Dynamic programming and gambling models

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Abstract
Dynamic programming is used to solve some simple gambling models. In particular we consider the situation where an individual may bet any integral amount not greater than his fortune and he will win this amount with probability p or lose it with probability 1 — p. It is shown that if p ≧ ½ then the timid strategy (always bet one dollar) both maximizes the probability of ever reaching any preassigned fortune, and also stochastically maximizes the time until the bettor becomes broke. Also, if p ≦ ½ then the timid strategy while not stochastically maximizing the playing time does maximize the expected playing time. We also consider the same model but with the additional structure that the bettor need not gamble but may instead elect to work for some period of time. His goal is to minimize the expected time until his fortune reaches some preassigned goal. We show that if p ≦ ½ then (i) always working is optimal, and (ii) among those strategies that only allow working when the bettor is broke it is the bold strategy that is optimal

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