Can Stupid people do Smart things ?
Andre Willers
4 Oct 2014
Synopsis :
They can , and do all the time . There are proven algorithms
that enhances this Bootstrap Effect .
Discussion :
Interestingly enough , when I googled “can stupid people do
smart things” , there were no hits . Millions of hits for the inverse of smart
people doing stupid things .
This shows a thoroughly human bias of stupendous proportions
.
1.The Secretary Problem Algorithm
P=1/e
=0.367879441
~ 37%
The Bootstrap Effect is built into the nature of the
Universe : the statistical rules and logic principles .
God’s thumb on the scale .
The secretary problem is one of many names for
a famous problem of the optimal stopping theory.
The problem has been studied extensively in the fields of applied probability, statistics,
and decision theory. It is also known as the marriage
problem, the sultan's dowry problem, the fussy suitor
problem, the googol game, and the best choice problem.
The basic form of the problem is the following: imagine an
administrator willing to hire the best secretary out of 
rankable applicants for
a position. The applicants are interviewed one by one in random order. A
decision about each particular applicant is to be made immediately after the
interview. Once rejected, an applicant cannot be recalled. During the
interview, the administrator can rank the applicant among all applicants
interviewed so far, but is unaware of the quality of yet unseen applicants. The
question is about the optimal strategy (stopping rule)
to maximize the probability of selecting the best applicant. If the decision
can be deferred to the end, this can be solved by the simple maximum selection algorithm of tracking the
running maximum (and who achieved it), and selecting the overall maximum at the
end. The difficulty is that the decision must be made immediately.
rankable applicants for
a position. The applicants are interviewed one by one in random order. A
decision about each particular applicant is to be made immediately after the
interview. Once rejected, an applicant cannot be recalled. During the
interview, the administrator can rank the applicant among all applicants
interviewed so far, but is unaware of the quality of yet unseen applicants. The
question is about the optimal strategy (stopping rule)
to maximize the probability of selecting the best applicant. If the decision
can be deferred to the end, this can be solved by the simple maximum selection algorithm of tracking the
running maximum (and who achieved it), and selecting the overall maximum at the
end. The difficulty is that the decision must be made immediately.
Algorithm :
The problem has an elegant solution. The optimal stopping rule
prescribes always rejecting the first 
applicants after
the interview (where e is the base of the natural logarithm) and then
stopping at the first applicant who is better than every applicant interviewed
so far (or continuing to the last applicant if this never occurs). Sometimes
this strategy is called the 
stopping rule,
because the probability of stopping at the best applicant with this strategy is
about already for
moderate values of . One reason why the
secretary problem has received so much attention is that the optimal policy for
the problem (the stopping rule) is simple and selects the single best candidate
about 37% of the time, irrespective of whether there are 100 or 100 million
applicants. In fact, for any value of the probability of
selecting the best candidate when using the optimal policy is at least .
applicants after
the interview (where e is the base of the natural logarithm) and then
stopping at the first applicant who is better than every applicant interviewed
so far (or continuing to the last applicant if this never occurs). Sometimes
this strategy is called the stopping rule,
because the probability of stopping at the best applicant with this strategy is
about already for
moderate values of . One reason why the
secretary problem has received so much attention is that the optimal policy for
the problem (the stopping rule) is simple and selects the single best candidate
about 37% of the time, irrespective of whether there are 100 or 100 million
applicants. In fact, for any value of the probability of
selecting the best candidate when using the optimal policy is at least .
This can be seen from
http://andreswhy.blogspot.com/2008/04/infinite-probes2.html
as well from a completely different derivation .
1.2.The second best :
P tends to 0.25 .
See why this is important in http://andreswhy.blogspot.com/2012/08/the-long-revenge.html
The best go off somewhere , and the second-best remain to
run things here .
Algorithm:
One variant replaces the desire to pick the
best with the desire to pick the second-best. Robert
J. Vanderbei calls this the "postdoc"
problem arguing that the "best" will go to
Harvard. For this problem, the probability of
success for an even number of applicants is exactly 
. This probability tends to 1/4 as n tends to infinity
illustrating the fact that it is easier to pick the best than the second-best.
. This probability tends to 1/4 as n tends to infinity
illustrating the fact that it is easier to pick the best than the second-best.
2.An interesting application is in DNA editing
.
The cell’s own editors and DNA repair machinery
use this principle , as can be seen from D(0),which is the dose producing, on average, one lethal
hit per cell, where 37% of the cells survive. https://www.inkling.com/read/perez-bradys-principles-practice-radiation-oncology/chapter-2/quantitative-radiobiology-and
This can be seen as the DNA repair using the
1/e algorithm as above .
3.Just using this algorithm consistently , the
user betters his chances over random by (1-1/e) = 0.63212055882 ie 63 % . This
is an enormous advantage .
To put it into context : If a person with an IQ
100 does not use the algorithm and chooses as chance happens (regrettably frequent
as far as marriage is concerned “Marry in haste and repent at leisure”) , then
a competitor will have to have an IQ of 163 to beat ( IQ100+ Algorithm ) .
The irony : ( IQ163 + Algorithm ) has no extra
advantage .
Conclusion : This Universe is designed to have liminal
IQ’s of 100*(2-1/e)^L = 100*(1.63)^L , where L is Intelligence Level = 1,2,3,…
L(1) = 163
L(2) = 265
L(3) = 433
L(4) = 705
L(5) = 1157
Etc
This is true for any intelligence , AI’s as well .
L(2) is equivalent to 1 in 4.4 x 10^3939 , which means L(2)
personalities will be mostly virtual .(Est number of atoms in universe ~ 10^82
.
4.AI’s
Humans can then create AI’s smarter than themselves . But the first hurdle is high . IQ 265 :
See Appendix IQ below for highest human estimated IQ’s . (The
fQuannigton one is dubious : somebody’s little joke)
5.Your kids can be smarter than you .
6.Other Bootstrap Algorithms :
6.2 http://en.wikipedia.org/wiki/Neuroevolution_of_augmenting_topologies NEAT for complexification/decomplexification
6.3 http://en.wikipedia.org/wiki/Kolmogorov_complexity Optimal descriptors exist and can be found
. Ie you can be smarter by reorganization .
A properly chaired
committee can reach levels 63% better than the stupidest individual in the
group .
A bad chairman results
in the inverse : 63 % stupider than the stupidest individual in the group . In
real life these are always remembered .
6.4.1 Smart
individuals making stupid mistakes : think of a person as a collection of
traits , habits , etc , with the consciousness as chair of this committee . If
the chair is having a bad day , truly stupid things can emerge .
6.4.2 A happy
marriage .
6.6 Pop-a-pill and Plug in . Common in human enhancement
project , from Ritalin to transcranial stimulation . See http://andreswhy.blogspot.com/2014/09/prodigies-update-i.html
6.7 Religion
6.8 Many others I don’t know about .
7.The Giga Society .
Membership of the Giga Society is ideally open to anyone
outscoring .999999999 of the adult population on at least one of the accepted
tests. This means that in theory one in a billion individuals can qualify.
This is so because the world
average I.Q., projected onto this scale, is not 100 but somewhat below 90,
probably between 85 and 90. The crux is that the I.Q. scale we use in actuality
refers only to the adult populations of Western countries such as those in
Europe and North America, and is not correct for the rest of humanity.
One wonders if the elusive Dr William Alfred Quannigton in
Appendix A is a member . That L() level would be associated with a virtual
intelligence . Ie a machine or hybrid machine intelligence . An AI poking fun
at the natives .
8. Further references you will need :
AI’s
Souls
Stupidly yours
Andre
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Appendix IQ
Here are the top ten highest IQs ever recorded:
1.Phd. William Alfred Quannigton
Child IQ=350+ (Should have been 430+)
Adult IQ=300+
2. William James Sidis
IQ=265+
3. Leonardo Da Vinci
IQ=225
4.Johann Wolfgang Von Goeth
IQ=210
5. Hypatia
IQ=210
6. Nathan Leopold
IQ=210
7. Emanuel Sweedonburg
IQ=205
8. Gottfried Wihlem Lebinitz
IQ=205
9.Hugo Grotius
IQ=200
10.Tomas Wolsey
IQ=200
There is no set way to measure intelligence as there are too
many aspects to take into account , so don't put too much faith in your IQ
rating. Hope this helps ;-)
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