Week 10 – Decision Support and Expert Systems

Last name First name Address Database Review Databases consist of Records Records consist of Fields Your HR record might look like this: Field Field Field Last name First name Address

Database Review If we kept customer information, it might contain these fields Last name First name Address Gender Age Income A decision Support System or Expert System might use these fields to help a manager make decisions.

Decision Support Given such customer information, a car rental company might make several decisions: Last name First name Address Gender Age Income Age – if under 21, do not allow Income – is under 10,00 OMR, do not allow Address – if country is US, recommend automatic transmission

Decision Design Structured decisions – simple rules, few exceptions: Age – if under 21, do not allow Income – if under 10,000 OMR, do not allow Address – if country is US, recommend automatic transmission Semi-structured decisions – more variables are involved, answers are less certain If age >70, do not allow rental, or charge higher fees for additional risk?

Decision Design Example of less structure – Life Insurance What factors would be involved in a decision to grant – and price – a life insurance policy? How would a computer system help you make such decision? What information might I keep in an insurance company database to help make such decisions in the future?

Decision Design Current commercial application – Amazon.com book recommendations Amazon currently makes recommendation about books you might want to buy. What information is it keeping in its database to help make such recommendations?

Decision Design Potential commercial application – Travel recommendations What if you were starting an e-business for leisure travel. What information would you collect on customers to help you make recommendations for the best place to travel?

Decision Design Potential commercial application – Supply chain optimization Pizza Hut managers don’t have time to order food and other materials for their store. What data would you keep to support a DSS for their stores?

Decision Support Virtues Why write these computer programs? Collected wisdom – decisions are recommended based on years of evidence – more than most employees have. Consistency – we get the same answer no matter which insurance underwriter or bank clerk we speak with Improved performance – some decisions will still be wrong, but there should be fewer of them

Expert Systems – Artificial Intelligence The point of these systems is to do the work of experts. They have been used for 40 years. They are procedural and algorithmic. They usually proceed through a series of questions and arrive at a specific response.

Expert Systems – Example Mycin The system diagnoses blood disorders. It involves some test results and some questions. For example – Have you traveled to a tropical country recently? What is the current white blood cell count? What is the current temperature? Each answer leads to another question in a kind of “tree.”

Expert Systems The systems are usually built by reviewing the procedures used by experts e.g. “How would a great pathologist diagnose a disease?” The systems can provide expertise when no local expert is available - e.g. a remote location The systems can store the information and procedures of experts who may be retiring – e.g. Nabisco baker

Expert Systems Example – a “Warren Buffett” investment system What data do you think he looks at when purchasing a stock? What procedure would he use?

Expert Systems Risks These systems are only as good as the information put into them. They do make mistakes. People might have legal objections to the systems if they are denied a loan or a job or are given incorrect medical advice.

Review What are 3 things from today’s class you want to remember?