On a method of Bownds for solving Volterra integral equations

An initial-value method of Bownds for solving Volterra integral equations is reexamined using a variable-step integrator to solve the differential equations. It is shown that such equations may be easily solved to an accuracy ofO(10^–8), the error depending essentially on that incurred in truncating expansions of the kernel to a degenerate one.This work was sponsored by a University of Nevada at Las Vegas Research Grant.     

Introduction to normative multiagent systems

This article introduces the research issues related to and definition of normative multiagent systems. It also describes the papers selected from NorMAS05 that are part of this double special issue and relates the papers to each other. Guido Boella received the PhD degree at the University of Torino in 2000.He is currently professor at the Department of Computer Science of the University of Torino. His research interests include multi-agent systems, in particular, normative systems, institutions and roles using qualitative decision theory.He is the co-chair of the firstworkshops on normative multi-agent systems (NorMas05), on coordination and organization (CoOrg05), and the AAAI Fall Symposium on roles (Roles05). Leendert van der Torre received the Ph.D. degree in computer science fromErasmus University Rotterdam, The Netherlands, in 1997. He is currently a Full Professor at the University of Luxembourg. He has developed the so-called input/output logics and the BOID agent architecture. His current research interests include deontic logic, qualitative game theory, and security and coordination in normative multiagent systems. Harko Verhagen received his Ph.D. degree in computer and systems sciences from Stockholm University (Sweden) in 2000 and is currently an associate professor at the department. His research has focussed on simulation of organizational behaviour, simulation as a scientific method, the use of sociological theories in multiagent systems research and more in particular theories on norms and autonomy.     

Stable Outcomes for Two-Sided Contract Choice Problems

In this paper we study the cooperative theory of stable outcomes for the room-mates problem modeled as a contract choice problem. We show, that a simple generalization of the Deferred Acceptance Procedure with firms proposing due to Gale and Shapley (1962), yields outcomes for a two-sided contract choice problem, which necessarily belong to the core and are Weakly Pareto Optimal for firms. Under the additional assumptions: (a) given any two distinct workers, the set of yields achievable by a firm with the first worker is disjoint from the set of yields achievable by it with the second, and (b) the contract choice problem is pair-wise efficient, we prove that there is no stable outcome at which a firm can get more than what it gets at the unique outcome of our procedure.Somdeb Lahiri is a Professor of Microeconomic Theory at the School of Economic and Business Sciences, University of Witwatersrand at Johannesburg since May 2001. He received his Ph.D in Economic Theory from the University of Minnesota, Minneapolis in 1986. Subsequently he has been on the faculty of Indian Institute of Technology, Kanpur (January to May 1987) and Indian Institute of Management, Ahmedabad (May 1987 to May 2001). He has held visiting positions at the Indian Institute of Science Bangalore, the Indian Statistical Institutes at Delhi and Kolkata, INSEE Paris, Mc Master University and Mc Gill University. His research interests are in Social Choice theory, Cooperative Game theory and Axiomatic Economic Allocation theory.     

Constrained LQR problems in elliptic distributed control systems with point observations—convergence results

In this paper constrained LQR problems in distributed control systems governed by the elliptic equation with point observations are studied. A variational inequality approach coupled with potential theory in a Banach space setting is adopted. First the admissible control set is extended to be bounded by two functions, and feedback characterization of the optimal control in terms of the optimal state is derived; then two numerical algorithms proposed in [5] are modified, and the strong convergence and uniform convergence in Banach space are proved. This verifies that the numerical algorithm is insensitive to the partition number of the boundary. Since our control variables are truncated below and above by two functions inL ^p and in our numerical computation only the layer density not the control variable is assumed to be piecewise smooth, uniform convergence guarantees a better convergence. Finally numerical computation for an example is carried out and confirms the analysis. This research was supported in part by NSF Grant DMS-9404380 and by an IRI Award of Texas A&M University. The current address of the first author is the Department of Mathematical Science, University of Nevada at Las Vegas, Las Vegas, NV 89154, USA.     

Optimization approach to the design of frequency sampling filters

Many digital signal processing applications require linear phase filtering. For applications that require narrow-band linear phase filtering, frequency sampling filters can implement linear phase filters more efficiently than the commonly used direct convolution filter. In this paper, a technique is developed for designing linear phase frequency sampling filters. A frequency sampling filter approximates a desired frequency response by interpolating a frequency response through a set of frequency samples taken from the desired frequency response. Although the frequency response of a frequency sampling filter passes through the frequency samples, the frequency response may not be well behaved between the specific samples. Linear programming is commonly used to control the interpolation errors between frequency samples. The design method developed in this paper controls the interpolation errors between frequency samples by minimizing the mean square error between the desired and actual frequency responses in the stopband and passband. This design method describes the frequency sampling filter design problem as a constrained optimization problem which is solved using the Lagrange multiplier optimization method. This results in a set of linear equations which when solved determine the filter's coefficients.This work was partially funded by The National Supercomputing Center for Energy and the Environment, University of Nevada Las Vegas, Las Vegas, Nevada and by NSF Grant MIP-9200581.     

Simulating the effect of nepotism on political risk taking and social unrest

Nepotism has been the primary influence on political behavior throughout human history. Despite the spread of democracy in the 20th century, nepotistic regimes have hardly disappeared. Nepotism heavily influences political activity throughout the developing world, Middle East, and central Asia where family ties are essential for gaining access to power, state resources, and privileges. Rebelling against such nepotistic regimes is difficult and risky. RiskTaker is an agent-based model we developed for testing the influences of various social forces on risk taking behavior, including the formulation of rebellious coalitions. We use RiskTaker to examine the influence of nepotism on the distribution of wealth and social status. Nepotism heavily skews the distribution of wealth and status, leading to the formation of opposing coalitions and exacerbating social unrest.This paper was tied for Best Paper, NAACSOS (North American Association for Computational Social and Organizational Science) Annual Conference 2005, June 26–28, Notre Dame. Robert Sedlmeyer, Department of Computer Science, Indiana University – Purdue University, Fort Wayne provided programming for the RiskTaker model. Lawrence A. Kuznar is a professor of anthropology and director of the Decision Sciences and Theory Institute at Indiana University—Purdue University, Fort Wayne. He has conducted fieldwork among Aymara Indians in Andean Peru and the Navajo of the American southwest. His research interests include computer modeling, theories of risk taking and conflict, terrorism, social evolution, and scientific epistemology. He has authored articles in Ecological Economics (with W. Frederick), Current Anthropology, American Anthropologist, Mathematical Anthropology and Culture Theory and Journal of Anthropological Research, and published two books (Awatimarka Harcourt Brace, 1995 and Reclaiming a Scientific Anthropology Altamira Press, 1997) and two edited volumes. William Frederick has served as a faculty member in the departments of mathematical sciences and the department of computer sciences at Indiana University—Purdue University, Fort Wayne since 1979. His primary interests include mathematical modeling, game theory, and genetic algorithms.     

Initial value methods in the theory of Fredholm integral equations

This paper deals with the problem of establishing the equivalence of a family of integral equations of Fredholm type with kernels that depend on a parameter and a related Cauchy system of integrodifferential equations. We also show how the Cauchy problem can be given an abstract formulation as an initial value problem in a complex Banach space.This research was supported by the University of Nevada at Las Vegas, Research Grant No. 4503.     

Social Network Discovery by Mining Spatio-Temporal Events

Knowing patterns of relationship in a social network is very useful for law enforcement agencies to investigate collaborations among criminals, for businesses to exploit relationships to sell products, or for individuals who wish to network with others. After all, it is not just what you know, but also whom you know, that matters. However, finding out who is related to whom on a large scale is a complex problem. Asking every single individual would be impractical, given the huge number of individuals and the changing dynamics of relationships. Recent advancement in technology has allowed more data about activities of individuals to be collected. Such data may be mined to reveal associations between these individuals. Specifically, we focus on data having space and time elements, such as logs of people's movement over various locations or of their Internet activities at various cyber locations. Reasoning that individuals who are frequently found together are likely to be associated with each other, we mine from the data instances where several actors co-occur in space and time, presumably due to an underlying interaction. We call these spatio-temporal co-occurrences events, which we use to establish relationships between pairs of individuals. In this paper, we propose a model for constructing a social network from events, and provide an algorithm that mines these events from the data. Experiments on a real-life data tracking people's accesses to cyber locations have also yielded encouraging results. Hady W. Lauw is a graduate student at the School of Computer Engineering, Nanyang Technological University, Singapore. His research interests include spatio-temporal data mining, social network discovery, and link analyisis. He has a BEng in computer engineering from Nanyang Technological University. Ee-Peng Lim is an Associate Professor with the School of Computer Engineering, Nanyang Technological University, Singapore. He received his PhD from the University of Minnesota, Minneapolis in 1994 and B.Sc. in Computer Science from National University of Singapore. Ee-Peng's research interests include information integration, data/text/web mining, digital libraries, and wireless intelligence. He is currently an Associate Editor of the ACM Transactions on Information Systems (TOIS), International Journal of Digital Libraries (IJDL) and International Journal of Data Warehousing and Mining (IJDWM). He was the Program Co-Chair of the ACM/IEEE Joint Conference on Digital Libraries (JCDL 2004), and Conference/Program Co-Chairs of International Conference on Asian Digital Libraries (ICADL 2004). He has also served in the program committee of numerous international conferences. Dr Lim is a Senior Member of IEEE and a Member of ACM. HweeHwa Pang received the B.Sc.—with first class honors—and M.S. degrees from the National University of Singapore in 1989 and 1991, respectively, and the PhD degree from the University of Wisconsin at Madison in 1994, all in Computer Science. He is currently an Associate Professor at the Singapore Management University. His research interests include database management systems, data security and quality, operating systems, and multimedia servers. He has many years of hands-on experience in system implementation and project management. He has also participated in transferring some of his research results to industry. Teck-Tim Tan is an IT Manager (Operations) at the Centre for IT Services, Nanyang Technological University (NTU), Singapore. He administers and oversees NTU's campus-wide wireless LAN infrastructure which facilitates access to the University's vast IT resources and services practically anywhere on campus.     

An agent-based investigation into the new product development capability

The capability to bring products to market which comply with quality, cost and development time goals is vital to the survival of firms in a competitve environment. New product development comprises knowledge creation and search and can be organized in different ways. In this paper, we study the performance of several alternative organizational models for new product development using a model of distributed, self-adapting (learning) agents. The agents (a marketing and a production agent) are modelled via neural networks. The artificial new product development process analyzed starts with learning on the basis of an initial set of production and marketing data about possible products and their evaluation. Subsequently, in each step of the process, the agents search for a better product with their current models of the environment and, then, refine their representations based on additional prototypes generated (new learning data). Within this framework, we investigate the influence of different types of new product search methods and generating prototypes/learning according to the performance of individual agents and the organization as a whole. In particular, sequential, team-based Trial & Error and House of Quality guided search are combined with prototype sampling methods of different intensity and breadth; also, the complexity of the agents (number of hidden units) is varied. It turns out that both the knowledge base and the search procedure have a significant impact on the agents' generalization ability and success in new product development. Andreas Mild was born in Vienna, Austria, in 1973. He studied business administration in Vienna, in 2000 he received his Ph.D. from the Vienna University of Economics and Business Administration (WU). Since 2003 he is associated professor at the WU. He has been guest professor in Frankfurt, Germany, Sydney, Australia and Bangkok, Thailand. Previous research appeared in Journals such as MIS Quarterly, Management Science and Marketing Science. His research interests currently include agent-based models, new product development and recommender systems. Alfred Taudes was born in Vienna, Austria, in 1959. He studied business administration and management information systems (MIS) in Vienna (doctorate 1984), in 1991 he received his Ph.D. from the Vienna University of Economics and Business Administration (WU). He was assistant professor at the WU (1986–1991) and professor for MIS at the German Universities of Augsburg (1991), Münster (1991/92) and Essen (1992/93). Since 1993, he has been professor for MIS at the WU and Head of the Department for Production Management. Since 2000, Dr. Taudes has been speaker for the Special Research Area SFB # 010 (Adaptive Information Systems and Modelling in Economics and Management Science). His research interests currently include agent-based models of industry structures, management of innovation, technology management and business strategy.     

寡头竞争模型在旅游联盟协议中的应用

博弈论中的寡头竞争模型在经济、政治、军事等社会科学中得到了广泛的应用.针对旅游联盟的机会主义行为,以库诺特(Cournot)寡头竞争模型为基础建立了旅游联盟博弈模型,来分析旅游联盟建立正式协议的必要性,并探讨了联盟协议的主要内容,包括:联盟的战略目标和合作范围;投入资源评价与利益分配;交流渠道;联盟解散条款.最后指出了建立协议的协商原则和灵活性原则.     

Mathematical Models for Studying the Value of Motivational Leadership in Teams

Mathematical models are presented for studying the value of leadership in a team where the members interact with each other. The models are based on a leader’s role of motivating each team member to perform closer to his/her maximum ability. These models include controllable parameters whose values reflect the amount of task interdependence among the workers as well as the motivational skill and variability in the skill of the leader. Confirming results—such as the fact that the skill level of the leader is a critical factor in the expected performance of the team—establish credibility in the models. Mathematical analysis and computer simulations are used to provide new managerial insights into the value of the leader—such as the fact that the skill of the leader can be more important than controlling the amount of interdependence among the team members and that having a choice of multiple leaders with no particular motivating skill is beneficial to the performance of small teams but not to large teams.Daniel Solow received a B.S. in Mathematics from Carnegie-Mellon, an M.S. in Operations Research from the University of California at Berkeley, and a Ph. D. in Operations Research from Stanford University. He has been a professor at Case Western Reserve University since 1978. His research interests include complex systems, discrete, linear, and nonlinear optimization. He has also developed systematic methods for teaching mathematical proofs, computer programming, and operations research.Sandy Kristin Piderit is an assistant professor of organizational behavior at the Weatherhead School of Management at Case Western Reserve University, and earned her Ph.D. from the University of Michigan. She studies the roles of relationships among coworkers on their performance and satisfaction with their work environments, and has published studies in the Academy of Management Review, the Journal of Management Studies, and Management Science.Apostolos Burnetas received a Diploma in Electrical Engineering from National Technical University in Athens, Greece, and an M.B.A. and Ph.D. in Operations Research from Rutgers University. He has been at the Department of Operations at Case Western Reserve University and is currently an Associate Professor at the Department of Mathematics at the University of Athens. His research interests include stochastic models and optimization, complex systems, and applications in queueing systems, supply chain and the interface of operations with finance.Chartchai Leenawong received a B.S. in Mathematics from Chulalongkorn University in Bangkok, an M.S. in Computer Science from the Asian Institute of Technology in Bangkok, and a Ph.D. in Operations Research from Case Western Reserve University. His research interests include mathematical modeling of complex systems as applied to business organizations. He has been a professor at King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand since 2002.     

Graph Theoretic and Spectral Analysis of Enron Email Data

Analysis of social networks to identify communities and model their evolution has been an active area of recent research. This paper analyzes the Enron email data set to discover structures within the organization. The analysis is based on constructing an email graph and studying its properties with both graph theoretical and spectral analysis techniques. The graph theoretical analysis includes the computation of several graph metrics such as degree distribution, average distance ratio, clustering coefficient and compactness over the email graph. The spectral analysis shows that the email adjacency matrix has a rank-2 approximation. It is shown that preprocessing of data has significant impact on the results, thus a standard form is needed for establishing a benchmark data. Anurat Chapanond is currently a Ph.D. student in Computer Science, RPI. Anurat graduated B. Eng. degree in Computer Engineering from Chiangmai University (Thailand) in 1997, M. S. in Computer Science from Columbia University in 2002. His research interest is in web data mining analyses and algorithms. M.S. Krishnamoorthy received the B.E. degree (with honors) from Madras University in 1969, the M. Tech degree in Electrical Engineering from the Indian Institute of Technology, Kanpur, in 1971, and the Ph. D. degree in Computer Science, also from the Indian Institute of Technology, in 1976. From 1976 to 1979, he was an Assistant Professor of Computer Science at the Indian Institute of Technology, Kanpur. From 1979 to 1985, he was an Assistant Professor of Computer Science at Rensselaer Polytechnic Institute, Troy, NY, and since, 1985, he has been an Associate Professor of Computer Science at Rensselaer. Dr. Krishnamoorthy's research interests are in the design and analysis of combinatorial and algebraic algorithms, visualization algorithms and programming environments. Bulent Yener is an Associate Professor in the Department of Computer Science and Co-Director of Pervasive Computing and Networking Center at Rensselaer Polytechnic Institute in Troy, New York. He is also a member of Griffiss Institute of Information Assurance. Dr. Yener received MS. and Ph.D. degrees in Computer Science, both from Columbia University, in 1987 and 1994, respectively. Before joining to RPI, he was a Member of Technical Staff at the Bell Laboratories in Murray Hill, New Jersey. His current research interests include bioinformatics, medical informtatics, routing problems in wireless networks, security and information assurance, intelligence and security informatics. He has served on the Technical Program Committee of leading IEEE conferences and workshops. Currently He is an associate editor of ACM/Kluwer Winet journal and the IEEE Network Magazine. Dr. Yener is a Senior Member of the IEEE Computer Society.     

Game analysis of technology innovation alliance stability based on knowledge transfer

Under incomplete information, a game model is used to investigate the influence of ownership level and learning ability on the stability of technology innovation alliance from the perspective of knowledge transfer. The decision-making processes of involved parties are divided into two stages in the model. In the first stage, the firm possessing advanced technology decides on the level of knowledge it transfers to its alliance partner. In the second stage, the decision of the parties on whether to maintain or terminate the alliance is based on two factors: the level of knowledge learned and profits gained. The outcomes of the Cournot–Nash equilibrium in the model can reveal when the parties decide to maintain or terminate the alliance. The model explores the status of alliance stability under different ownership levels and learning abilities to provide theoretical support for the selection of optimal dynamic competitive-cooperative relationship and managerial flexibility.     

The Emergence of Racial Segregation in an Agent-Based Model of Residential Location: The Role of Competing Preferences

Models of segregation dynamics have examined how individual preferences over neighborhood racial composition determine macroscopic patterns of segregation. Many fewer models have considered the role of household preferences over other location attributes, which may compete with preferences over racial composition. We hypothesize that household preferences over location characteristics other than racial composition affect segregation dynamics in nonlinear ways and that, for a critical range of parameter values, these competing preferences can qualitatively affect segregation outcomes. To test this hypothesis, we develop a dynamic agent-based model that examines macro-level patterns of segregation as the result of interdependent household location choices. The model incorporates household preferences over multiple neighborhood features, some of which are endogenous to residential location patterns, and allows for income heterogeneity across races and among households of the same race. Preliminary findings indicate that patterns of segregation can emerge even when individuals are wholly indifferent to neighborhood racial composition, due to competing preferences over neighborhood density. Further, the model shows a strong tendency to concentrate affluent families in a small number of suburbs, potentially mimicking recent empirical findings on favored quarters in metropolitan areas. This paper was the first runner-up for the 2004 NAACSOS best paper award. Kan Chen is an associate professor in the Department of Computational Science at the National University of Singapore. His recent research interests include spatial and temporal scaling in driven, dissipative systems, applications of self-organized criticality, dynamics of earthquakes, and computational finance. He received a B.Sc. in physics from the University of Science and Technology of China (1983) and a Ph.D. in physics from Ohio State University (1988). Elena Irwin is an associate professor in the Department of Agricultural, Environmental, and Development Economics at Ohio State University. Her research interests include land use change, urban sprawl, household location decisions, and the value of open space. This research applies theory and modeling techniques from the fields of spatial and regional economics, including the application of spatial econometrics and geographic information systems (GIS). She received a B.A. in German and History from Washington University in St. Louis (1988) and a Ph.D. in Agricultural and Resource Economics from the University of Maryland (1998). Ciriyam Jayaprakash is a Professor in the Department of Physics at the Ohio State University. His recent research interests include spatially extended nonlinear systems including fully-developed turbulence, genetic regulatory networks, and applications of statistical mechanical techniques to financial and social sciences. He received an M.S. in Physics from the Indian Institute of Technology, Kanpur (1973), an M.S. in Physics from Caltech (1975) and a Ph.D in Physics from the University of Illinois at Urbana-Champaign (1979). Keith Warren is an assistant professor in the College of Social Work at Ohio State University. His research interests focus on interpersonal interactions in the development and solution of social problems, particularly those of urban areas such as segregation, substance abuse and increased interpersonal violence. He received a B.A in Behavioral Science from Warren Wilson College (1984), and a Ph.D. in Social Work from the University of Texas at Austin (1998)     

On the potential of norm-governed behavior in different categories of artificial societies

Based on a classification of artificial societies and the identification of four different types of stakeholders in such societies, we investigate the potential of norm-governed behavior in different types of artificial societies. The basis of the analysis is the preferences of the stakeholders and how they influence the state of the society. A general conclusion drawn is that the more open a society is the more it has to rely on agent owners and designers to achieve norm-governed behavior, whereas in more closed societies the environment designers and owners may control the degree of norm-governed behavior. Paul Davidsson is professor at the Department of Systems and Software Engineering, School of Engineering, Blekinge Institute of Technology, Sweden. He received his Ph.D. in Computer Science in 1996 from Lund University, Sweden. His research interests include the theory and application of multi-agent systems, autonomous agents, and machine learning. Application areas include logistics, transport systems, district heating systems, building automation, and telecommunications systems. The results of this work have been reported in more than 75 peer-reviewed scientific articles published in international journals and conference proceedings. Moreover, he has been the co-editor of three books on Multi Agent Based Simulation and member of program committees of numerous international conferences, such as the International Joint Conference on Autonomous Agents and Multi-Agent Systems Stefan Johansson is an assistant professor at Department of Systems and Software Engineering, Blekinge Institute of Technology, Sweden, where he also finished his PhD in 2002. The main research areas cover coordination issues in multi-agent systems and theories of autonomous agents. Applications of special interests are agents in game ai, robotics, telecommunication networks. On his list of publications are more than 35 peer-reviewed papers published in conference proceedings and scientific journals in the areas of agents, ai, robotics and games. He has also been a member of a variety of programme committees of scientific conferences, including e.g. Intelligent Agent Technology.     

Email Surveillance Using Non-negative Matrix Factorization

In this study, we apply a non-negative matrix factorization approach for the extraction and detection of concepts or topics from electronic mail messages. For the publicly released Enron electronic mail collection, we encode sparse term-by-message matrices and use a low rank non-negative matrix factorization algorithm to preserve natural data non-negativity and avoid subtractive basis vector and encoding interactions present in techniques such as principal component analysis. Results in topic detection and message clustering are discussed in the context of published Enron business practices and activities, and benchmarks addressing the computational complexity of our approach are provided. The resulting basis vectors and matrix projections of this approach can be used to identify and monitor underlying semantic features (topics) and message clusters in a general or high-level way without the need to read individual electronic mail messages. Michael W. Berry is a Professor and Interim Department Head in the Department of Computer Science at the University of Tennessee and a faculty member in the Graduate School in Genome Science and Technology Program at the University of Tennessee and Oak Ridge National Laboratory. His research interests include information retrieval, data mining, scientific computing, computational science, and numerical linear algebra. He is a member of the Society for Industrial and Applied Mathematics (SIAM), Association for Computing Machinery (ACM), and the Computer Society of the Institute of Electrical and Electronics (IEEE). Professor Berry is on the editorial boards of “Computing in Science and Engineering” (IEEE Computer Society and the American Institute of Physics) and the SIAM Journal of Scientific Computing. Murray Browne is a Research Associate in the Department of Computer Science at the University of Tennessee. He is a member of the American Society for Information Science and Technology and has published numerous essays, book reviews, newspaper articles, and feature stories.     

Scan Statistics on Enron Graphs

We introduce a theory of scan statistics on graphs and apply the ideas to the problem of anomaly detection in a time series of Enron email graphs. Previous presentation: Workshop on Link Analysis, Counterterrorism and Security at the SIAM International Conference on Data Mining, Newport Beach, CA, April 23, 2005. Carey E. Priebe received the B.S. degree in mathematics from Purdue University in 1984, the M.S. degree in computer science from San Diego State University in 1988, and the Ph.D. degree in information technology (computational statistics) from George Mason University in 1993. From 1985 to 1994 he worked as a mathematician and scientist in the US Navy research and development laboratory system. Since 1994 he has been a professor in the Department of Applied Mathematics and Statistics, Whiting School of Engineering, Johns Hopkins University, Baltimore, Maryland. At Johns Hopkins, he holds joint appointments in the Department of Computer Science and the Center for Imaging Science. He is a past President of the Interface Foundation of North America—Computing Science & Statistics, a past Chair of the Section on Statistical Computing of the American Statistical Association, and on the editorial boards of Journal of Computational and Graphical Statistics, Computational Statistics and Data Analysis, and Computational Statistics. His research interests are in computational statistics, kernel and mixture estimates, statistical pattern recognition, statistical image analysis, and statistical inference for high-dimensional and graph data. He was elected Fellow of the American Statistical Association in 2002. John M. Conroy received a B.S. in Mathematics from Saint Joseph's University in 1980 and a Ph.D. in Applied Mathematics from the University of Maryland in 1986. Since then he has been a research staff member for the IDA Center for Computing Sciences in Bowie, MD. His research interest is applications of numerical linear algebra. He is a member of the Society for Industrial and Applied Mathematics, Institute of Electrical and Electronics Engineers (IEEE), and the Association for Computational Linguistics. David J. Marchette received a B.A. in 1980, and an M.A. in mathematics in 1982, from the University of California at San Diego. He received a Ph.D. in Computational Sciences and Informatics in 1996 from George Mason University under the direction of Ed Wegman. From 1985–1994 he worked at the Naval Ocean Systems Center in San Diego doing research on pattern recognition and computational statistics. In 1994 he moved to the Naval Surface Warfare Center in Dahlgren Virginia where he does research in computational statistics and pattern recognition, primarily applied to image processing, text processing, automatic target recognition and computer security. Dr. Marchette is a Fellow of the American Statistical Society. Youngser Park received the B.E. degree in electrical engineering from Inha University in Korea in 1985, the M.S. degree in computer science from The George Washington University in 1991, and had pursued a doctoral degree there. From 1998 to 2000 he worked at the Johns Hopkins Medical Institutes as a senior research engineer. Since 2003 he is working as a research analyst in the Center for Imaging Science at the Johns Hopkins University. His research interests are clustering algorithm, pattern classification, and data mining.     

Modeling and Analyzing Cultural Influences on Project Team Performance

Research on international joint ventures (IJV) finds managers experience difficulties in working with cross-cultural teams. Our research aims to understand how cultural differences between Japanese and American firms in IJV projects effect team performance through computational experimentation. We characterize culture and cultural differences using two dimensions: practices and values.Practices refer to each cultures typical organization style, such as centralization of authority, formalization of communication, and depth of organizational hierarchy. Values refer to workers preferences in making task execution and coordination decisions. These preferences drive specific micro-level behavior patterns for individual workers. Previous research has documented distinctive organization styles and micro-level behavior patterns for different nations. We use a computational experimental design that sets task complexityat four levels and team experience independently at three levels, yielding twelve organizational contexts. We then simulate the four possible combinations of USvs.Japanese organization style and individual behavior in each context to predict work volume, cost, schedule andprocess quality outcomes. Simulation results predict that: (1) both Japanese and American teams show better performance across all contexts when each works with its familiar organization style; (2) the Japanese organization style performs better under high task complexity, with low team experience; and (3) process quality risk is not significantly affected by organization styles. In addition, culturally driven behavior patterns have less impact on project outcomes than organization styles. Our simulation results are qualitatively consistent with both organizational and cultural contingency theory, and with limited observations of US-Japanese IJV project teams.This paper won the best Ph.D. student paper award at NAACSOS 2004, Pittsburgh PA. NAACSOS is the main conference of the North American Association for Computational Social and Organizational Science.Tamaki Horii is a Ph.D. candidate in the Civil and Environmental Engineering Department at Stanford University. His research focuses on various aspects of cultural and institutional influences on team performance. He is currently developing new models to capture and distinguish the cultural factors that emerging in global projects. He received a MS in Architecture at the Science University of Tokyo and a MS in Civil and Environmental Engineering at Stanford University.Yan Jin is an Associate Professor of Mechanical Engineering at University of Southern California and Director of USC IMPACT Laboratory , and a visiting Professor of Civil Engineering Department at Stanford University. He received his Ph.D. degree in Naval Engineering from the University of Tokyo in 1988. Prior to joining USC faculty in the Fall of 1996, Dr. Jin was a Senior Research Scientist at Stanford University. His current research interests include design methodology, agent-based collaborative engineering, and computational organization modeling. Dr. Jin is a recipient of National Science Foundation CAREER Award (1998), TRW Excellence in Teaching Award (2001), Best Paper in Human Information Systems (5th World Multi-Conference on Systemic, Cybernetics and Informatics, 2001), and Xerox Best Paper Award (ASME International Conference on Design Theory and Methodology, 2002).Raymond E. Levitt is a Professor of Civil Engineering Department at Stanford University, a Professor, by Courtesy, Medical Informatics, an Academic director of Stanford Advanced Project Management Executive Program, and a Director of Collaboratory for Research on Global Projects (CRGP) . His Virtual Design Team (VDT) research group has developed new organization theory, methodology and computer simulation tools to design organizations that can optimally execute complex, fast-track, projects and programs. VDT is currently being extended to model and simulate service/maintenancework processes such as health care delivery and offshore platform maintenance. Ongoing research by Professor Levitts Virtual Design Team research group attempts to model and simulate the significant institutional costs that can arise in global projects due to substantial differences in goals, values and cultural norms among project stakeholders.     

A variation of the housing unit method for estimating the population of small,rural areas: a case study of the local expert procedure

Sociologists designed a random sampling study based on an adaptation of the Housing Unit Method and the local expert method to determine the socioeconomic features of a 3 unincorporated rural communities near Yucca Mountain, Nevada which scientists will use to conduct a comprehensive impact analysis of the proposed geologic nuclear waste repository at Yucca Mountain, about 90 miles northwest of Las Vegas. Electrical company representatives indicated the location and type of housing with all up to date electrical connections in southern Nye county. This information was included in the housing unit file made from utility records from each community. After determining the sample size needed, households were randomly chosen from each file (326 Amargosa Valley, 672 Beatty, and 3224 Pahrump). Meter readers from the local utility companies were the local experts. 2 local experts worked together to authenticate the accuracy of recorded data which included number of person in the household as of July 15, 1990 and age and gender of each member. Data accuracy was tested and it was found that the 1990 US Census counts were within the relatively narrow 95% confidence intervals. The mean width was 7.2% of the estimated population, thus the estimates were meaningful. The estimates were too low for Pahrump (7190 vs. 7425) and Amargosa Valley (841 vs. 853), however. This may have been due to recent in-migration from the Las Vegas Valley. Age and gender accuracy could not be tested since the 1990 census data were not yet ready. Nevertheless, it is believed that this procedure can obtain very accurate estimates.     

On the Monte carlo boolean decision tree complexity of read-once formulae

In the boolean decision tree model there is at least a linear gap between the Monte Carlo and the Las Vegas complexity of a function depending on the error probability. We prove for a large class of read-once formulae that this trivial speed-up is the best that a Monte Carlo algorithm can achieve. For every formula F belonging to that class we show that the Monte Carlo complexity of F with two-sided error p is (1 ? 2p)R(F), and with one-sided error p is (1 ? p)R(F), where R(F) denotes the Las Vegas complexity of F. The result follows from a general lower bound that we derive on the Monte Carlo complexity of these formulae. This bound is analogous to the lower bound due to Saks and Wigderson on their Las Vegas complexity.