In order to generate uniform distributed sample on the fly, the C++ Boost library was applied with its powerful statistical distribution templates. For generating a uni form distributed sample, the Mersenne Twister generator was implemented used. The Mersenne Twister is a relatively new and complex algorithm for generating random numbers described in detail elsewhere. It has been designed with Monte-Carlo appli cations in mind but it is of course useful for any application which equires uniform random samples The Boost implementation of this generator is available called
the mt19937. Both the algorithm itself and the Boost implementation have a signifi cant number of parameters; and the most frequently used standard parametrization is the MT19937 version and this parametrization is defined in a header file for easy use.Seeding from a simple integer number is likely to be sufficient for many applications such as this simulation.
In addition, for all agents’ decision making process which relate to the logistic dis tribution; the Boost library was again used for generating logistic distributed samples on the fly. One thing to note is that for the two parameters of logistic distribution
{ location and scale, will also be generated uniformly within a range on the fly, inorder to give each agent a different decision making metric. These two parameters,will be kept track of agents as class attributes and will only be modified when the agents’ marital status are changed; because the parameters of logistic distribution is associated with agents’ marital status only. Although C++ now is the standard tool for use in production systems in the filed of computational finance, there are very few standard yet widely used C++ libraries.Boost is the best candidate for such a library and learning and applying to this particular thesis has brought the author with significant benefits.

boost c++