1. 1 Ch 17: Alternative Decision-Support Systems

2. 2 What is an expert system? ‘The modeling, within a computer, of expert knowledge in a given domain, such that the resulting system can offer intelligent advise or take intelligent decisions’ British Computer Society The system should be able to justify the logic and reasoning underlying its advice or decision making.

3. 3 Expert knowledge Knowledge elicitation – gaining knowledge from an expert This knowledge may consist of many unwritten rules of thumb Knowledge bottleneck – difficulty of eliciting expert knowledge and transferring it to a computer program

4. 4 Representing knowledge in expert systems Production rules: IF (condition in database) THEN (action to update the database) Control structure or inference engine: determines what rule is to be tried next Forward chaining: following pathways through from known facts to resulting conclusions Backward chaining: choosing hypothetical conclusions and testing to see if the necessary rules underlying the conclusions hold true

5. 5 Successful systems must be able to interface effectively with their users in order to: 1. Gain the information needed to test the rules 2. Give understandable advice in plain English and justify the logic and reasoning underpinning the advice given or decision made

6. 6 Indicators of whether an expert system can be built within a reasonable time frame 1.That the subject domain has been formalized, e.g. do manuals exist? 2.That the subject domain is amenable to verbal expression Wright and Ayton (1987)

7. 7 Underwriting options

8. 8 Example of rule base of the geographical module

9. 9 Statistical models of judgment 1.Obtain data on the judgments an expert made and the variables he/she used to make the decision. E.g. an expert judges risk of firms failing:

10. 10 Statistical models of judgment 2. Fit a statistical (regression) model to the data to capture the weight the expert placed on each variable. E.g. p(a business will fail) = 0.3 Financial ratio1 + 0.1 Financial ratio2 + 0.05 Financial ratio3

11. 11 Statistical models of judgment 3. Use the model of the expert, rather than the expert him- or herself, to make predictions –this model is likely to outperform the expert because it ‘averages out’ inconsistencies in the expert’s judgment while retaining the ‘core’ of his, or her, expertise.

12. 12 Example of statistical modeling of judgment