I was reading this paper: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2002698&download=yes
The author has the model presented here: http://modelingcommons.org/browse/one_model/3443#model_tabs_browse_info
But I am confused. There's all this build up of using quantum mechanics and quantum probability in the model, but the only thing he adds in the code is a Normally distributed stochastic variable he calls the "business cycle quantum game term." What is quantum about this? Why bother with all the quantum formalism if the end result is effectively just Gaussian white noise? I'm not formally trained in QM, so am I missing something? Example of relevant portion of code (from second link):
; Business Cycle Quantum Game Term:
ask patches [ set z random-normal 0 1.000 ] ; Gaussian wave packet reduction around the standardized fitness operator
ask patches [ set Mb (1 - m) * (b * x_t-1 - (b + 1) * x_t-1 ^ 3) + m * r_t-1 ] ; cubic map update (equation (18) with Mb := f_b,m)
ask patches [ set x_t (1 - epsilon - gamma) * Mb + epsilon * mean [ Mb ] of patches + gamma * z ] ; F update and result of the quantum wave packet reduction in terms of the fitness field operator eigenvalue
I also don't get this (from the second link):
There are three main advantages of the quantum approach to Evolutionary Financial Economics:
The explanatory effectiveness is expanded by the fact that one does not need any prior probability assumption, instead, one models the system's inter-relations and dynamics and from that result dynamical probabilities.
Probabilities can have evolutionary and game theoretical interpretations.
The adaptation process of a Complex Adaptive System (CAS) can be fully integrated with the probability formation and quantum game equilibrium assumptions.
Can classical methods not do any of these things?