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 96 of 99
1991
Introduction in IND and recursive partitioning
W Buntine, R Caruana
Research75 citationsDesign overview of the Aditi deductive database system
J Vaghani, K Ramamohanarao, DB Kemp, Z Somogyi, PJ Stuckey
Proceedings. Seventh International Conference on Data Engineering, 240,241 …, 1991
Research70 citations- Research70 citations
Incremental linear constraint solving and detection of implicit equalities
PJ Stuckey
ORSA Journal on Computing 3 (4), 269-274, 1991
Machine Learning34 citations- Research13 citations
An introduction to Aditi deductive database system
J Vaghani, K Ramamohanarao, DB Kemp, Z Somogyi, PJ Stuckey
Australian Computer Journal 23 (2), 37-52, 1991
Research9 citationsCollected Notes on the Workshop for Pattern Discovery in Large Databases
W Buntine, M Delalto
Research6 citationsThe CLP (R) language and system: an overview
J Jaffar, S Michaylov, PJ Stuckey, RHC Yap
Digest of Papers-Compcon: IEEE Computer Society International Conference, 376, 1991
Research5 citations- Research5 citations
Modelling default and likelihood reasoning as probabilistic reasoning
W Buntine
Annals of Mathematics and Artificial Intelligence 4 (1), 25-68, 1991
Bayesian Methods3 citationsJA THOM, AJ KENT, R SACKS-DAVIS
J VAGHANI, DBK KRAMAMOHANARAO, Z SOMOGYI, PJ STUCKEY
ResearchIntroduction to IND and recursive partitioning, version 1.0
W Buntine, R Caruana
Research
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