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wernerb November 13, 2019

Science is a gamble

Roulette is the origin of statistics

Source: Flickr

Everybody knows that the world we live in today has been transformed by science. Everything we do depends on science, from the buildings we live and work in to the vehicles we use to get around and the devices we use to entertain ourselves. Fun things we do every day like playing games in a casino online would be impossible without science.  What everybody doesn’t know, though, is that modern science owes gambling an enormous debt. That’s because science has a lot of complicated math under the hood, and a lot of that math would never have been developed without casinos and card games. 

A throw of the dice

In his book The Perfect Bet: How Science and Maths Are Taking the Luck Out of Gambling, mathematician Adam Kucharski describes several key areas where gambling gave science a helping hand. The first was the invention of probability theory. In the 16th century, there was no way to quantify luck. If someone rolled two sixes during a game of dice, people thought it was just good fortune. The Italian physican and gambler Gerolamo Cardano showed that dice games are not purely a question of luck: they can be tackled mathematically. His gambler’s manual of dice tables was the beginning of probability theory, which is essential for computer science.

Roulette and randomness

From clinical trials to experiments at CERN, modern scientists rely on statistics, and for that they owe everything to the game of roulette. During the 1890s, mathematician Karl Pearson was looking for data about random events, and he found it in Monte Carlo roulette results published in the Le Monaco newspaper. Pearson’s roulette analysis allowed him to develop statistical methods that are now a vital part of science. Scientists use them to see if their experiments are generating enough evidence to confirm their ideas.

Pay to play

The concept of “utility” is another gift from gambling to science. In 1738, mathematician Daniel Bernouilli used it to solve the puzzle of the St Petersburg Lottery. In this game, I toss a coin until heads appears. If its heads on first throw, I pay you $2; second throw, $4; third throw, $8, etc., doubling each time.  You could win really big; so how much would you pay to play? The puzzle was that even though the expected value of the game was huge, few people were happy to pay more than a few dollars to play.  According to utility, the less money someone has, the less they are likely to risk it to have a small chance of getting a big win. Today, the entire insurance industry runs on utility.

Computer simulations have their origin in cards

Source: Flickr

Science in the cards

In the 1940s, Stanislaw Ulam was playing the casino solitaire card game Canfield and wondered about the probability of the cards falling in a way that would let him win. Instead of calculating the possibilities he found it easier to just lay the cards out several times and see the results. In 1947, he and his colleague John von Neumann applied this “Monte Carlo method” to study nuclear chain reactions. Using repeated computer simulations helped them get to grips with a problem too complex for traditional math. Now the Monte Carlo method is used everywhere from computer graphics to disease outbreak analysis.

The games of life

The field of game theory plays a vital role in economics, artificial intelligence and evolutionary biology. Mathematician John van Neumann sucked at poker so he analysed the game mathematically to find out what strategies would work best. Working out what cards might be dealt wasn’t enough – he also needed to anticipate his opponent’s moves. This analysis developed into game theory, which uses math to predict the strategy and decision-making of different players. Game theory has even made it into the movies – it features in “A Beautiful Mind”, the story of John Nash, the brilliant but mentally troubled mathematician who took von Neumann’s ideas further.

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