Description: The article introduces the concept of overfitting in predictive modeling and illustrates how it hinders our ability to predict new data well.

Description: A discussion on machine learning and how it can be used for forecasting.

Description: This article discusses techniques used to create risk scores to help determine appropriate primary care panel sizes for a provider organization.

Description: The Possible Role of Convolutional Neural Networks in Mortality Risk Protection

Description: This paper explores how health care complexity challenges us to expand our repertoire of actuarial methods—and even recast our trend driver paradigm—in order to do our jobs better and maintain our credibility.

Description: This review discusses the notion that a diverse group of individuals, namely, a crowd, has greater wisdom and can make better decisions than a very accomplished individual expert or a very smart group of individuals who are similar to each another and applies this thinking to business in general.

Description: In Complexity: A Guided Tour, readers are given a short history lesson on the roots of dynamical systems theory, chaos and prediction.

Description: Discusses the three complexity science sessions at the Annual Meeting that were sponsored by the Section.

Description: The author discusses the field of artificial intelligence, as well as some of the key definitions along with some possible applications.

Description: Bayesian networks are one of many models that are useful for predictive modeling. This article introduces the concept of Bayesian network and shows how time-series data can be modeled for a Bayesian network.