Research on Resilience & Sustainability

Our systems science approach considers social, cultural, ecological, and technical aspects as closely interlinked components—and reveals how changes in one area influence the entire system. This enables us to lay the foundation for strengthening decision-making sovereignty in a targeted manner: with strategies that are robust, measurable, adaptable, and proven in practice.

Our expertise is based on four strategic pillars:

Knowledge fusion & decision support

We bring together data from a wide variety of sources and scales, structure it using AI-supported knowledge graphs, ontologies, and systems science models, and thus make complex system states navigable. This makes it possible to identify correlations and conflicting goals at an early stage and to justify decisions – for example, in climate adaptation or infrastructure planning. Looking ahead, we are building semantically interoperable knowledge spaces, linking monitoring data to living knowledge graphs, and developing decision support systems that explicitly address uncertainties as well as conflicting goals and automatically identify adaptive control points.
 

Multidimensional quantification & holistic analysis

We combine mixed methods, indicator-based assessments, and life cycle assessments (LCA) to make social, cultural, ecological, and technical aspects measurable and comparable. We use impact chains to structure causes and effects, capture cascading effects and compound hazards, and derive robust metrics from them—for example, for neighborhoods, cultural landscapes, or value chains. We are continuously refining these methods, for instance by deepening impact chains for complex, interconnected situations, linking them to knowledge graphs, and establishing dynamic, context-sensitive indicator sets as well as practical scorecards and standards.
 

Integrated risk and resilience management

We combine traditional risk management with proactive resilience building and manage this process using dynamic adaptive policy pathways. Quantified system parameters help us identify switching points as well as guardrails and secure room for maneuver. We place particular emphasis on identifying and avoiding maladaptation at an early stage: We systematically examine side effects, distributional impacts, as well as lock-in risks, and design adaptive policy corridors that jointly strengthen resilience and sustainability—supplemented by scenario ensembles as well as stress and robustness tests.
 

Co-creative validation & strategy design

We bring practical knowledge directly into the model: In co-creation formats, simulation exercises, and hybrid decision-making environments, we work with local stakeholders to test how strategies function under real-world conditions. In this way, we enhance practical relevance, ownership, and readiness for implementation—for example, in cultural landscapes, municipal planning, or crisis and emergency training. In the future, we will place even greater emphasis on making conflicts of interest and use between public and private actors visible and negotiable, and on resolving them in a targeted manner using conflict mapping, facilitated processes, and robust evidence chains. Accompanying evaluations ensure the transfer into routine practice.

We explicitly map the system dynamics relevant to decision-making—no more and no less. Systems science and AI serve to highlight conflicting goals and design viable paths that combine technological efficiency with social values, economic viability, and ecological limits. People are always at the center of our work: our models and tools are designed to provide targeted support to decision-makers – enabling them to make transparent, responsible, and sustainable decisions.