Heuristics vs. optimisation
Why rules of thumb systematically lead to suboptimal results in complex investment portfolios
In many companies, investment decisions are not made on the basis of a complete, formally evaluated decision space.
They are made on the basis of simplification.
Rules of thumb, standard metrics and proven routines reduce complexity and speed up decisions.
But it is precisely these heuristics that are often the reason why capital does not flow into the best combination of projects in modern CapEx and portfolio contexts.
As part of our analysis of decision quality, we have identified "heuristics vs. optimisation" as a central mechanism that structurally explains suboptimal investment decisions.
Definition
Heuristics are simplifying decision rules that provide quick answers without calculating the entire decision space.
Optimisation means systematically evaluating a formally defined decision space under explicit constraints to determine the best combination.
Heuristics are not fundamentally wrong.
However, they are limited where complexity, dependencies and budget constraints dominate.
Why companies use heuristics
Heuristics arise for understandable reasons :
- Time pressure : Decisions must be made quickly.
- Complexity reduction : Many projects cannot be evaluated simultaneously.
- Process standardisation : Uniform rules create governance and comparability.
- Psychological security : A rule of thumb feels more stable than a complete reassessment.
The problem: these advantages apply primarily in simple decision-making situations.
Typical heuristics in the investment process
In practice the following often dominate :
- Payback period : Focus on quick returns, ignoring later cash flows.
- ROI thresholds : Projects are filtered according to minimum ROI without considering portfolio effects.
- Uniform WACC : Standard discounting despite different project risks.
- "Strategic importance": Qualitative labels replace formal evaluation logic.
- Reputation logic : Projects are prioritised based on internal power or responsibilities.
These rules seem plausible, but they are local.
They do not produce a globally optimal allocation.
Why heuristics fail in portfolios
An investment portfolio is not a stack of isolated projects.
It is a system of :
- budget constraints
- capacity limits
- project dependencies
- payment profiles over time
- conflicting objectives (e.g. ROI, risk, impact, strategy)
This creates a combinatorial decision space: it is not the evaluation of individual projects that is decisive, but the quality of the combination.
Heuristics typically evaluate a project.
Optimisation evaluates the best combination.
The consequence: local instead of global decisions
Heuristics often lead to a "local optimum":
- Many projects make sense individually
- However, the overall combination is not the best
- Alternatives with a higher overall value are displaced
- Opportunity costs remain invisible
The result is not just a small loss of efficiency.
It is a structural misallocation of capital.
What makes optimisation different
Optimisation requires that a decision model be explicitly defined :
- Which projects are available for selection?
- What constraints apply (budget, capacities, dependencies)?
- Which objectives are maximised (value, impact, risk, strategy)?
- What time logic applies (cash flows, phases, milestones)?
Only then can the best combination be calculated.
Conclusion
Heuristics are an understandable reflex to complexity.
But in modern investment portfolios, they become a systematic source of error because they ignore combinatorics.
If you want to improve the quality of your decisions, you don't just have to decide faster.
You have to decide structurally: from a rule-of-thumb process to formal optimisation under constraints.
