A Generalized Model for Automating Judgmental Decisions

This paper presents a model that develops a concise representation of a decision making task with a fixed number of alternatives. The methodology relies upon a conceptual information processing system analogous to that employed by a novice decision maker during the evolution of his decision paradigms. The model requires only the specification of relevant input stimuli and a sample of stimuli-response relationships in order to develop a representation of the task. The resulting representation is in a form which allows a predicted decision to be made given only the relevant stimuli. A mechanism is presented for efficiently operationalizing the task representation. An empirical study is presented which demonstrates the learning and performance capabilities of the model in the confines of an overdraft processing task.

• 저널Management Science · 22(8) · 841–851
• 토픽AI-based Problem Solving and Planning · Artificial Intelligence
• DOI10.1287/mnsc.22.8.841
• 저자Michael Parks, Nicolai Siemens, Hugh J. Watson