Daniel S. Levine, PhD - US grants

This collaborative project by a team of researchers at Hofstra University on Long Island, New York, the University of Cincinnati, and the University of Texas at Arlington represents a comprehensive study of the creative idea generation process in groups and organizations. The modern global economy is increasingly competitive, and there is growing pressure on science, industry, business and government to produce novel ideas and innovative solutions for difficult problems. Because most real-world problems are highly technical or involve the integration of information across a number of domains of expertise, almost all efforts to generate creative, effective solutions must be collaborative at many levels. In recent years, social psychologists have studied creative idea generation in small groups in a variety of settings, and have identified the factors that seem to facilitate or hinder innovation in organizations. However, it is now important to develop ways for systematically optimizing group creativity, and that is the ultimate goal of this research project. A model of idea generation in individuals, based on current knowledge of how the brain retrieves information from memory and organizes that information into both familiar and novel combinations, will be used as the basis for experimental studies of creative idea generation in groups and teams. Extensions of the model based on the theory of complex networks will be used to suggest ways to enhance innovation at the level of larger organizations. The research team itself is an example of effective collaboration among diverse individuals, with computer scientists, engineers, and cognitive and social psychologists pooling their expertise to study a complex and important problem.

This project brings together a top control engineer with a cognitive neuroscientist, in order to design a new family of control designs which would replicate and explain key capabilities of living brains which have never yet been achieved in engineering (or in the models used in computational neuroscience). This new work builds on previous work by Frank Lewis, in developing adaptive controllers (RLADP) which can maximize performance over time, in the face of nonlinearity and challenges which require foresight, such as the management of power grids. Here, the group will try to explain and replicate how brains can also handle challenges which require them to learn how to structure time, with multiple levels of decision with multiple time horizons, and how to handle complex structure in space, as we need to in managing complex infrastructure networks. These two challenges essentially address two of the three gaps between today's best RLADP and the highest capabilities of the mammal brain. The new fundamental design work will feed into ongoing laboratory work in the control of electric power microgrids, which are important as building blocks for future electric power distribution networks capable of coping with large penetrations of plug-in hybrid cars or rooftop solar and the like.

The previous work is reviewed in the Handbook of RLADP, from IEEE/Wiley, edited by Lewis and Liu. Spatial complexity will be addressed by considering optimal control of systems defined over graphs, such as power and communication networks. Game theoretic extensions, related to issues of distributed or collective intelligence, will also be considered. The work will also build on work by co-PI Levine on models of mechanisms in the brain involving the emotional gates in the amygdala and deliberative decisions in the anterior cingulate cortex.