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Automated decision-making systems have been deployed in many industrial, commercial, and financial applications. The needs for such systems are usually motivated by requirements for variation reduction, capacity increase, cost and cycle time reduction, and end-to-end traceability of the transaction or product. Before we can use any automated decision-making system in a production environment we must develop a strategy to insure high quality throughout its entire lifecycle. We need to guarantee its performance through a rigorous Design for Six Sigma process (DFSS). This process includes validation, tuning, and production testing of the system. Once the system is in production we must monitor and maintain its performance over its lifecycle. In this paper we will outline the Six Sigma process that led to the deployment of an automated decision-making system in one of the General Electric Financial Assurance businesses. Copyright © 2005 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Despite much progress in brain and cognitive sciences, attempts to connect brain function to cognition are hampered by the large explanatory gap between psychology and neurobiology. In recent years, a neurocomputational perspective has emerged as the most promising approach to integrating brain and mind. According to this perspective, the brain is a special sort of computer, a system of many parallel neural networks whose operation underlies cognition. In this paper, we present this neurocomputational perspective and examine the ways in which this new approach to explaining our mental skills differs from earlier ones. In particular, we examine its emerging insights into two domains. First, we explore the neurocomputational approach to decision-making, the adaptive guidance of behavior in the satisfaction of life maintenance goals. Decision-making is central to all mobile creatures in an uncertain environment, and this approach reveals a surprising conservation of decision-making strategies across many species. We then examine the neurocomputational approach's new insights into characterizing cognitive development. In particular, this approach offers the new framework of self-organization to characterize the complex interaction between neural developmental programs and the environment, a framework that has important implications for understanding early intervention. MRDD Research Reviews 1999;5:86–99. © 1999 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]