Feedback Delays: How Can Decision Makers Learn Not to Buy a New Car Every Time the Garage Is Empty?

Decision makers in dynamic environments (e.g., stock trading, inventory control, and firefighting) learn poorly in experiments where feedback about the outcomes of their actions is delayed. In searching for ways to mitigate these effects, this paper presents two computational models of learning with feedback delays and contrasts them against human decision-makers' performance. The no-memory model hypothesizes that decision makers always perceive feedback as immediate. The with-memory model hypothesizes that, over time, decision makers are able to develop internal representations of the task that help them to perform with delayed feedback. As borne out by human subjects, both models predict that a display of past history improves learning with delay and that increasing delay increasingly degrades performance. Even though the length of training in this task exceeds that used in many laboratory-based dynamic tasks, neither the two models nor the subjects are able to effectively learn without decision aids when faced with feedback delays. When given an amount of training that more closely approximates that provided in functioning dynamic environments, the with-memory model predicts that human decision makers may learn without decision aids over the long term if feedback delays are simple. These results raise several issues for continued theoretical investigation as well as potential suggestions for training and supporting decision makers in dynamic environments with feedback delays. Copyright 2000 Academic Press.