Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. In Social Network Problems usually the underlying mechanism is not fully understood, and the experimenter must approximate the unknown function g with appropriate empirical model,..., , 2 1 ) + ε, where the term ε represents the error in the system.Usually the function f is a first-order or second-order polynomial. This empirical model is called a response surface model.Identifying and fitting from experimental data an appropriate response surface model requires some use of statistical experimental design fundamentals, regression modeling techniques, and optimization methods. All three of these topics are usually combined into Response Surface Methodology (RSM).Also the experimenter may encounter situations where the full model may not be appropriate. Then variable selection or model-building techniques may be used to identify the best subset of regressors to include in a regression model. In our approach we use the simulated annealing method of optimization for searching the best subset of regressors. In some response surface experiments, there can be one or more near-linear dependences among regressor variables in the model. Regression model builders refer to this as multicollinearity among the regressors. Multicollinearity can have serious effects on the estimates of the model parameters and on the general applicability of the final model.The RSM is also extremely useful as an automated tool for model calibration and validation especially for modern computational multi-agent large-scale social-networks systems that are becoming heavily used in modeling and simulation of complex social networks.The RSM can be integrated in many large-scale simulation systems such as BioWar, ORA and is currently integrating in Vista, Construct, and DyNet. This report describes the theoretical approach for solving of these problems and the implementation of chosen methods.

Organizations are frequently designed and redesigned, often in efforts to improve performance or meet various managerial goals for coordination and communication. Such design is often done through the review of a few of options and the use of managerial and possibly personnel insight into how the new design might work. In contrast, we provide a systematic optimization based approach. In this approach, the user can pick one or more Dynamic Network Analysis (DNA) metrics and then use one or more of the available optimizers to find a design that more closely meets this ideal. The optimizer utilizes heuristic based optimization procedures to generate an optimized organizational design given a particular mission. DNA metrics, such as Communication Congruence, Resource Congruence, Cognitive Load, and Actual Workload, serve to define criteria. The Optimizer can perform multi-criteria optimization in order to improve several metrics simultaneously. Two optimization methods can be used -Monte Carlo and Simulated Annealing, both of which are statistical methods of finding a global optimum. DNA metrics used in the optimizations are computed by ORA. This report describes this optimizer.