The interaction and cooperation between computers and the human brain is at a crossroad. There are some who believe that decision support systems should be completely automated. There are others who believe that there are many areas of business, technology, and science that have not been discovered yet, and, hence, only part of a decision support system can be automated. I subscribe to the latter proposition.
Computer science is, at its core, an attempt to replicate the processing, reasoning, and learning processes of the human brain. Therefore, an understanding of the human brain is fundamental to determine the next steps into advances in the area of business analytics (i.e. the fusion of technology, science, and business). See http://atomai.blogspot.com/2008/06/intersection-of-business-science-and.html; and http://atomai.blogspot.com/2008/05/attention-system-of-human-brain.html.
Visualization is an important method that the human brain uses to perceive and make decisions. See http://atomai.blogspot.com/2008/06/on-visualization-of-mathematics.html. Classification into groups with similar characteristics, or data mining, is another method to make decisions. Depth and movement perception based on prior experience for the human brain is the equivalent of what we call in science predictive modeling or forecasting. The amalgamation of these elements in a decision support system is equivalent to the way the human brain makes decisions.
History teaches us that some discoveries provide a quantum leap of understanding. The earth revolving around the sun, gravity, and the theory of relativity are examples of these types of discoveries. On the other hand, most decision making is incremental in nature. See http://atomai.blogspot.com/2008/05/kaizen-analytics-continuous-improvement.html. For example, buying securities collateralized with real estate might not be the best investment in the United States right now; buying short-term oil contracts in the commodities market might be a more profitable investment.
We have reached a point in our technological development that we can put together genetic algorithms, data mining techniques, grid or cloud computing, and visualization techniques using gaming technologies to automate some areas of decision support systems to reduce operational costs. For an interesting perspective on how gaming techniques use predictive modeling to forecast movement, see http://web.cs.wpi.edu/~claypool/courses/4513-B03/papers/games/bernier.pdf. Also, the joining of these techniques will facilitate a decision support system to make both incremental as well as quantum leap discoveries in many business areas.
