ALL THINGS BIG AND SMALL 2.0

I am beginning to believe that most of the phenomena in our world are too complex to be modeled using closed-form analytic models. Even if you could identify the variables, the inter-relationship between the variables are forever changing, and the variables themselves might change. The natural tendency is to turn to computer simulation modeling, where we generate data for the simulation, run the simulation and analyze the results.. From this we can calculate probability of outcomes and the confidence intervals. Computer simulation is widely used in many fields from the simulation of manufacturing processes, to military planning and econometric forecasting, and the valuation of financial derivatives. On a more pedestrianlevel it can help estimate waiting times in queues, estimate the resources required for a project or help with decision-making when there are several options. The exponential increase in the speed and power of today's computers makes simulation less of a chore than i...

It is an accepted principle that in attempting to build a model of something in the real world, one should have a deep knowledge of the subject that is being modeled. Because domain expertise is crucial for choice of input variables, as well as clear statement of the relationship between the variables, as well as the quantification of the coefficients [parameters] for each equation of the model. However, in recent times, there is some serious study of blackbox approaches to modeling. A blackbox type of model is one where [1] choice of Input need not be restricted to those which are justified to be inputs according to the theoretical principles of the subject to be modeled. [2] A clear statement of the relationship between input variables is not absolutely necessary, instead leaving it to the blackbox to work out the relationship. [3] For [2], non-parametric methods machine-learning methods such as neural nets, fuzzy logic and genetic algorithms are used to classify, detect patterns or ...

Blackbox models of complex situations are tempting. Without the need to specify parameters, and not limited by choice of variables, you can throw in everything including the kitchen sink, and see how the model performs. But in real life, it is not that simple. With every variable that you throw in, you are increasing the complexity of the model exponentially as well as the computational workload. Although there are algorithms for dimension reduction as well as for assessing the significance of an input towards model accuracy, nothing beats a good old human being with expertise in the subject domain, for initial choice of input variables. Let's do an academic exercise for the building of a model that (1) hopefully gives an objective, 'true' valuation of a house. (2) predict the future house price. We will attempt to build our model using a Neural Network, or a hybrid Neural with Genetic Algorithms for the last mile of calculation, to avoid local optima. Any human expert in r...