Heuristic Rules

1. Intro

Any rule or set of rules that reduce the uncertainty of an event is an heuristic rule(s) or expert system.  Heuristic rules can be used in a stand alone system or coded as a variable into a model.  There are cases where heuristic rules are the best choice when building a model.

When heuristic rules are the best choice:

1. The process is too non-linear for other forms of modeling.

2. There is good common sense knowledge of processes.

3. The market does not trust statistical models.

Do not discount heuristic rules when building models.  They can be just as powerful in forecasting as any math based model. Also by using hybrid system with both modeling and heuristics rules you can improve your forecast dramatically. Heuristics rules excel when there are a plethora of complex or nonlinear relationships, exactly where statistical models can fall apart.

2. Building a Heuristic model

a. Experts

Talk to people!  You may know statistics but that does not mean you know everything about the problem you are trying to solve. Find an expert in the field you are analyzing and listen.  Here is an example, in one of my prior jobs a non-technical person researching prison populations noted that the incarceration rates always increase when a new prison is built, mainly due to the tremendous political pressure to fill new prisons.  This simple heuristic rule ends up explaining a great deal of the variation in incarceration rates.

b. Data Analysis

Look at your data!  The biggest mistake a modeler can do is ignore his or her data.  Simple rules may emerge out of just looking at plots or frequencies that can have enormous predictive power.  Once I noticed the system was shutting down periodically once a day. A simple frequency showed a relationship between output and whether it was noon.  Obviously the system was shutting down for lunch.  This is common sense, but we had not thought of it till we looked at the data.  Knowing this great improved the efficiency of the model.

c. Data Mining

Data mining is ideal in discovery of heuristic rules.  Trees, discussed in the data mining sections, are well designed to discover both complex and simple rules.