Gambling behavior in two-outcome multistage betting games

Abstract In a favorable multistage gambling process, a biased coin with the probability of “heads” p > 1 2 is tossed repeatedly. Before each toss, a gambler is required to bet a fraction of his capital on “heads”. His problem is to choose a betting policy that is optimal with respect to some criterion. A model yielding a proportional stationary Markov betting policy is presented and generalized. The sensitivity of the model to departures from optimality is investigated. Fifty-one subjects participated individually in a multistage gambling experiment. While most subjects performed efficiently in terms of the model, the model was not strictly supported, since both the current amount of capital and the pattern of previous outcomes affected subjects' betting decisions.

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