X-Message-Number: 9489 From: (John P. Pietrzak) Newsgroups: sci.cryonics Subject: Probability (warning, long post!) (was Re: Darwin-Preliminary) Date: Wed, 15 Apr 1998 05:18:40 GMT Message-ID: <> References: <> <> On 13 Apr 1998 21:34:36 GMT, (Ettinger) wrote: >Mr. Pietrzak denies, in effect, that there is any way of estimating >probabilities of future advances in various areas such as nanotech or others >relating to revival of cryonics patients. > >Please see"Cryonics: Probability of Rescue" on our web site, >http://www.cryonics.org I've recently received, and have (briefly) studied a paper copy of Cryonics: The Probability of Rescue" (thank you, Mr. Ettinger). I'm not certain I have everything correct here, but let me try to sum up and see if you agree with my summary below. (Note for others reading this: it's really unfair to judge a paper simply from another person's summary, particularly one who might not know what he's talking about, so if you're interested in it please refer to the source above to get all the details.) (Note 2: I'll cross-post this to cryonet, as I suspect it may be of some interest there as well.) (Note 3: If you've already read Mr. Ettinger's paper, you can skip to the end of this message to find my complaints about it. My paraphrase of the paper is probably too long, but does give context for my comments.) ------------------------------------------------------------------- [Paraphrasing the paper, section by section:] Introduction: This paper is intended to describe the "correct" manner in which probability theory should be applied in judging the probability of success in cryonics. Scientists may have a harder time with the paper than laymen. (?) I. Foundations of Probability Theory A. Approaches taken to probability 1. von Mises -- a "frequency" theory of probability A well known (?) form of probability, it deals with mapping a problem onto an idealized infinite sequence of (identical) experiments, and counts the probability as the frequency of occurance of the event in this sequence. This approach is vague in application. 2. Doob -- another "frequency" theory of probability This approach provides a somewhat less vague definition of the bounds on the sequence of experiments, but is still not appropriate in application. 3. Laplace -- "classical" probability (also frequency based) This approach involves defining probability as the ratio m/n, where n is the number of outcomes of an experiment (all being equally likely and mutually exclusive), and m is the number of these which are "favorable". Easier to apply. 4. Koopman -- A very different form of probability This approach, arguing that frequency theory is inadequate, attempts to bring intuition directly into the theory. It adds axioms called "Laws of Thought" to the system. B. Synthesis -- (of Koopman vs. the frequency-based probabilities) We attempt here to extend the frequency theory of probability to include "single events" and "subjective" probability. A description of the process for applying von Mises is given, noting: 1) the limiting frequency can never be noted exactly 2) experiments will not be perfectly identical 3) isolated events are considered outside the theory, unless an appropriate series of events can be simulated. 4) probability only refers to an experiment in the context of a sequence. Example: bets placed upon football game between teams W and M. Bettor A: Chooses team M from A.P. Poll. A.P. Poll has history of 80% success, so probability M will win is 80%. Bettor B: Chooses M randomly (the guy has never even heard of football before.) Probability M will win is 50%. Bettor C: A coach, rating M four touchdowns better than W, and having history of 95% correct choice of winner when given a four-touchdown difference, chooses M. Probability M will win is 95%. The differences being the experience of the bettors. [Another example is given, which I omit for space] We underscore the idea that the relevance of information is more important than it's precision. Conclusion: "Thus, we treat frequency probability and personal probability on a single objective basis, such that we can assign a definite (although imprecise) numerical probability to any event of whatever kind." C. Some Probability Notions Clarified [Most of these were unclear to me, so I'll not confuse the issue by trying to summarize them here.] II. Applications Examples of problems with probability as currently used. Example of medicine man bringing rain on day when low chance of rain given by weather service, thus validated. Lacks a priori probability. Example of ESP, again lacking a priori probability as well as poor experimental controls. Quote: "A rough guess, properly used, is much more useful than an exact datum inappropriately applied." A. A suggested Estimate for the Exponential Life Parameter An application of the above, originally applied to estimating the mean life of vacuum tubes (in 1953). Bit hard to encode in ASCII, but mainly is composed of comparing equation (2) giving theta-hat sub F (without a priori knowledge) to equation (5) giving theta-hat sub B (using a priori knowledge). In situations where samples are few, the a priori factor dominates and produces "better" results. B. Aspects of Probability Concerning Cryonics We will now apply probability to the success of cryonics. First, we will ignore the risk of global war or catastrophe, business risks, and political risks. The focus will be on the scientific chances. C. Intuition and Probability Examples: a) people intuitively avoid cars when crossing the street b) Goddard, Tsiolkovsky, etc. predicted moon rockets. Lacking any problems with feasability, they had the intuition that the political and monetary petty details would work out. c) Leonardo da Vinci intuitively invented flying machines; given, for example, birds, the scaling up to flying machines was only details. Many experts fail to intuitively accept cryonics due to the time involved -- it will not succeed within their lifetimes. D. Unlimited Wealth -- Not "Probable" but Certain [I'll skip this part, I don't really care about it and it's assumptions are even less defensible than those in the previous section.] E. Summing Up: "Probability" Derives from "Experience" [Let me quote a portion of the summary verbatim:] " 1. In the modern era, not a single goal of science, so far as I know, has been shown impossible (although some have proven more difficult than expected, and others become irrelevant). Odds-on for success. 2. Many cells survive even uncontrolled freezing, and there have been partial successes with freezing mammalian brains: odds-on that, even in freeze-damaged brains, injury is limited and reparable. 3. The Precedent Principle, the Feinberg Principle, and the prospect of nanotechnology assure us that the atom-by-atom manipulation of tissue (frozen or not) will allow construction or reconstruction, in finest detail, of any human configuration known, designable, or capable of inference. [...] Odds-on that _you_ can be restored. In still other words, we conclude the "gamble" of cryonics is odds-on in your favor: the probability of success (from the standpoint of technical feasability) is much closer to one (certainty) than to zero. The number may be imprecise, but it is the best and most scientific estimate available." ---------------------------------------------------------------- I guess I became disappointed in this paper by the time I got to the Koopman section in the discussion of approaches to probability, becase I knew it was going to delve into regions of which I don't approve. In short, Mr. Ettinger's argument seems to follow this line: 1) Intuition is an unavoidable part of probability, and can be an asset when included explicitly into the theory. (Part I of the paper.) 2) A priori knowledge is valuable in determining the true probability of an event, using the "Classical" Bayesian approach. This is particulary true when few samples are used, as the a priori knowledge can (theoretically) make up for the lack of information. (The first part of part II goes over this, including the mathematical example.) 3) Mr. Ettinger provides some (dubious, in my mind) examples of intuition which have, over time, been proven correct (in the Intuition and Probability section). He's essentially trying to give a large probability to the success of intuition; trying to show intuition as the coach who can forecast success with a 95% probability. 4) Here's the magic part: we now use our intuitive knowledge of cryonics, and apply it (a priori) to a probability function which has (guess what) _no_ samples at all! Hmm, all we've got left, then, is our a priori knowledge, so that becomes our probability. Take a look again above at the material I quoted at the end of my summary of his paper. Those three points, despite what you or I may think of them subjectively, are not only the only inputs Mr. Ettinger provides for the a priori knowledge, they are also (through the magic of Classical Bayesian manipulation) the only possible outputs of his probabilistic study. Unless I've missed my mark completely, this twenty page thesis comes just about as close to circular reasoning in it's conculsion as I've ever seen. This is _always_ a problem when you bring (unconstrained!) subjective reasoning into a theory, and the a priori element of the classical Bayesian system seems pretty well general license to do whatever you wish when the number of samples is small. The combination of the two is deadly. :) Basically, I just can't agree that he has in any manner reached a "scientific" estimate. John Rate This Message: http://www.cryonet.org/cgi-bin/rate.cgi?msg=9489