An algorithm that helps decide whether you should be allowed to play a casino game can be used to determine whether you will be a good or bad gambler, a new study has found.

    Key points:Researchers say the algorithm, dubbed the GameStop-Tower of Doom, can predict if a person will be likely to make a mistakeThe algorithm can even predict whether a person has a “high” or “low” risk of gamblingSource: ABC News (AAP)The algorithm was developed by a computer scientist who is part of the Australian Institute of Company Directors (AICD) and the University of Queensland.

    The algorithm, named the GameStamp-Towers of Doom or G-TOD, is based on a model developed by Professor Daniel Sperling, of the University at Adelaide, and his colleagues.

    “The model is based not on a set of rules, but on a suite of factors that can be adjusted and altered to generate a random sample,” Professor Sperles said.

    “What’s unique about this algorithm is that it can be trained using large datasets.”

    Researchers from the University’s Department of Mathematical and Computational Sciences, led by Professor Sattar, said the G-Stands of Doom algorithm can be programmed to generate thousands of “towers”, and then used to predict whether someone is likely to get an advantage or disadvantage from playing a casino.

    The algorithm works by taking the sum of all the games played by a player, and then applying a model to determine if a player has a high or low chance of playing a particular game.”

    It is an algorithm which can generate random samples of data from tens of millions of games and use that to generate its predictions.”

    The algorithm works by taking the sum of all the games played by a player, and then applying a model to determine if a player has a high or low chance of playing a particular game.

    It then uses this data to determine how many players there are who will play a given game.

    The results of the study were published in the journal Computers in Human Behavior, and are the first to show how the GStD algorithm works.

    It has been trained using over a billion games, which can be viewed on the University Games website, and has been used to identify players who are likely to play games which may result in higher or lower outcomes.

    “There’s a lot of data in there which we can use to train the model,” Professor Kumar said, adding that the algorithm can also be used for prediction of a range of outcomes, including those that could happen at random.

    “We can look at whether a given player is likely [to get a particular outcome] based on the outcomes that they play.”

    The research also shows how the algorithm could be used by casinos to predict who is likely at random to make poor decisions, which could potentially make them a bad gambier.

    Professor Kumar said the algorithm is used to calculate how many games a person plays, and the amount of money they have in their bank account.

    “If you’re playing $1000 games a week, then you’re likely to have $1000 in your bank account, so you can afford to gamble,” he said.

    The study also showed that the algorithms ability to predict “win probabilities” for particular types of games was “good at predicting the odds of winning at certain types of tournaments”.

    “The best predictor for winning at poker tournaments is the poker player, but you can use the same model for football or basketball, which we find is also good,” Professor Kripal Singh said.

    Dr Kumar said while the algorithm was not perfect, it could be improved in ways that would improve its usefulness.

    “It’s still an open question whether we can apply it to predict which games a particular person is likely in, or to predict where they will make mistakes in particular games,” he told ABC News.

    “But that’s the beauty of algorithms.

    They can be applied to a range and to a wide range of different kinds of data, and we think we’ve found an algorithm that can do it.”

    Professor Kumar’s team is now working on developing a more powerful version of the algorithm.

    “In the meantime, we’re continuing to improve our models for predicting outcomes,” he added.

    Topics:business-economics-and-finance,casinos-and_lifestyle,human-interest,education,law-crime-and/or-justice,casino-industry,research,human,psychology,law,world-politics,australiaFirst posted September 25, 2018 07:53:23Contact Paul WhiteMore stories from Victoria


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