Many situations involving strategic interaction between agents involve a continuos set of choices. Therefore it is natural to model these problems using continuous space games. Consequently the population of agents playing the game will be represented with a density function defined over the continuous set of strategy choices.
Simulating evolution of this population is a challenging problem. We present a method for simulating replicator dynamics in continuos space games using sequential Monte Carlo methods. The particle approach to density estimation provides a computationally efficient way of modeling the evolution of probability density functions.
Finally a resampling step and smoothing methods are used to prevent particle degeneration problem associated with particle estimates. Finally we compare and contrast the resulting algorithm for simulation with genetic programming techniques.