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## Friday, May 29, 2020

Intriguing.  We spent lots of time updating ow we generated random numbers,  sometimes laboriously checking internal random number generators.    I can think of ways this could be used to generate numbers more clearly understandable to decision makers.    Since many real systems use numbers that are 'loaded' by context.

Algorithm quickly simulates a roll of loaded dice    by Steve Nadis, Massachusetts Institute of Technology in TechExplore

A new algorithm, called the Fast Loaded Dice Roller (FLDR), simulates the roll of dice to produce random integers. The dice, in this case, could have any number of sides, and they are “loaded,” or weighted, to make some sides more likely to come up than others. Credit: Jose-Luis Olivares, MIT

The fast and efficient generation of random numbers has long been an important challenge. For centuries, games of chance have relied on the roll of a die, the flip of a coin, or the shuffling of cards to bring some randomness into the proceedings. In the second half of the 20th century, computers started taking over that role, for applications in cryptography, statistics, and artificial intelligence, as well as for various simulations—climatic, epidemiological, financial, and so forth.

MIT researchers have now developed a computer algorithm that might, at least for some tasks, churn out random numbers with the best combination of speed, accuracy, and low memory requirements available today. The algorithm, called the Fast Loaded Dice Roller (FLDR), was created by MIT graduate student Feras Saad, Research Scientist Cameron Freer, Professor Martin Rinard, and Principal Research Scientist Vikash Mansinghka, and it will be presented next week at the 23rd International Conference on Artificial Intelligence and Statistics.

Simply put, FLDR is a computer program that simulates the roll of dice to produce random integers. The dice can have any number of sides, and they are "loaded," or weighted, to make some sides more likely to come up than others. A loaded die can still yield random numbers—as one cannot predict in advance which side will turn up—but the randomness is constrained to meet a preset probability distribution. One might, for instance, use loaded dice to simulate the outcome of a baseball game; while the superior team is more likely to win, on a given day either team could end up on top.... "