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Blog main article:
Decisions under uncertainty
Why classic decision-making logics systematically fail - and how organizations make structurally better decisions
Executive Summary
Making decisions under uncertainty is one of the key challenges facing modern organizations. Investment decisions, project portfolios or strategic initiatives are regularly made even though key parameters are not fully known.
The prevailing assumption is:
More information, better models and more coordination lead to better decisions.
However, the reality shows a different picture:
Despite growing amounts of data, simulations and governance structures, decisions often remain suboptimal.
The reason is not primarily a lack of data or methods, but a structural misjudgement:
Uncertainty is treated as an information problem - even though it is a decision space problem.
1. The nature of uncertainty in organizations
Uncertainty always arises when future states cannot be clearly predicted. In practice, this applies to almost all relevant areas of decision-making:
- Investments in fixed assets (CAPEX)
- Strategic projects and transformations
- Product developments
- Infrastructure measures
- Portfolio decisions in private equity or real estate
A distinction can be made between three forms:
1.1 Risk (measurable uncertainty)
- Probabilities are known or can be estimated
- Example: default rates, historical returns
1.2 Uncertainty (not fully quantifiable)
- Probabilities can only be approximated
- Example: market development, demand
1.3 Ambiguity (structural uncertainty)
- Causalities are unclear
- Example: Disruptive technologies
Critical point:
Organizations often treat all three forms in the same way - usually via scenarios or simulations.
2. The classic way of dealing with uncertainty
In practice, three decision-making approaches dominate:
2.1 Scenario-based planning
- Best case / base case / worst case
- Goal: Understanding bandwidths
2.2 Monte Carlo simulations
- Thousands of random runs
- Goal: Probability distributions
2.3 Expert-based evaluation
- Scoring models, committee decisions
- Goal: plausibility and consensus
These approaches provide valuable insights - but they have a structural limit:
They analyze uncertainty - but do not make an optimal decision in the full decision space.
3. The real problem: the decision space
In every organization, there is not just one decision, but a combinatorial decision space.
For N projects, the following applies:
- Number of possible combinations = 2^N
Examples:
| Number of projects | Combinations |
|---|---|
| 10 | 1.024 |
| 20 | 1.048.576 |
| 30 | > 1 billion |
| 50 | > 1 quadrillion |
Consequence:
Complexity grows exponentially with each additional option. Even if uncertainty were perfectly modeled, the central question remains:
Which combination of decisions is optimal under all uncertainties?
Classical methods do not answer this question.
4. Why simulation does not replace a decision
Simulations answer the question:
"What happens if I choose a particular option?"
They do not answer:
"Which option is the best among all possible options?"
This is a fundamental difference.
Example: Real estate portfolio
A company examines 50 real estate projects.
- Simulation: evaluates individual projects or scenarios
- Reality: there are 2^50 possible combinations
Problem:
Simulation considers alternatives in isolation - not systematically across the entire space.
5. Behavioral biases under uncertainty
Uncertainty systematically reinforces cognitive biases:
Typical effects:
- Loss aversion: Risks are overestimated
- Status quo bias: Existing projects remain in the portfolio
- Overconfidence: Forecasts are overestimated
- Anchoring: Initial valuations dominate
Result:
Decisions are not only incomplete, but systematically distorted.
6. The structural misconception
The dominant assumption is:
More information → better decisions
In fact, the following applies:
More options + uncertainty → exponentially increasing decision complexity
This leads to three problems:
- Reduction of the decision space (pre-filtering)
- Simplified models
- Subjective weighting
Consequence:
The global optimum is generally never considered.
7. Rethinking decision-making under uncertainty
A robust decision logic must address two dimensions simultaneously:
7.1 Modeling uncertainty
- Probabilities
- Risks
- Scenarios
7.2 Fully analyze the decision space
- All combinations
- All restrictions
- All dependencies
Only the combination of both levels enables real optimization.
8. Comparison of the approaches
| Approach | Strength | Weakness | Result |
|---|---|---|---|
| Scenario analysis | Comprehensibility | no optimization | limited informative value |
| Monte Carlo | probabilistic depth | no decision making | Simulation instead of selection |
| Expert decision | Experience | Bias | inconsistent results |
| Heuristics | Speed | local optima | suboptimal |
| Combinatorial optimization | complete space | high computational demand | globally optimized decision |
9. Fixed assets as a critical use case
The problem is particularly evident when investing in fixed assets:
- High capital commitment
- Long maturities
- Low reversibility
Typical process:
- Projects are evaluated individually
- Budget is distributed
- Portfolio is created iteratively
Problem:
The complete investment list is rarely considered as a combinatorial decision space.
10. Ex-ante vs. ex-post logic
Most organizations optimize decisions ex-post:
- Ex-post evaluation
- Portfolio adjustments
- Lessons Learned
A superior logic is:
Ex-ante optimization - before the decision
This means:
- All options are considered simultaneously
- Restrictions are integrated
- Uncertainty is modeled
- The result is the optimal combination
11. Governance implications
The introduction of a structured decision-making logic fundamentally changes organizations:
Classical:
- Committees decide
- Discussion dominates
- Consensus is the goal
New:
- Models calculate optimal solutions
- Transparency about opportunity costs
- Decisions become verifiable
12. Opportunity costs under uncertainty
The largest, often invisible cost block is:
The difference between the chosen solution and the global optimum
This effect increases massively under uncertainty:
- incorrect prioritization
- inefficient allocation of capital
- long-term loss of value
13. Decision quality as a competitive factor
Organizations compete not only on products or markets, but increasingly on the quality of their decisions:
The quality of their decisions
This means
- better allocation of capital
- greater resilience
- faster adaptability
14. Practical implementation
A modern decision-making architecture includes
1. Complete project list before decision
No iterative selection
2. Clear restrictions
Budget, resources, dependencies
3. Modeling of uncertainty
Probabilities, scenarios
4. Algorithmic optimization
Searching the entire decision space
15. Conclusion
Decisions under uncertainty are not a pure forecasting problem.
They are a structural optimization problem.
The key insight is this:
Uncertainty cannot be eliminated - but decisions can be made optimally despite uncertainty.
Organizations that understand this difference gain a massive advantage:
- higher return on investment
- better strategic coherence
- reduced wrong decisions
FAQ
What does "decision under uncertainty" mean in concrete terms?
It means that decisions have to be made even though relevant information about the future is not fully known.
Why are simulations not enough?
Simulations show possible outcomes of individual decisions, but do not identify the best decision within all possible combinations.
What is the difference between risk and uncertainty?
Risk is measurable (with probabilities), uncertainty is not fully quantifiable.
Why is the decision space so important?
Because the number of possible combinations grows exponentially (2^N) and classical methods do not fully analyze this space.
What is the biggest problem of classical decision processes?
The systematic reduction of the decision space and the resulting suboptimal decisions.
What does "global optimum" mean?
The best possible combination of all decisions under given restrictions and uncertainties.
When does this become particularly relevant?
- CAPEX decisions
- Project portfolios
- Infrastructure investments
- Private equity
What is the key improvement of modern approaches?
The combination of uncertainty modeling and complete analysis of the decision space.
Dr. Igor Kadoshchuk is a computer scientist, algorithm architect, and one of the leading minds behind mAInthink's optimization and decision-making algorithms. As scientific director of the StratePlan™ and DeepAnT platforms, he combines in-depth mathematical research with practical applications in project portfolio optimization, business, finance, and public administration.
He holds a PhD in computer science from the renowned Moscow Institute of Physics and Technology (MIPT), where he also taught as a professor of computer engineering and mathematics. He has decades of experience developing highly complex mathematical models for project portfolio optimization and financial systems, investment planning, and strategic decision-making. His professional career includes leading positions such as Head of IT at Gazprombank and Director of Project Management at TransTeleCom.
Dr. Kadoshchuk writes on the mAInthink AI Blog. Kadoshchuk on:
- Algorithmic strategy optimization
- New methods for calculating ROI and impact
- Project portfolio optimization beyond traditional tools
- The limits of human decision-making – and how AI overcomes them
His aim: to calculate strategy, not estimate it.
His contributions combine scientific precision with clear, understandable language – always with the goal of making complex decision-making spaces transparent, manageable, and measurable.