Computer programs exist that attempt to generate random numbers, but n

Question

Computer programs exist that attempt to generate random numbers, but no such program can fully replicate a truly random selection. Computer programs are, by definition, a set of instructions that use an input to generate an output. If both the input and the algorithm are known, the result is fully predictable. Even the best random number generation programs can only be called pseudo-random because the input itself is generated by the program. As a result, a pattern will emerge within the results, even if the program is sophisticated enough to make the pattern very complicated, and that pattern can be used to predict future results. True random number generation often depends on measurement of an unpredictable physical phenomenon, such as weather patterns or atmospheric radiation, and using that measurement as an input to generate a result.

The passage provides information sufficient to infer each of the following statements EXCEPT: ​

A​ The outputs of random number generators that provide their own input could, with enough information about past results, eventually be predicted. ​
B​ If a person knew both the algorithm that a corporation used to generate a truly random number and the atmospheric measurements that served as the input, the person would be able to generate the same output obtained by the corporation.  ​
C​ With a sufficiently large table of the results of a pseudo-random number generator over time, it would be possible to derive the input and algorithm used to generate those results. ​
D​ The integrity of applications for which the unpredictability of the result is vital, such as lotteries or data encryption, can be best preserved by using a method of random number generation that is truly random. ​
E​ It is impossible to know the exact amount of atmospheric radiation emitted at a particular location and time until after a measurement is taken.

singhall · Answer

A short summary of the given question stem is if you program is generating the input, is applying an algorithm and generating an output-> The results driven would be predictable from the patterns that would be seen in the output. With this thought, let's evaluate...

We need to pick a choice that doesn't follow this thought...

A​ The outputs of random number generators that provide their own input could, with enough information about past results, eventually be predicted. ​ Direct conversion of the summary..

B​ If a person knew both the algorithm that a corporation used to generate a truly random number and the atmospheric measurements that served as the input, the person would be able to generate the same output obtained by the corporation. ​

This choice is adding that the program is not picking the input, nonetheless you know the input, there is an an algorithm, very easy to predict the result. OUT

C​ With a sufficiently large table of the results of a pseudo-random number generator over time, it would be possible to derive the input and algorithm used to generate those results. ​
 Here we only know the results, we want to derive both the inputs and the algo.. Nope the argument never said this. KEEP

D​ The integrity of applications for which the unpredictability of the result is vital, such as lotteries or data encryption, can be best preserved by using a method of random number generation that is truly random. ​

This is an example of the application of a good random generator where the results cannot be predicted.

E​ It is impossible to know the exact amount of atmospheric radiation emitted at a particular location and time until after a measurement is taken. 
 The last statement in the QS talks about this..

I hope this helps.
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Have faith in yourself.

Aarti0701 · Answer

In option E, although author talks about it but in option E , word "impossible" is used, which is too extreme, while author uses the word "often" . Generally in CR questions, we don't prefer such options, right? Can someone please explain? However, i do agree that option 'C' is the best suited option.

Kaifbinarshad · Answer

Option E says that we will not be able to know how much atmospheric radiation is emmitted at a place until we measure it and clearly it can not be inferred from the QS,the question stem only talks about atmospheric radiation as an input to make the output truly random and not predictable,it does not talk anything about measurement of it.so clearly it can't be inferred,if OA was not given anywhere then they would have got E as an answer.

coolsies · Answer

The question nowhere talks about what requires use of truly random number generation. How can we derive D from the question then?

Elite097 · Answer

Pseudo-random Generators: Outputs are based on a known algorithm and input, making them predictable if these are known.
True Random Generators: Depend on physical phenomena like atmospheric radiation, which are described as unpredictable.
The passage emphasizes unpredictability but does not explicitly state that atmospheric radiation is impossible to know before measurement.
Option Analysis
E. It is impossible to know the exact amount of atmospheric radiation emitted at a particular location and time until after a measurement is taken.
The passage describes atmospheric radiation as an unpredictable phenomenon used for true random generation. While this implies difficulty in predicting radiation levels, it does not explicitly state that it is impossible to know the exact amount before measurement.
However, unpredictability of radiation is inherent in the concept of true random generation, making this a reasonable inference from the passage.
C. With a sufficiently large table of the results of a pseudo-random number generator over time, it would be possible to derive the input and algorithm used to generate those results.
The passage states that patterns emerge in pseudo-random outputs, but it does not go so far as to say that these patterns can definitively reveal the input and algorithm.
Reverse-engineering a pseudo-random generator requires more than just observing patterns; it depends on additional factors not discussed in the passage, such as computational techniques or knowledge of the program's structure.
This claim goes beyond what is supported by the passage and cannot be inferred directly.
Why Not E?
E is a stronger claim than what the passage explicitly states, but it aligns with the concept of true randomness and unpredictability. Atmospheric radiation is described as unpredictable, which supports the idea that it cannot be precisely known without measurement.
The passage's discussion of unpredictability makes E more reasonable than C, which introduces a claim not addressed in the passage.
Conclusion
The correct answer is C because the passage does not provide sufficient evidence to conclude that the input and algorithm of a pseudo-random generator can be derived solely from observing its outputs. E is supported by the passage's description of atmospheric radiation as an unpredictable phenomenon.

arushi118 · Answer

The passage says: "a pattern will emerge within the results, even if the program is sophisticated enough to make the pattern very complicated, and that pattern can be used to predict future results." So according to me this should mean that the pattern which emerges within the results can be used to predict the input and algorithm. So cannot understand that how can we not infer C to be correct.

KarishmaB can you please help.
Thankyou in advance.

KarishmaB  can you please help.
Thankyou in advance.

KarishmaB · Answer

We are given that the pattern can be used to predict results, not the algorithm. 
So a pseudo random algorithm produces "random numbers". If you have enough of those, over time, you can find a pattern in which the numbers are getting predicted (such as odd number appears after every 2, that odd number increases by 1 every time...) so you can predict the result (now next number will be say 7). But what input was given and how the algorithm processed it to generate the output, you may not be able to say.

Computer programs exist that attempt to generate random numbers, but n

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Computer programs exist that attempt to generate random numbers, but n

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