Learning to make decisions under uncertainty:
The contribution of qualitative reasoning

John Fox

The majority of work in the field of human judgement and decision making under uncertainty is based on the use and development of algebraic approaches, in which judgement is modelled in terms of mathematical choice functions. Such approaches provide no account of the mental processes underlying decision making. In this paper we explore a cognitive model (implemented within {\sc cogent}) of decision making developed in order to account for subject performance on a simulated medical diagnosis task. Our primary concern is with learning, and empirical results on human learning in the modelled task are also reported. Learning in the computational model shares many qualitative features with the human data. The results provide further support for cognitive (i.e., non-algebraic) approaches to decision making under uncertainty.

No previous abstract Workshop 2 Integrating interactive activation into COGENT