1. Check for Self-interested Biases
- Is there any reason to suspect the team making the recommendation of errors motivated by self-interest?
- Review the proposal with extra care, especially for over-optimism.
2. Check for the Affect Heuristic
Has the team fallen in love with its proposal? Rigorously apply all the quality controls on the checklist.
3. Check for Groupthink
Were there dissenting opinions within the team? Were they explored adequately? Solicit dissenting views, discreetly if necessary.
4. Check for Saliency Bias
Could the diagnosis be overly influenced by an analogy to a memorable success? Ask for more analogies, and rigorously analyse their similarity to the current situation.
5. Check for Confirmation Bias
- Are credible alternatives included along with the recommendation?
- Request additional options.
6. Check for Availability Bias
If you had to make this decision again in a year’s time, what information would you want, and can you get more of it now? Use checklists of the data needed for each kind of decision.
7. Check for Anchoring Bias
Do you know where the numbers came from? Can there be:
- …unsubstantiated numbers?
- …extrapolation from history?
- …a motivation to use a certain anchor?
Re-anchor with figures generated by other models or benchmarks, and request new analysis.
8. Check for Halo Effect
Is the team assuming that a person, organisation, or approach that is successful in one area will be just as successful in another? Eliminate false inferences, and ask the team to seek additional comparable examples.
9. Check for Sunk-Cost Fallacy, Endowment Effect
Are the recommenders overly attached to a history of past decisions? Consider the issue as if you were a new CEO.
10. Check for Overconfidence, Planning Fallacy, Optimistic Biases, Competitor Neglect
- Is the base case overly optimistic?
- Have the team build a case taking an outside view; use war games.
11. Check for Disaster Neglect
- Is the worst case bad enough?
- Have the team conduct a pre-mortem: Imagine that the worst has happened, and develop a story about the causes.
12. Check for Loss Aversion
Is the recommending team overly cautious? Realign incentives to share responsibility for the risk or to
remove risk.