Core idea: Decision biases are predictable patterns that affect how evidence is gathered, assumptions are set and results are interpreted.
When this model is useful
Use a bias review when a favored option has momentum, estimates are unusually precise or the model owner has a strong stake in the outcome.
The method is most useful when the question, alternatives and time horizon are stated before calculations begin. It should clarify tradeoffs and identify which assumptions deserve attention, not merely produce a score.
Inputs and evidence
Independent estimates, base rates, forecast ranges, alternative explanations, disconfirming evidence and a reviewer separate from the model builder.
Use consistent units, definitions and periods across options. Mark estimates clearly, record their source and use ranges when precision is not supported. Evidence should be proportionate to the cost, risk and reversibility of the choice.
Step-by-step method
Gather views before group discussion, build the strongest case against the leading option, separate past sunk cost from future effects and compare estimates with historical error.
- Write the decision question and accountable owner.
- List realistic alternatives and eliminate any that fail hard constraints.
- Collect evidence and separate verified facts from assumptions.
- Run a base case and at least one downside test.
- Record the chosen option, accepted tradeoffs and review triggers.
Practical example
A team anchored on a vendor’s first price may treat later quotes as adjustments around that number rather than estimating value independently.
The point of the example is not the exact numbers. It is the discipline of using the same boundaries for every option and making the decision drivers visible.
Common mistake and limitation
A spreadsheet can encode bias while appearing objective. Review the model structure, not only the arithmetic.
Review whether the result changes under reasonable alternative assumptions. A close or fragile ranking should be presented as such rather than converted into false certainty.
Questions to ask before deciding
- What evidence would change the preferred option?
- Which consequence is missing because it is difficult to measure?
- Could a threshold or constraint override the numerical result?
- Who receives the benefits and who bears the costs or risk?
- When should the decision be reviewed?
Professional context: Legal, tax, investment, safety, medical, engineering and regulated decisions require qualified, jurisdiction-specific advice.