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Mathematics Of Correctness : When To Give Up ?


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#1 petrushkagoogol

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Posted 15 July 2017 - 12:21 AM

Consider an outcome based on 5 sequential decisions :
N=5 (mandatory)
 
Probability of Success P(x) >= 0.5 / step
 
0.5, 0.5, 0.5, 0.5, 0.5 Total = 2.50 Person A
0.4, 0.3, 0.4, 0.8, 0.7 Total = 2.60 Person B
 
Person B takes a hit initially, but gains in the long term.
 
0.1, 0.1, 0.1, x, y Total = NA Person C
 
Person C should give up after event 3 because he cannot reach 2.5 (0.5 * 5), even if he perfects outcome 4 and 5. (1/1).
 
Is giving up not as stupid as it seems ?  :bow:  :bow: 
 
P.S. Application could be a sequential manufacturing industry like diamond processing.
 
Steps are -
 
  • Cutting
  • Polishing
  • Interim QA  - introduce a micro QA for minimizing time of subsequent steps for non-eligible diamonds 
  • Bagging
  • Setting
  • QA

Edited by petrushkagoogol, 15 July 2017 - 12:25 AM.


#2 Buffy

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Posted 15 July 2017 - 11:45 AM

You may wish to look up "combined probabilities" and "Bayes' Theorem"... 

 

It is, however, true that people rarely know when to "give up" when they really should.

 

 

The 50-50-90 rule: anytime you have a 50-50 chance of getting something right, there's a 90% probability you'll get it wrong, :phones:

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#3 Farming guy

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Posted 12 August 2017 - 07:48 AM

 

 

It is, however, true that people rarely know when to "give up" when they really should.

 

 

Is this why gambling is addictive to some?

 

If the weather forecast calls for a 20% chance of showers, it seems there is a 90% chance it will rain on the hay field that is almost ready to bale.

 

I don't need to gamble because I farm.



#4 exchemist

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Posted 25 August 2017 - 11:33 AM

Is this why gambling is addictive to some?

 

If the weather forecast calls for a 20% chance of showers, it seems there is a 90% chance it will rain on the hay field that is almost ready to bale.

 

I don't need to gamble because I farm.

Just seen this and I am sure it is all too true. Though I suspect we all tend to remember disproportionately those occasions on which a forecast was wrong, rather than the more routine incidences of them being right.