Gambles in casinos are usually set up so that the casino makes a profit in expectation--as long as gamblers play honestly. However, some gamblers are able to cheat, reducing the casino's profit. How should the casino address this? A common strategy is to selectively kick gamblers out, possibly even without being sure that they were cheating. In this paper, we address the following question: Based solely on a gambler's track record, when is it optimal for the casino to kick the gambler out? Because cheaters will adapt to the casino's policy, this is a game-theoretic question. Specifically, we model the problem as a Bayesian game in which the casino is a Stackelberg leader that can commit to a (possibly randomized) policy for when to kick gamblers out, and we provide efficient algorithms for computing the optimal policy. Besides being potentially useful to casinos, we imagine that similar techniques could be useful for addressing related problems--for example, illegal trades in financial markets.
['Troels Bjerre Sørensen', 'Melissa Dalis', 'Joshua Letchford', 'Dmytro Korzhyk', 'Vincent Conitzer']
Beat the cheater: computing game-theoretic strategies for when to kick a gambler out of a casino