Here’s some more fun stuff gleaned from the book I’m reading.
One of the canonical reasons for poor judgement is known as availablity; i.e. we think and work using the tools that are easily at hand. If a tool is beyond reach we are far less likely to apply that tool. There are lot of examples. The folks that plan for floods, for example, rarely plan for floods other than those they have experienced. If your asked a mess of people to estimate how many words start with the letter r, like road, v.s. how many end with the letter r, like tour they will get the wrong answer, begins with, because your noodle is set up to recall words by their first rather than their last letter. I love the variant of that example; people will guess that abstract nouns, like love, hate, truth, are occur more commonly than concrete knowns, like door, street, bicycle. Apparently people have an easier time recalling abstract nouns v.s. concrete ones. Who knows why, possibly because the abstract nouns cover broad swaths of experiance.
In reading about availablity, and the other insights outlined in this book, I find myself thinking about how they get mixed into the ways people market products, debate issues, manage planning, and even how you could create carnival games out of them. One reason a component manufature has to give way a huge number of free samples is to assure that his components are highly available at the moment when some random designer is reaching out to pick something up to solve his problem. That’s one reason Open Source works; it adopts a r-Strategy.
This example of availably is particularly thought provoking. A reasonably standard formal way to estimate risk is to create a fault tree. You can view lots of examples at Google Images. The process of making a fault tree is lot like the process people use in making any plan. You attempt to collect all the contengencies and then sort out their likelyhood and dependencies. For example in thinking about why the car might not start you might include dead battery and left headlights on by mistake. Professional planning and fault tree work involves getting experts to help. You might do that by doing surveys and investigations of various car starting falures; or you might interview experts (say are mechanics) and have them help generate contengencies and probablities of those contengencies.
Because of availablity asking the experts does not work.
Say you have methodically assembled a large and nominally complete fault tree and because you doubt you’ve thought of everything you include a node for “other causes.” At that point your fault tree covers a 100 percent of the failure scenerios. You go to the experts and ask them to help by assigning 100 points (i.e. percentage chance) across the perimeter of the tree.
Why doesn’t that work?
It doesn’t work because studies show that you get radically different answers from the experts if you check their work by varying the fault tree you present. If you have a tree with 10 nodes and you collapse 2 or 3 into the “other causes” node the experts will then raise their estimates for all the remaining causes. Because the nodes you merged into “other causes” are no longer easilly available.
Of course what’s in that “other causes” node is the long tail of contengencies.
You imply there are more words that end in ‘r’ than begin with it, which actually matches my guess, based largely on the ‘er’ and ‘or’ suffixes. My /usr/dict/words agrees (2514 vs 3010), but surprisingly the Official Scrabble Players’ Dictionary (2nd edition, I think) disagrees: (7141 vs 6968). That may be due to the OSPD including plurals, roughly doubling the number of ‘^r’ nouns, but not increasing the number of ‘r$’ at all.