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Randomness is really important in games.
From simple character draws, to damage with a range, or effects that activate randomly, or critical hits. There are also things like monsters or NPCs that have random movements, or even just games that are entirely based on randomness, like gambling.
Unfortunately, PCs cannot generate “perfectly random numbers”.
Because they appear to be random, they are called pseudorandom. Pseudo- is a prefix meaning fake, similar, or similar. In other words, it is a fake random number.
It simply returns a “reprocessed number” by adding, subtracting, multiplying, and dividing a base value (seed value) such as the time of the PC or server.

In other words, it is a number that is "random but has a pattern" and is generated by an algorithm.

In games like Minecraft or Factorio, even if the map is randomly generated, if you know the seed value, other people can use the same map. This is because it is the result of a pseudo-random number created by processing the seed value.
Planet Poker, where a simple random number algorithm that used the server's time as a seed was hacked, allowing anyone to know the next card to be dealt, was doomed.
▷ Superstitions that surprisingly come true due to pseudorandom numbers? - If you support the wealth, you can get a good one: In the case of algorithms that use a random result as a seed for the next random value, there is a tendency for certain patterns to repeat. - It sticks well when strengthened at a specific time: Games based on simple pseudo-random number algorithms basically use the “current server time” as the seed value for the random number. So, it is possible that you will get a good chance to strengthen or draw at a certain time (Seed value). - Certain characters are more likely to be drawn (in certain locations or conditions (weather))?: Some games had an issue where random events shared a random seed . It was indeed possible to draw a specific desired value under certain conditions (while a random event occurred).
If you randomly pick 0 and 1 and mark the dots in order, if it is truly random, it will look like the one on the left. However, the random numbers generated by a typical random number algorithm (right) appear to have some pattern or regularity.
Because of these limitations of pseudo-randomness, various methods or algorithms have been devised around the world to generate “truly random” numbers. Among them, let's look at some interesting methods of generating genuine random numbers.
🔥 1. Lava Lamp Randomness It's used by a security company called Cloudflare. Dozens of magma lamps are displayed on the wall, and we keep taking pictures of them. The movement of fluids inside a magma lamp is unpredictable due to complex physical phenomena such as temperature and convection. This photo is converted into digital data and hashed to create a random number.
Hashing is very important here, as even a single character difference can result in a completely different string. Even if there is a static part (background) in the picture that does not change, if there is a different part, the result can be a completely different string (random seed).
What is a hash algorithm? Description Link: https://page.onstove.com/indie/global/view/9986035?boardKey=10005
⚛️ 2. Using Radioactive Decay
It is impossible to predict exactly when radioactive material will decay. Generates a random number based on the time intervals of decay events detected by a radiation detector. The tool used here is a famous radiometric device called a 'Geiger counter'.
This is a typical site. When you enter this site, you can hear the unique 'tick tock tock' sound of a radiation detector every time a random number is generated. https://www.fourmilab.ch/hotbits/
📷 3. Sensor noise of the webcam When you turn on a webcam without recording anything, the image will have very subtle pixel shifts due to tiny electrical noise from the sensor. We convert these subtle changes into data and use them as random numbers.
When shooting dark scenes, you can easily encounter the “squeaky noise”, and random numbers are generated based on this. (Sangchunjae, Blue House at night)
🌐 4. Internet Latency (Ping Jitter) When you ping servers around the world, response times will constantly change depending on complex network conditions (routing, traffic, etc.). This method sends a response to the server and precisely measures the delay time (ms) to create a random number.
This huge number of completely different pings are used as seeds for random values.
🎲 5. Human mouse movement A user's mouse cursor movements, click intervals, keyboard timings, etc. are good sources of entropy because humans input in unpredictable ways. Sometimes we also use the pixel coordinates that the user clicked as part of the seed value (since it is difficult to get an exact matching touch on mobile).
By the way, the famous captcha with the “I am not a robot” checkbox is also We determine whether a user is a bot by examining the user's mouse movements (direction, speed, etc. of the mouse moving in a non-straight line or searching for a point), click intervals, and response speed.
ReCAPTCHA is not just about checking the robot with just one check box, but also checking the process.
🧪 6. Vacuum Noise/Quantum Fluctuations It is a random number generation method based on quantum physics, which is different from classical physics. The electromagnetic fields of a vacuum exhibit random fluctuations in phase and amplitude at all frequencies. By carefully measuring these fluctuations, we can generate random numbers with extremely high bandwidth. This allows us to generate hundreds of unique random numbers almost instantly. There exist quantum random number generators (QRNGs) that measure these quantum fluctuations to create random numbers.
The example image is a repeating gif file, so it looks regular, but it doesn't actually repeat. It just explains what quantum fluctuations feel like.
In addition, there are processors (semiconductors) dedicated to random number generation based on various physical phenomena (acceleration, magnetism, ambient light, temperature, and VLF reception/amplification)!
'Cortex M0 processor', a random number generator processor that can be used in Arduino
▷ Recently, almost perfect random numbers are generated at the OS level.
There is a limitation that TPM 2.0 support is required to update to Windows 11. Unfortunately, my sub-notebook (netbook) doesn't make it through this and is destined to be thrown away ㅠㅠ
There is a way to bypass TPM 2.0, but it's probably time to replace it because your laptop's specs are just too low.
TPM stands for Trusted Platform Module, which is installed in the latest CPUs and motherboards. As the name suggests, it is a hardware-based technology that is directly related to security. As a hardware that supports various encryptions, the supported functions include the generation of “true random numbers.”
Now, we have reached an era where we can generate true random numbers at the PC level without the help of true random number generation methods as mentioned above. Of course, if you look at it objectively, this is also a pseudo-random number created by combining a seed value and an algorithm. However, it is a random number at a level where it is virtually impossible for a person to trace the pattern of the random number or to predict the next number by reverse-estimating the algorithm, and since the pattern as above is almost invisible, there is no problem in viewing it as a true random number.
Since pseudorandom number algorithms have been greatly modified and improved over time, the need to approach true random numbers as above is disappearing.
Next time you're going to do a power-up or draw, if this comes to mind... it'll be fun to wonder if it's really random, or if it's a pseudo-random number with a pattern, lol.

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