Whats going on everybody? So I don't know if anyone else is counting but it 4 weeks left (est. 28 days) till the semester is over and I'm graduated. Don't know about anyone else but I'm excited. Anyways on to this all important blog thing. So the $64,000 question is how deep is your decision tree? For those who have no idea what I'm talking about http://www.mindtools.com/dectree.html
should be about to help you out. The basis of the theory is to help you choose between several different action opportunities. By dong this you can see the risks, rewards, and outcomes of each decision. Sounds pretty cool, uh? Well I'll spare you the article because I'm sure you'll read it but I will pose a issue. If you have several options, and each option has several risks, rewards, and options, you might be at it all day. But it could also make a good topic for a small company meeting when a decision needs to be made. More minds at a problem might find different solutions and options to solving/making a decision. But it could also be used on a personal level I believe. Like buy a car which is what I'm going through right now. Now I can buy a car which will allow me to get to new places, around town, and no longer depend on the MetroLink and public transportation, allowing me to get a job (potentially better paying, hope so) away from the Metro. But on the other hand, not getting a car could save me money but limit my traveling to where I can get on and off a bus or train. I could make a easy decision tree for all this but again I'll spare the reader. So go out and make a decision tree for yourself and see how deep it really goes. Cheers
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One of the problems of decision trees is that they get very large very quickly and are difficult to optimize. There are techniques, including dynamic programming. But, often heuristics are used because of the complexity. Suppose you were considering your car question. What kinds of tools would you want included in the DSS to help you -- regardless of whether you used an algorithm or heuristic?
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