder of the conference "Fairness, Accountability, and Transparency in Machine Learning." He is co-author of "Fairness and Machine Learning: Limitations and Opportunities" (MIT Press, 2022) and "Patterns, Predictions, and Actions: A Story About Machine Learning" (Princeton University Press, 2022). Read more

Moritz Hardt

MPI-IS, Scientific Director
Personal Website

Multi-Agent Systems

Goran Radanović is a research group leader at the Max Planck Institute for Software Systems. He is generally interested in studying AI systems, and more specifically in the design and analysis of systems with intelligent and self-interested agents. Particular topics of his research interest include value-aligned artificial intelligence, human-AI collaboration, and decision making systems with societally-aware utility functions. His research utilizes tools from game theory (esp. mechanism design), machine learning (esp. reinforcement learning), and human-centered AI (esp. crowdsourcing). His work covers both theoretical and practical aspects of problem instances related to his research topics. Read more

Goran Radanovic

MPI-SWS, Research Group Leader
Personal Website

Humans & Machines

Iyad Rahwan is a scientific director at the Max Planck Institute for Human Development, where he leads the Center for Humans & Machines. He is also an honorary professor of Electrical Engineering and Computer Science at the Technical University of Berlin. Previously, he was an Associate Professor of Media Arts & Sciences at the Massachusetts Institute of Technology (MIT). Rahwan's work lies at the intersection of computer science and human behavior, with a focus on the impact of Artificial Intelligence on society. His work appeared in major academic journals, including Science and Nature. Read more

Iyad Rahwan

MPI for Human Development, Scientific Director
Personal Website

Machine Teaching

Adish Singla is a faculty member at the Max Planck Institute for Software Systems. He is interested in the design of AI-ML methods that interact with, learn from, and teach other learning entities such as humans, robots, and machines. His research interests span various application domains, including the design of intelligent tutoring systems for personalized education, social robotics, and adversarial machine learning. The theoretical aspects of his work include machine learning (esp. online, active), AI (esp. probabilistic modeling), and optimization (esp. submodular). The focus is towards designing principled techniques that are both theoretically well-founded with strong provable guarantees and are practically applicable. Read more