Why StratePlan
Investment decisions rarely fail because of individual projects - but because of the combination of these projects.
The decision space grows exponentially with each additional investment. Traditional methods inevitably reduce this complexity - and make decisions based on incomplete considerations.
A structural problem - scientifically proven
The challenge of combinatorial decision spaces has been the subject of intensive research in mathematics and computer science for decades. Leading institutions are unanimous in their findings: The complete solution space of complex decision problems cannot be fully analyzed using classical methods.
- Exponential growth of decision spaces
- Limitations of heuristic and sequential methods
- Systematic emergence of local rather than global optima
The gap between theory and practice
While research describes this problem precisely, there is no scalable method in practice, to fully analyze real investment portfolios.
Decisions therefore continue to be:
- made on a project-by-project basis instead of systemically
- prioritized on the basis of simplified models
- implemented without full transparency of opportunity costs
The StratePlan approach
StratePlan transfers the theoretical principles of combinatorial optimization into a practical decision-making logic for the first time.
Instead of evaluating individual projects, StratePlan analyzes the entire decision space under real restrictions such as budget, capacity, risk and strategic requirements - and identifies the optimal portfolio structure.
The result: A comprehensible, mathematically sound decision-making basis for complex investment decisions.
What changes as a result
- From isolated valuation → to systemic portfolio optimization
- From approximation → to calculated optimality
- From implicit → to transparent opportunity costs
From understanding to application
Learn how StratePlan calculates the complete decision space in practice.
View StratePlan in detailScientific context
The relationships presented are based on research in the field of combinatorial optimization, including
- Max Planck Society - Combinatorial Optimization
- RWTH Aachen - Combinatorial Optimization
- University of Osnabrück - AG Combinatorial Optimization
- University of Cologne - Publications Combinatorial Optimization
- MIT-Massachusetts Institute of Technology - Cambridge (USA) - Combinatorial Optimization
- Simons Institute - Berkeley University San Francisco (USA)- Machine Learning Combinatorial Algorithms
- University of OXFORD (UK) Combinatorial Optimization
The technological basis: Hybrid AI for complex decision spaces
StratePlan is based on a hybrid AI approach that integrates mathematical optimization, decision science modeling and scalable computing architectures. The aim is to formally model complex investment decisions and systematically optimize them on this basis.
Combinatorial optimization
Mathematical core for modeling and analyzing the complete decision space of possible project combinations under real restrictions such as budget, capacity and dependencies.
Behavioral Economics (modeling level)
Structured consideration of cognitive biases and real decision logics by transferring them into model-relevant parameters such as weightings, risk assumptions and prioritizations.
Parallel computing (computational level)
Scalable analysis of exponentially growing solution spaces through highly parallel processing and efficient screening of complex decision spaces.
The architecture is based on established scientific findings from combinatorial optimization and algorithmic decision research. The transfer of these approaches into a scalable, practically applicable system architecture was carried out under the direction of Dr. Igor Kadoshchuk.
The result is a decision logic that models real decision structures and at the same time goes beyond heuristic and sequential procedures. Based on the fully defined solution space, complex investment portfolios can be systematically analyzed and value-maximizing combinations can be identified.
