Is Thanos’ Snap Truly Random? Exploring True vs Pseudo Random Numbers

After watching Avengers: Endgame , the author reflects on Thanos' goal of erasing half of all life to maintain universal balance and questions whether his "random" snap is truly random.

Randomness

Randomness denotes the absence of predictable patterns; a random process continuously generates unpredictable outcomes. In computing, random numbers are widely used, especially in communication security and modern cryptography.

Random numbers are classified as true random numbers and pseudo‑random numbers. Most software uses pseudo‑random numbers generated by deterministic algorithms with a seed.

True random numbers are derived from physical phenomena such as coin tosses, dice rolls, electronic noise, or nuclear decay, requiring unpredictability and non‑reproducibility.

Pseudo‑random numbers are produced by algorithms; they are deterministic and can only approximate true randomness.

Because a deterministic algorithm cannot generate genuine randomness, modern computers cannot produce true random numbers solely through software.

Why Thanos Is Not Fair

Analyzing Thanos' snap against the three properties of true randomness—randomness, unpredictability, and non‑reproducibility—shows it fails each test.

Randomness : The surviving characters in the movies follow patterns (e.g., paired couples), and Thanos himself is immune, indicating selective removal.

Unpredictability : Doctor Strange predicts 14,000,005 possible outcomes, demonstrating the process is not unpredictable.

Non‑reproducibility : The single victorious outcome is later reenacted, showing the event can be reproduced.

The snap likely follows a stratified sampling procedure: identify protected DNA, partition populations, and delete half of each partition.

True Random Number Generators

True random numbers are generated by hardware devices that exploit physical noise (thermal, photoelectric, quantum effects). While classical noise sources are deterministic in principle, quantum phenomena provide intrinsic randomness.

In 2018, NIST researchers published a Nature paper describing a quantum‑based method for generating provably random numbers, enhancing cryptographic security.

Random Number Generation in Java

Java offers several APIs for random number generation:

System.currentTimeMillis() can be used as a seed: final long l = System.currentTimeMillis(); To obtain a number in a range, apply modulo arithmetic: final int i = (int)(l % 100); Math.random() returns a double in [0,1): final double d = Math.random(); Convert to an integer range: final int i = (int)(d * 100); java.util.Random and java.util.concurrent.ThreadLocalRandom provide thread‑safe pseudo‑random generators; the latter offers better performance in multithreaded contexts.

Random random = new Random();
int i2 = random.nextInt(100);

SecureRandom (java.security.SecureRandom) accesses operating‑system entropy sources for cryptographically strong randomness, though it may be slower and block while gathering entropy.

The article concludes with an analogy: just as a product manager defines a wish‑fulfilling API, developers implement random number generators that serve various needs, from simple simulations to secure cryptographic operations.

References:

https://www.cnblogs.com/skywang12345/p/3341423.html

https://www.zhihu.com/question/277121161

http://www.nsfc.gov.cn/csc/20340/20343/30636/index.html