Monday, November 21, 2022

Before you make that big decision

 1. Check for self -interested biases

Is there any reason to suspect the person making the recommendation of errors motivated by self interest?

Review the proposal with extra care, especially for over optimism.

2. Check for affect heuristic

Had the person fallen in love with its proposal?

Rigourously apply all the quality controls on this checklist.

3. Check for groupthink

Were there dissenting opinions within the team? Were they explored adequately?

Solicit dissenting views, discretely 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 come from? Can there be unsubstantiated numbers, extrapolation from history, a motivation to use a certain anchor?

Reanchor with figures generated by other models, or benchmarks, and request new analysis

8. Check for halo effect

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 the 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.