  This dissertation highlights connections between the fields of neural
networks, game theory and time series generation. The concept of
antipredictability is explained, and the properties of time series that are
antipredictable for several prototypical prediction algorithms (neural
networks, Boolean funtions etc.) are studied. The Minority Game provides a
framework in which antipredictability arises naturally. Several variations of
the MG are introduced and compared, including extensions to more than two
choices, and the properties of the generated time series are analysed. A
learning algorithm is presented by which a neural network can find a good mixed
strategy in zero-sum matrix games. In a certain limit, this algorithm is a
stochastic variation of the "fictitious play" learning algorithm.
