ABSTRACT
This chapter follows a path through theory development from the original linear model for simple binary choices to models which incorporate internal representations, along the way discussing expertise and optimality, and exemplar representation. The linear model is an extraordinarily compelling yet simple way of accounting for how people learn to assign weights to cues in a decision environment, and its scope suggests that error-driven learning in some form or other plays a central role in the learning process. The model often predicts convergence to optimal behaviour in the long run, and this appears to be consistent with expert performance. In many settings, both in the laboratory and in real life, experts seem able to iron out many biases such as base-rate neglect. The Recognition-Primed Decision Making model which emerges from this approach blends rapid, intuitive processing based on the availability and representativeness of situations and cues with more deliberative mental simulation to produce satisficing responses in real-world situations.
