TECHNICAL CHANGE AND NEW DIRECTIONS FOR COTTON PRODUCTION

This report summarizes a year-long study of the current and future role of technology in the Mid-South, Southeast, and High Plains cotton production systems. Specific research objectives were to: 1) Identify the impacts of emerging technology on regional cotton production systems, including the implications of technology adoption on the economic and environmental stability of the system; 2) Examine the future direction of technical change in cotton production and its implications for the biological and economic structure of the cotton production system; and 3) Determine the potential role of future technologies on shifting regional competitiveness in cotton production. Information used in the analysis was collected through a series of consultations with leading cotton research and extension personnel at regional research facilities and land grant universities. Given the verbal, descriptive nature of the information collected, the analysis represents the expert opinions of individuals working with and in the cotton production industry. In short, this report documents the combined vision of cotton production scientists and extension personnel with respect to the future of U.S. and regional cotton production. Necessary background information was obtained from published academic, industry, and government sources.Production Economics, Research and Development/Tech Change/Emerging Technologies,

Variable threshold algorithm for division of labor analyzed as a dynamical system

Division of labor is a widely studied aspect of colony behavior of social insects. Division of labor models indicate how individuals distribute themselves in order to perform different tasks simultaneously. However, models that study division of labor from a dynamical system point of view cannot be found in the literature. In this paper, we define a division of labor model as a discrete-time dynamical system, in order to study the equilibrium points and their properties related to convergence and stability. By making use of this analytical model, an adaptive algorithm based on division of labor can be designed to satisfy dynamic criteria. In this way, we have designed and tested an algorithm that varies the response thresholds in order to modify the dynamic behavior of the system. This behavior modification allows the system to adapt to specific environmental and collective situations, making the algorithm a good candidate for distributed control applications. The variable threshold algorithm is based on specialization mechanisms. It is able to achieve an asymptotically stable behavior of the system in different environments and independently of the number of individuals. The algorithm has been successfully tested under several initial conditions and number of individuals

Evolving team compositions by agent swapping

Optimizing collective behavior in multiagent systems requires algorithms to find not only appropriate individual behaviors but also a suitable composition of agents within a team. Over the last two decades, evolutionary methods have emerged as a promising approach for the design of agents and their compositions into teams. The choice of a crossover operator that facilitates the evolution of optimal team composition is recognized to be crucial, but so far, it has never been thoroughly quantified. Here, we highlight the limitations of two different crossover operators that exchange entire agents between teams: restricted agent swapping (RAS) that exchanges only corresponding agents between teams and free agent swapping (FAS) that allows an arbitrary exchange of agents. Our results show that RAS suffers from premature convergence, whereas FAS entails insufficient convergence. Consequently, in both cases, the exploration and exploitation aspects of the evolutionary algorithm are not well balanced resulting in the evolution of suboptimal team compositions. To overcome this problem, we propose combining the two methods. Our approach first applies FAS to explore the search space and then RAS to exploit it. This mixed approach is a much more efficient strategy for the evolution of team compositions compared to either strategy on its own. Our results suggest that such a mixed agent-swapping algorithm should always be preferred whenever the optimal composition of individuals in a multiagent system is unknown