Machine Learning
Detecting Regime Shifts: The Causes of Under- and Overreaction
Management Science
A predictive empirical model for pricing and resource allocation decisions
Proceedings of the ninth international conference on Electronic commerce
Identification and prediction of economic regimes to guide decision making in multi-agent marketplaces
Flexible decision control in an autonomous trading agent
Electronic Commerce Research and Applications
Detecting and forecasting economic regimes in multi-agent automated exchanges
Decision Support Systems
Identifying and forecasting economic regimes in TAC SCM
AMEC'05 Proceedings of the 2005 international conference on Agent-Mediated Electronic Commerce: designing Trading Agents and Mechanisms
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We investigate the effects of adding procurement information (component offer prices) to a sales-based economic regime model, which is used for strategic, tactical, and operational decision making in dynamic supply chains. The performance of the regime model is evaluated through experiments with the MinneTAC trading agent, which competes in the TAC SCM game. We find that the new regime model has a similar overall predictive performance as the existing model. Regime switches are predicted more accurately, whereas the prediction accuracy of dominant regimes is slightly worse. However, by adding procurement information, we have enriched the model and we have further opportunities for applications in the procurement market, such as procurement reserve pricing.