Random Numbers and Gaming

In Counter Strike: Global Offensive spray pattern control becomes a muscle memory to a player after long periods of playing. It’s a design choice that makes the gunplay between players more about instant crosshair placement with the faster player usually winning. This is very different from the gunplay of the current popular shooter Player Unknown’s Battlegrounds. Player Unknown’s Battleground’s spray pattern for the guns are random. So how does this affect the player experience? Well as opposed to Counter Strike: Global Offensive, the design choice makes gunplay between two players more about how a person can adapt faster when encountering another. So why does the change from a set pattern to random make for such a different experience in gameplay. Did the usage of randomness make for such a different experience? Random numbers in video games are utilized frequently and have been used for a long time, whether it was better for the player experience is often hard to tell. So just what is this “randomness”? What games use random numbers and why? Are random numbers a bad practice? The usage of random numbers in games is nothing new, but poor implementations and bad business practices have given random numbers a smudge mark on their reputation

Algorithm for normal random numbers

We propose a simple algorithm for generating normally distributed pseudo random numbers. The algorithm simulates N molecules that exchange energy among themselves following a simple stochastic rule. We prove that the system is ergodic, and that a Maxwell like distribution that may be used as a source of normally distributed random deviates follows when N tends to infinity. The algorithm passes various performance tests, including Monte Carlo simulation of a finite 2D Ising model using Wolff's algorithm. It only requires four simple lines of computer code, and is approximately ten times faster than the Box-Muller algorithm.Comment: 5 pages, 3 encapsulated Postscript Figures. Submitted to Phys.Rev.Letters. For related work, see http://pipe.unizar.es/~jf

PRNG Random Numbers on GPU

Limited numerical precision of nVidia GeForce 8800 GTX and other GPUs requires careful implementation of PRNGs. The Park-Miller PRNG is programmed using G80’s native Value4f floating point in RapidMind C++. Speed up is more than 40. Code is available via ftp ftp://cs.ucl.ac.uk/genetic/gp-code/random-numbers/gpu park-miller.tar.g

Random Numbers Certified by Bell's Theorem

Randomness is a fundamental feature in nature and a valuable resource for applications ranging from cryptography and gambling to numerical simulation of physical and biological systems. Random numbers, however, are difficult to characterize mathematically, and their generation must rely on an unpredictable physical process. Inaccuracies in the theoretical modelling of such processes or failures of the devices, possibly due to adversarial attacks, limit the reliability of random number generators in ways that are difficult to control and detect. Here, inspired by earlier work on nonlocality based and device independent quantum information processing, we show that the nonlocal correlations of entangled quantum particles can be used to certify the presence of genuine randomness. It is thereby possible to design of a new type of cryptographically secure random number generator which does not require any assumption on the internal working of the devices. This strong form of randomness generation is impossible classically and possible in quantum systems only if certified by a Bell inequality violation. We carry out a proof-of-concept demonstration of this proposal in a system of two entangled atoms separated by approximately 1 meter. The observed Bell inequality violation, featuring near-perfect detection efficiency, guarantees that 42 new random numbers are generated with 99% confidence. Our results lay the groundwork for future device-independent quantum information experiments and for addressing fundamental issues raised by the intrinsic randomness of quantum theory.Comment: 10 pages, 3 figures, 16 page appendix. Version as close as possible to the published version following the terms of the journa

Quasi-random numbers for copula models

The present work addresses the question how sampling algorithms for commonly applied copula models can be adapted to account for quasi-random numbers. Besides sampling methods such as the conditional distribution method (based on a one-to-one transformation), it is also shown that typically faster sampling methods (based on stochastic representations) can be used to improve upon classical Monte Carlo methods when pseudo-random number generators are replaced by quasi-random number generators. This opens the door to quasi-random numbers for models well beyond independent margins or the multivariate normal distribution. Detailed examples (in the context of finance and insurance), illustrations and simulations are given and software has been developed and provided in the R packages copula and qrng

Generation of pseudo-random numbers

Practical methods for generating acceptable random numbers from a variety of probability distributions which are frequently encountered in engineering applications are described. The speed, accuracy, and guarantee of statistical randomness of the various methods are discussed