Overarching Challenges

There are many challenges in developing and modeling data-driven systems, regardless of domain or discipline. IDSS research is unified in its efforts to address these challenges.

Resilience and Systemic Risk
Interconnections within a networked system can propagate shocks, amplifying their effects and making the system more prone to disruption or failure. Systemic risk arises when shocks to one part of a system threaten to create, or cause, cascading failures. IDSS aims to build a foundational science that measures and minimizes these types of risk, resulting in more resilient systems.

System Design and Architecture
Good architecture is easy to recognize in retrospect, but harder to predict or design. As we transform the nation’s power grid, develop smart and autonomous transportation systems, and enable real-time data exchange in financial markets, secure network architecture is essential. IDSS incorporates foundational theory, practical algorithms, and concrete applications to develop a framework for robust and efficient system design.

Sustainability and Policy
Improving societal well-being across the domains of ecology, economics, politics, and culture requires systematic evaluations of public and scientific innovations. Through quantitative analysis and design, IDSS researchers are working to derive models that account for society’s complexities, an effort that is critical to well-informed public policy.

Identifying models for decision-making is a hallmark of decision theory. However, the magnitude, diversity, and structure of modern data sets creates new challenges around how to use them. IDSS research addresses issues of technical management, security, privacy, and data integrity, as new tools are developed to explore data sets and models and to make better decisions.

© MIT Institute for Data, Systems, and Society | 77 Massachusetts Avenue | Cambridge, MA 02139-4307 | 617-253-1764 | Design by Opus