This volume describes how mathematical probability theory can dispense with the inconveniences of measure theory and how its applications, especially in finance, can be liberated from the myth of inherent randomness in nature. In doing so, it provides a unique and revolutionary combination of probability and finance theories. Provides a foundation for probability based on game theory rather than measure theory. A strong philosophical approach with practical applications. Presents in-depth coverage of classical probability theory as well as new theory

Índice: Probability and finance as a game; Probability without measure; the historical context; the bounded strong law of large numbers; Kolmogorov's strong law; the law of the iterated logarithm; the weak laws; Lindeberg's theorem; the generality of probability games; finance without probability; game-theoretic probability in finance; games for pricing options in discrete time; games for pricing options in continuous time; the generality of game-theoretic pricing; games for American options; games for diffusion processes; the efficient-market hypothesis.