Intelligent Systems to Support Human Decision Making

Background

A decision is a reasoned choice between alternatives (Simon, 1955), while decision making is the process of choosing between alternatives in order to satisfy a goal or goals (Turban & Aaronson, 1998). Decisions can be characterized as structured, semi-structured or unstructured (Turban & Aaronson, 1998). Structured decision problems are routine and can be solved with a standard model. Unstructured decision problems have no agreed-upon criteria or solution and rely on the preferences of the decision maker. In between these two types of problems, there is a wide range of semi-structured problems that generally have some agreed-upon parameters and yet require human input or preferences for a decision. Semi-structured decision problems are particularly amenable to decision support since they require a combination of user guidance and analytical methods to develop alternatives based on criteria and potential solutions.

Data can also be considered as structured, semi-structured or unstructured. Structured data are data that can be represented in a format or schema such as a relational database. Numeric data, and textual data that can be converted to numeric form such as dates, have an underlying structure. At the other end of the spectrum are unstructured data with no underlying structure. For example, audio files, video files, and visual images such as photographs are unstructured data. In-between there are semi-structured data in which the underlying structure is contained within the data themselves, a characteristic sometimes called self-describing (Buneman, 1997). For example, disparate databases using different schema may have the need to exchange data. Decision makers may exist in complex environments, with structured, semi-structured or unstructured decisions that require structured, semi-structured or unstructured data.