Relay-Version: version B 2.10 5/3/83; site utzoo.UUCP Posting-Version: version B 2.10.2 9/18/84; site brl-tgr.ARPA Path: utzoo!watmath!clyde!burl!ulysses!mhuxr!mhuxt!houxm!whuxl!whuxlm!akgua!gatech!ut-sally!seismo!brl-tgr!gwyn From: gwyn@brl-tgr.ARPA (Doug Gwyn ) Newsgroups: net.research,net.cog-eng Subject: Re: Failure Probabilities in Decision Chains Message-ID: <947@brl-tgr.ARPA> Date: Fri, 20-Dec-85 17:22:15 EST Article-I.D.: brl-tgr.947 Posted: Fri Dec 20 17:22:15 1985 Date-Received: Sun, 22-Dec-85 01:23:53 EST References: <892@brl-tgr.ARPA> Distribution: net Organization: Ballistic Research Lab Lines: 24 Xref: watmath net.research:390 net.cog-eng:604 > One of our Directors has asked me to inquire about a reputed Bell labs > study from 7 or so years ago, which he heard about at a conference. This > study was on "failure probabilities"; one of the statements or > conclusions he recalls was that if you have a string of five sequential > decisions, one after the other, each based upon the preceeding, the > reliability of the result is at the 59% level. I don't really have much > other than this to go on, so, if this comment rings a bell with you, and > you know the study (or studies) that this sort of conclusion came out > of, I would greatly appreciate it if you could mail me a reference. If > you know of work being done in this area by other organizations or > particular researchers, any comments or rumors or hearsay or pointers to > published work or theses would be welcomed. Gee, this is hardly revolutionary. If you assume a single decision can be made with 90% confidence, and successive decisions are statistically independent (important!), then the confidence for the composition is (90%)^5 which is about 59%. This is very elementary probability theory; almost any mathematically trained person could reproduce this -- it isn't any research specialty. Perhaps you are really interested in "fault tree" analysis or rare-event estimation, such as is used in predicting failure rates for nuclear reactors and other such systems where empirical data are few or non-existent.