I think a lot of folks are missing the point (and the author could have been more clear).
The point is not that bad heuristics are bad, but to think about when heuristics should be used and what value they add.
In the examples, heuristics shouldn't be used to reduce probabilistic occurrence to binary likelihood before deciding to act. Decisions should be informed based on the actual data when available. Application of a heuristic results in a loss of information, which reduces accuracy and applicable scope. Sometimes this can be entirely defeat the purpose.
Perhaps the recommendation is that if you are tempted to use a heuristic, stop and ask if it is necessary, and what you stand to gain from using it instead of other data or new analysis.
The point is not that bad heuristics are bad, but to think about when heuristics should be used and what value they add.
In the examples, heuristics shouldn't be used to reduce probabilistic occurrence to binary likelihood before deciding to act. Decisions should be informed based on the actual data when available. Application of a heuristic results in a loss of information, which reduces accuracy and applicable scope. Sometimes this can be entirely defeat the purpose.
Perhaps the recommendation is that if you are tempted to use a heuristic, stop and ask if it is necessary, and what you stand to gain from using it instead of other data or new analysis.