The objective of this project is to develop mathematical structure that will be connection of evolutionary game paradigm with life history theory. In classical life history optimization models there are no interactions among individuals or density dependence: "Life history evolution usually ignores density and frequency dependence. The justification is convenience, not logic or realism" (Stearns 1992) On the other hand, in classical game theoretic models there is no age or stage structure. Payoff describes averaged lifetime activity of an individual, which can be found for example in Cressman 1992: "...an individual's strategy is fixed over lifetime or, alternatively, the life history of an individual is its strategy.". Objectives of the project can be divided into three branches: a) Development of method of decomposition of entire population into subgroups (multipopulation replicator dynamics). This generalization of standard replicator dynamics approach will allow to game theoretic modeling of structured populations, divided into subclasses (different species, sexes, age or stage classes). b) Description of the dynamics of turnover of individuals. Explicit consideration of births and deaths instead of fixed Malthusian parameter will allow to include tradeoffs between mortality and fecundity to dynamic game theoretic models c) Derivation of game theoretic payoff functions due to the methods of life history theory. This approach will allow to formulate crossover problems where life history traits and phenotypic may affect interactions among individuals. Classical optimization of life histories approach investigates how ecology affects evolution of life history parameters. New approach will allow to model how life history parameters affect ecology of a population by determining outcomes of interactions between individuals.