Research & Decision Science
Where forecasting research becomes operational decision infrastructure
RabbitHawk is built on peer-reviewed work in probabilistic forecasting, hierarchical reconciliation, optimization and applied AI, translated into systems that improve real decisions under real constraints.
- publications
- 1,181publications
- citations
- 53,485citations
- highly cited papers
- Top 1%highly cited papers
Scientific foundations
Methods proven in the literature, applied in production
Our team’s research is recognized at the field’s leading venues and used at scale by major organizations. We bring that rigor to enterprise planning, but judge every method by whether it improves decisions in practical settings.
Research → product
| Research area | Enterprise problem | RabbitHawk capability |
|---|---|---|
| Hierarchical forecasting | Local plans do not add up to executive targets | Reconciled forecasts across every level |
| Probabilistic forecasting | Point forecasts hide risk | Full predictive distributions, intervals and scenario ranges |
| Optimization | Forecasts do not tell teams what to do | Constraint-aware recommendations |
| Agentic AI | Context is trapped in emails and notes | Context ingestion and decision agents |
| Human-in-the-loop AI | Automation without trust creates resistance | Approval, override and audit trails |
| Alignment science | Teams optimize against conflicting goals | 90North alignment and goal clarity |
Hierarchical forecasting
Local plans do not add up to executive targets
Reconciled forecasts across every level
Probabilistic forecasting
Point forecasts hide risk
Full predictive distributions, intervals and scenario ranges
Optimization
Forecasts do not tell teams what to do
Constraint-aware recommendations
Agentic AI
Context is trapped in emails and notes
Context ingestion and decision agents
Human-in-the-loop AI
Automation without trust creates resistance
Approval, override and audit trails
Alignment science
Teams optimize against conflicting goals
90North alignment and goal clarity
Selected publications
The full peer-reviewed database: search by title, author, venue, category or year.
1,181 results · page 58 of 99
2012
Evaluation and Improvement of Laruelle-Widgr\'en Inverse Banzhaf Approximation
F de Nijs, D Wilmer
arXiv preprint arXiv:1206.1145, 2012
Research12 citationsMaximising the net present value of large resource-constrained projects
H Gu, PJ Stuckey, MG Wallace
International Conference on Principles and Practice of Constraint …, 2012
Machine Learning10 citationsInter-instance nogood learning in constraint programming
G Chu, PJ Stuckey
International Conference on Principles and Practice of Constraint …, 2012
Machine Learning9 citationsOptimisation modelling for software developers
K Francis, S Brand, PJ Stuckey
International Conference on Principles and Practice of Constraint …, 2012
Research9 citationsOrthogonal hyperedge routing
M Wybrow, K Marriott, PJ Stuckey
International Conference on Theory and Application of Diagrams, 51-64, 2012
Research8 citationsEmpirical evaluation of multi-agent routing approaches
A ter Mors, C Witteveen, C Ipema, F de Nijs, T Tsiourakis
Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on …, 2012
Multi-Agent Systems7 citationsOptimal guillotine layout
G Gange, K Marriott, P Stuckey
Proceedings of the 2012 ACM symposium on Document engineering, 13-22, 2012
Research7 citationsTheory of dependent hierarchical normalized random measures
C Chen, W Buntine, N Ding
arXiv preprint arXiv:1205.4159, 2012
Research4 citationsAn introduction to search combinators
T Schrijvers, G Tack, P Wuille, H Samulowitz, PJ Stuckey
International Symposium on Logic-Based Program Synthesis and Transformation …, 2012
Optimization3 citations- Research2 citations
2012 Index IEEE Transactions on Neural Networks and Learning Systems Vol. 23
SP Adhikari, A Alessandri, B Alfano, AM Alimi, E Alonso, U Amato, ...
IEEE Transactions on Neural Networks and Learning Systems 23 (12), 2013, 2012
Machine LearningOptimization of neuro-coefficient smooth transition autoregressive models using differential evolution
C Bergmeir, I Triguero, F Velasco, JM Benítez
International Conference on Hybrid Artificial Intelligence Systems, 464-473, 2012
Optimization
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