|Title||An alternative modelling technique for the reduction of error in decision support spreadsheets|
|Publication||Cardiff Metropolitan University|
Spreadsheet applications are currently the most prevalent end user tool in organisations across the world. Surveys on spreadsheet use show spreadsheets are used as decision making tools in a range of organisations from credit liability assessment in the business world to patient cardiovascular-anaesthesia risk in the medical community.
However, there is strong evidence to suggest a significant proportion of spreadsheets contain errors that affect the validity of their operation and results. In addition most end users receive no relevant information systems training and consequently have no concept of creating reliable software. This can result in poorly designed untested spreadsheets that are potentially full of errors.
This thesis presents an alternative novel modelling technique to decision support spreadsheets. The novel technique uses attribute classifications (user defined examples) to create a model of a problem. This technique is coined "Example Driven Modelling" (EDM).
Through experimentation, the relative benefits and useful limits of EDM are explored and established. The practical application of EDM to real world spreadsheets demonstrates how EDM outperforms equivalent spreadsheet models in a medical decision making spreadsheet used to determine the anaesthesia risk of a patient undergoing cardiovascular surgery.
Spreadsheet errors are often due to a mismatch between man and machine. Human factors play a significant role in spreadsheet errors and have been largely ignored by the wider spreadsheet community.
In the proposed new paradigm, the human would pattern match and generate real world examples, the computer would use its ability of mathematical manipulation and logical deduction to build a model from the examples provided by the user.