Learning to Make Decisions in Dynamic Environments

Sterman (1989) proposed that decision makers misperceive the feedback provided by dynamically complex environments, and questioned whether people can learn to make effective decisions in such environments. We provide empirical evidence of learning in a well-known dynamic environment called the beer game. We then describe a preliminary version of an instance-based, dynamic decision making model built using the ACT-R cognitive architecture. The model mimics the general patterns of human behavior observed for aggregate performance across trials and local performance within trials. Implications for research on dynamic decision making are summarized.