Using Neural Networks to Model the Behavior and Decisions of Gamblers,in Particular,Cyber-Gamblers

This article describes the use of neural networks (a type of artificial intelligence) and an empirical data sample of, inter alia, the amounts of bets laid and the winnings/losses made in successive games by a number of cyber-gamblers to longitudinally model gamblers’ behavior and decisions as to such bet amounts and the temporal trajectory of winnings/losses. The data was collected by videoing Texas Holdem gamblers at a cyber-gambling website. Six “persistent” gamblers were identified, totaling 675 games. The neural networks on average were able to predict bet amounts and cumulative winnings/losses in successive games accurately to three decimal places of the dollar. A more important conclusion is that the influence of a gambler’s skills, strategies, and personality on his/her successive bet amounts and cumulative winnings/losses is almost totally reflected by the pattern(s) of his/her winnings/losses in the few initial games and his/her gambling account balance. This partially invalidates gamblers’ illusions and fallacies that they can outperform others or even bankers. For government policy-makers, gambling industry operators, economists, sociologists, psychiatrists, and psychologists, this article provides models for gamblers’ behavior and decisions. It also explores and exemplifies the usefulness of neural networks and artificial intelligence at large in the research on gambling.