Anti-fragile Decision-making: tHink beYonD Robustness And resilience (HYDRA) (HYDRA)

Main funder

Funder's project number360133

Funds granted by main funder (€)

  • 654 491,00

Funding program

Project timetable

Project start date01/09/2024

Project end date31/08/2028


The ambitious goal of HYDRA is to support decision-makers in being well-prepared for the continuously changing future and making sustainable and anti-fragile decisions to have a better world. After two decades in the twenty-first century, humanity still struggles with various crises. The Russian invasion of Ukraine and recent energy crises, pandemics, oil and Middle East crises, economic crises, and climate change highlight vulnerable and fragile decisions made based on unreliable predictions, ignoring plausible but less probable scenarios, and careless consideration of the consequences of the decisions. Although robust decision-making, sustainability, and resilient plans have become hot topics nowadays, recent worldwide crises accentuate their fragilities. Therefore, it is essential for a paradigm shift in decision-making and investigation of novel patterns to handle various sources of uncertainty.

In HYDRA, we renew the perspective of decision-making under uncertainty to think beyond robustness and resilience and look for opportunities to benefit from harm (e.g., producing face masks in the recent pandemic). We conceptualize antifragility in the body of multi-criteria decision-making and design a comprehensive modular architecture for decision support tools and anti-fragile decision-making. This architecture has various imperative features: 1) Does not rely only on history-based predictions and augments them with expert judgments and indeterminism; 2) Provides a systematic framework and guidelines for generating a representative set of scenarios, including surprise events; 3) Provides support for multiple objectives and trade-off comparisons between objectives in different scenarios; 4) Provides a learning option for decision-makers to realize the limitations of the problem and their preferences, parameter characteristics and interdependencies, available alternatives and their outcomes and consequences in different scenarios through an interactive solution process; 5) Instead of static decisions, looks for a chain of dynamic adaptive decisions for each plausible scenario (policy pathways) to avoid fragility and continuous performance improvement in a long-term planning horizon; 6) Provides support for identifying the optimal timing for implementing the scenario-relevant contingency plans; 7) Verified and inspired with practical applications and openly available for academic and practical use.

Principal Investigator

Primary responsible unit

Follow-up groups

Profiling areaDecision analytics utilizing causal models and multiobjective optimization (University of Jyväskylä JYU) DEMO; 2017-2021

Last updated on 2024-25-06 at 11:03