Cognitive Heuristics and Feedback in a Dynamic Decision Environment

Abstract

Research on cognitive processes in decision making has identified heuristics that often work well but sometimes lead to serious errors. This paper presents an investigation of the performance of heuristics in a complex dynamic setting, characterized by repeated decisions with feedback. There are three components: (1) A simulated task resembling medical decision problems (diagnosis and treatment) is described. (2) Computer models of decision strategies are developed. These include models based on cognitive heuristics as well as benchmark strategies that indicate the limit of the heuristic strategies' performance. The upper benchmark is based on statistical decision theory, the lower one on random trial and error. (3) Selected task characteristics are systematically varied and their influence on performance evaluated in simulation experiments. Results indicate that task characteristics often studied in past research (e.g., symptom diagonosticity, disease base-rates) have less influence on performance relative to feedback-related aspects of the task. These dynamic characteristics are a major determinant of when heuristics perform well or badly. The results also provide insights about the costs and benefits of various cognitive heuristics. In addition, the possible contribution of this research to the design and evaluation of decision aids is considered.

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