Discrete Gambling and Stochastic Games (Stochastic Modelling and Applied Probability (32))

✍ Scribed by Ashok P. Maitra, William D. Sudderth

Publisher: Springer
Year: 2011
Tongue: English
Leaves: 248
Series: Stochastic Modelling and Applied Probability (32) (Book 32)
Edition: Softcover reprint of the original 1st ed. 1996
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

The theory of probability began in the seventeenth century with attempts to calculate the odds of winning in certain games of chance. However, it was not until the middle of the twentieth century that mathematicians de veloped general techniques for maximizing the chances of beating a casino or winning against an intelligent opponent. These methods of finding op timal strategies for a player are at the heart of the modern theories of stochastic control and stochastic games. There are numerous applications to engineering and the social sciences, but the liveliest intuition still comes from gambling. The now classic work How to Gamble If You Must: Inequalities for Stochastic Processes by Dubins and Savage (1965) uses gambling termi nology and examples to develop an elegant, deep, and quite general theory of discrete-time stochastic control. A gambler "controls" the stochastic pro cess of his or her successive fortunes by choosing which games to play and what bets to make.

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