The field of decision theory and its companion methodology of decision analysis deal with the merits and making of decisions. As developed by philosophers, economists, and mathematicians over some 300 years, these disciplines have developed many powerful ideas and techniques, which exert major influences over virtually all the biological, cognitive, and social sciences. Their uses range from providing mathematical foundations for microeconomics to daily application in a range of fields of practice, including finance, public policy, medicine, and now even automated device diagnosis.
In spite of these remarkable achievements, the tools of traditional decision theory have not proven fully adequate for supporting recent attempts in AI to automate decision making. The field of qualitative decision theory aims to provide better support for automation efforts by developing qualitative and hybrid qualitative-quantitative representations and procedures that complement and improve the quantitative approach's ability to address the full range of decision-making tasks in the way such tasks appear within larger activities of planning, learning, and collaboration.
The following brief survey of qualitative decision theory seeks to stimulate new work in the area and alert researchers in other areas to topics of mutual interest. We first illustrate some of the motivations for pursuing more qualitative approaches and continue by examining the nature of traditional decision theory and analysis. We then identify a number of technical issues and topics for investigation. We provide sketches of representative results and work concerning these matters. Much of this work is incomplete and preliminary, providing many opportunities for further research. The concluding remarks seek to reflect on the available results to help set the context for future studies.
A Challenging Example
Naive and expert humans regularly and routinely solve decision problems that challenge the formalizations and methods of decision making that predominate in economics and AI.
To illustrate this point, consider someone, call him Aethelred, who goes to meet with a financial planner. Aethelred brings a number of preferences to the meeting, some of them implicit and unexamined, some pretty abstract or generic, and many of them competing. He feels restless and dissatisfied and would rather retire early, although he will not be forced to retire on account of age. He is more timid about financial risk than he was when he was younger and unsure about the financial markets. He feels increasingly uneasy about his health (one reason he wants to retire early).
When Aethelred meets with the planning expert, he somehow focuses on one part of this mix of preferences and beliefs and produces a goal. He says, "I want to retire at age 60." This opening remark clearly does not provide a total picture of his state of mind, nor does traditional decision theory provide a way of expressing the goal formally. A good advisor might explore what happens through planning from the supposed goal but will also be quite willing to challenge the goal itself. Suppose, for example, that the expert describes some scenarios based on the announced goal, and Aethelred is unhappy with them all. The expert asks him why he wants to retire at 60. Aethelred finds he can't produce any very compelling reason. He chose the number 60 arbitrarily. His reasons for preserving his salary and employee benefits are much more compelling than his reasons for early retirement. The expert points out that a large proportion of early retirees are restless and dissatisfied with their retirement. At the end of the discussion, Aethelred's preferences (of the degrees of importance he attaches to preference factors) have changed. He decides to do nothing for at least five years and to try harder to enjoy his job before rethinking the matter. The expert has neither brainwashed not compelled him but has instead helped Aethelred to reexamine and readjust his preferences. …