Optimal Exit From A Deteriorating Project With Noisy Returns
Probability in the Engineering and Informational Sciences
Identifying and predicting economic regimes in supply chains using sales and procurement information
Proceedings of the 11th International Conference on Electronic Commerce
Proceedings of the 11th International Conference on Electronic Commerce
Detecting and forecasting economic regimes in multi-agent automated exchanges
Decision Support Systems
Predicting the Next Big Thing: Success as a Signal of Poor Judgment
Management Science
Demand Forecasting Behavior: System Neglect and Change Detection
Management Science
Real-Time Tactical and Strategic Sales Management for Intelligent Agents Guided by Economic Regimes
Information Systems Research
Biased Judgment in Censored Environments
Management Science
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Many decision makers operate in dynamic environments in which markets, competitors, and technology change regularly. The ability to detect and respond to these regime shifts is critical for economic success. We conduct three experiments to test how effective individuals are at detecting such regime shifts. Specifically, we investigate when individuals are most likely to underreact to change and when they are most likely to overreact to it. We develop asystem-neglect hypothesis: Individuals react primarily to the signals they observe and secondarily to the environmental system that produced the signal. The experiments, two involving probability estimation and one involving prediction, reveal a behavioral pattern consistent with our system-neglect hypothesis: Underreaction is most common in unstable environments with precise signals, and overreaction is most common in stable environments with noisy signals. We test this pattern formally in a statistical comparison of the Bayesian model with a parametric specification of the system-neglect model.