Neural Networks

While the term neural networks may be unfamiliar to many organizational psychologists, exciting new applications of artificial intelligence are attracting notice among organizational behavior researchers. In ““Neural Networks in Organizational Research: Applying Pattern Recognition to the Analysis of Organizational Behavior”“, authors David Scarborough and Mark Somers bring researchers, academics, and practitioners up to speed on this emerging field, in which powerful computing capabilities offer new insights into longstanding, complex I/O questions such as employee selection and behavioral prediction. Neural networks mimic the way the human brain works, using interconnected nodes and feedback loops to ““learn”” to recognize even subtle patterns in vast amounts of data. They can process data far more quickly and efficiently than conventional techniques can, and produce better empirical results. They are especially useful for modeling nonlinear processes. The book traces the development of this methodology and demonstrates how it opens up new ways of thinking about traditional problems. Academic researchers will gain a design template for studying both the linear and non-linear elements of a given problem, and thus enhance their own research.