Uniformity,Bipolarization and Pluriformity Captured as Generic Stylized Behavior with an Agent-Based Simulation Model of Attitude Change

This paper focuses at the dynamics of attitude change in large groups. A multi-agent computer simulation has been developed as a tool to study hypothesis we take to study these dynamics. A major extension in comparison to earlier models is that Social Judgment Theory is being formalized to incorporate processes of assimilation and contrast in persuasion processes. Results demonstrate that the attitude structure of agents determines the occurrence of assimilation and contrast effects, which in turn cause a group of agents to reach consensus, to bipolarize, or to develop a number of subgroups sharing the same position. Subsequent experiments demonstrate the robustness of these effects for a different formalization of the social network, and the susceptibility for population size.This paper won the best paper award at NAACSOS 2004, Pittsburgh PA. NAACSOS is the main conference of the North American Association for Computational Social and Organizational Science.Wander Jager received his Ph.D. degree in Social Sciences in 2000 from the University of Groningen, the Netherlands. Dr. Jager is currently Associate Professor at the University of Groningen. His current application domain concerns marketing, innovation diffusion and social simulation. Dr. Jager has authored or co-authored various papers on market dynamics, diffusion processes, resource use and sustainable consumption.Frédéric Amblard received his Ph.D. degree in Multi-Agent Simulation in 2003 from Blaise Pascal University, Clermont-Ferrand, France. Dr. Amblard is currently Associate Professor at the University of Social Sciences in Toulouse and researcher associated to the CNRS-IRIT, Institute of Research in Computer Sciences in Toulouse. His current application domain now concerns Agent-Based Social Simulation. Dr. Amblard has authored or coauthored various research papers either in computer sciences, in physics or in sociology.A preceding version of this paper has been presented to the 2004 Conference of the North American Association for Computational Social and Organization Science, Pittsburgh, USA and received the best paper award from this conference.     

Validation and verification of social processes within agent-based computational organization models

The use of simulation modeling in computational analysis of organizations is becoming a prominent approach in social science research. However, relying on simulations to gain intuition about social phenomena has significant implications. While simulations may give rise to interesting macro-level phenomena, and sometimes even mimic empirical data, the underlying micro and macro level processes may be far from realistic. Yet, this realism may be important to infer results that are relevant to existing theories of social systems and to policy making. Therefore, it is important to assess not only predictive capability but also explanation accuracy of formal models in terms of the degree of realism reflected by the embedded processes. This paper presents a process-centric perspective for the validation and verification (V&V) of agent-based computational organization models. Following an overview of the role of V&V within the life cycle of a simulation study, emergent issues in agent-based organization model V&V are outlined. The notion of social contract that facilitates capturing micro level processes among agents is introduced to enable reasoning about the integrity and consistency of agent-based organization designs. Social contracts are shown to enable modular compositional verification of interaction dynamics among peer agents. Two types of consistency are introduced: horizontal and vertical consistency. It is argued that such local consistency analysis is necessary, but insufficient to validate emergent macro processes within multi-agent organizations. As such, new formal validation metrics are introduced to substantiate the operational validity of emergent macro-level behavior. Levent Yilmaz is Assistant Professor of Computer Science and Engineering in the College of Engineering at Auburn University and co-founder of the Auburn Modeling and Simulation Laboratory of the M&SNet. Dr. Yilmaz received his Ph.D. and M.S. degrees from Virginia Polytechnic Institute and State University (Virginia Tech). His research interests are on advancing the theory and methodology of simulation modeling, agent-directed simulation (to explore dynamics of socio-technical systems, organizations, and human/team behavior), and education in simulation modeling. Dr. Yilmaz is a member of ACM, IEEE Computer Society, Society for Computer Simulation International, and Upsilon Pi Epsilon. URL: http://www.eng.auburn.edu/~yilmaz     

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.     

Communication Networks from the Enron Email Corpus “It's Always About the People. Enron is no Different”

The Enron email corpus is appealing to researchers because it represents a rich temporal record of internal communication within a large, real-world organization facing a severe and survival-threatening crisis. We describe how we enhanced the original corpus database and present findings from our investigation undertaken with a social network analytic perspective. We explore the dynamics of the structure and properties of the organizational communication network, as well as the characteristics and patterns of communicative behavior of the employees from different organizational levels. We found that during the crisis period, communication among employees became more diverse with respect to established contacts and formal roles. Also during the crisis period, previously disconnected employees began to engage in mutual communication, so that interpersonal communication was intensified and spread through the network, bypassing formal chains of communication. The findings of this study provide valuable insight into a real-world organizational crisis, which may be further used for validating or developing theories and dynamic models of organizational crises; thereby leading to a better understanding of the underlying causes of, and response to, organization failure. Jana Diesner is a Research Associate and Linguistic Programmer at the Center for Computational Analysis of Social and Organizational Systems at the School of Computer Science (CASOS), Carnegie Mellon University (CMU). She received her Masters in Communications from Dresden University of Technology in 2003. She had been a research scholar at the Institute for Complex Engineered System at CMU in 2001 and 2002. Her research combines computational linguistics, social network analysis and computational organization theory. Terrill L. Frantz is a post-doc researcher at the Center for Computational Analysis of Social and Organizational Systems (CASOS) in the School of Computer Science at Carnegie Mellon University. His research involves studying the dynamics of organization social-networks and behavior via computer modeling and simulation. He is developing an expertise in workforce integration strategy and policy evaluation during organization mergers. He earned his doctorate (Ed.D. in Organization Change) from Pepperdine University, a MBA from New York University and a BS in Business Administration (Computer Systems Management) from Drexel University. Prior to entering academic research, for nearly 20 years he was a software applications development manager in the global financial services and industrial chemicals industries; most recently as a Vice President in Information Technology at Morgan Stanley in Hong Kong, New York and London. Kathleen M. Carley is a professor at the Institute for Software Research International in the School of Computer Science at Carnegie Mellon University. She is the director of the center for Computational Analysis of Social and Organizational Systems (CASOS) <http://www.casos.cs.cmu.edu/>, a university wide interdisciplinary center that brings together network analysis, computer science and organization science (www.casos.ece.cmu.edu) and has an associated NSF funded training program for Ph.D. students. She carries out research that combines cognitive science, social networks and computer science to address complex social and organizational problems. Her specific research areas are computational social and organization theory, group, organizational and social adaptation and evolution, social and dynamic network analysis, computational text analysis, and the impact of telecommunication technologies and policy on communication, information diffusion, disease contagion and response within and among groups particularly in disaster or crisis situations.     

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.     

On effectiveness of wiretap programs in mapping social networks

Snowball sampling methods are known to be a biased toward highly connected actors and consequently produce core-periphery networks when these may not necessarily be present. This leads to a biased perception of the underlying network which can have negative policy consequences, as in the identification of terrorist networks. When snowball sampling is used, the potential overload of the information collection system is a distinct problem due to the exponential growth of the number of suspects to be monitored. In this paper, we focus on evaluating the effectiveness of a wiretapping program in terms of its ability to map the rapidly evolving networks within a covert organization. By running a series of simulation-based experiments, we are able to evaluate a broad spectrum of information gathering regimes based on a consistent set of criteria. We conclude by proposing a set of information gathering programs that achieve higher effectiveness then snowball sampling, and at a lower cost. Maksim Tsvetovat is an Assistant Professor at the Center for Social Complexity and department of Public and International Affairs at George Mason University, Fairfax, VA. He received his Ph.D. from the Computation, Organizations and Society program in the School of Computer Science, Carnegie Mellon University. His dissertation was centered on use of artificial intelligence techniques such as planning and semantic reasoning as a means of studying behavior and evolution of complex social networks, such as these of terrorist organizations. He received a Master of Science degree from University of Minnesota with a specialization in Artificial Intelligence and design of Multi-Agent Systems, and has also extensively studied organization theory and social science research methods. His research is centered on building high-fidelity simulations of social and organizational systems using concepts from distributed artificial intelligence and multi-agent systems. Other projects focus on social network analysis for mapping of internal corporate networks or study of covert and terrorist orgnaizations. Maksim’s vita and publications can be found on Kathleen M. Carley is a professor in the School of Computer Science at Carnegie Mellon University and the director of the center for Compuational Analysis of Social and Organizational Systems (CASOS) which has over 25 members, both students and research staff. Her research combines cognitive science, social networks and computer science to address complex social and organizational problems. Her specific research areas are dynamic network analysis, computational social and organization theory, adaptation and evolution, text mining, and the impact of telecommunication technologies and policy on communication, information diffusion, disease contagion and response within and among groups particularly in disaster or crisis situations. She and her lab have developed infrastructure tools for analyzing large scale dynamic networks and various multi-agent simulation systems. The infrastructure tools include ORA, a statistical toolkit for analyzing and visualizing multi-dimensional networks. ORA results are organized into reports that meet various needs such as the management report, the mental model report, and the intelligence report. Another tool is AutoMap, a text-mining systems for extracting semantic networks from texts and then cross-classifying them using an organizational ontology into the underlying social, knowledge, resource and task networks. Her simulation models meld multi-agent technology with network dynamics and empirical data. Three of the large-scale multi-agent network models she and the CASOS group have developed in the counter-terrorism area are: BioWar a city-scale dynamic-network agent-based model for understanding the spread of disease and illness due to natural epidemics, chemical spills, and weaponized biological attacks; DyNet a model of the change in covert networks, naturally and in response to attacks, under varying levels of information uncertainty; and RTE a model for examining state failure and the escalation of conflict at the city, state, nation, and international as changes occur within and among red, blue, and green forces. She is the founding co-editor with Al. Wallace of the journal Computational Organization Theory and has co-edited several books and written over 100 articles in the computational organizations and dynamic network area. Her publications can be found at: http://www.casos.cs.cmu.edu/bios/carley/publications.php     

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.     

Mapping deontic operators to abductive expectations

Deontic concepts and operators have been widely used in several fields where representation of norms is needed, including legal reasoning and normative multi-agent systems. The EU-funded SOCS project has provided a language to specify the agent interaction in open multi-agent systems. The language is equipped with a declarative semantics based on abductive logic programming, and an operational semantics consisting of a (sound and complete) abductive proof procedure. In the SOCS framework, the specification is used directly as a program for the verification procedure. In this paper, we propose a mapping of the usual deontic operators (obligations, prohibition, permission) to language entities, called expectations, available in the SOCS social framework. Although expectations and deontic operators can be quite different from a philosophical viewpoint, we support our mapping by showing a similarity between the abductive semantics for expectations and the Kripke semantics that can be given to deontic operators. The main purpose of this work is to make the computational machinery from the SOCS social framework available for the specification and verification of systems by means of deontic operators. Marco Alberti received his laurea degree in Electronic Engineering in 2001 and his Ph.D. in Information Engineering in 2005 from the University of Ferrara, Italy. His research interests include constraint logic programming and abductive logic programming, applied in particular to the specification and verification of multi-agent systems. He has been involved as a research assistants in national and European research projects. He currently has a post-doc position in the Department of Engineering at the University of Ferrara. Marco Gavanelli is currently assistant professor in the Department of Engineering at the University of Ferrara, Italy. He graduated in Computer Science Engineering in 1998 at the University of Bologna, Italy. He got his Ph.D. in 2002 at Ferrara University. His research interest include Artificial Intelligence, Constraint Logic Programming, Multi-criteria Optimisation, Abductive Logic Programming, Multi-Agent Systems. He is a member of ALP (the Association for Logic Programming) and AI*IA (the Italian Association for Artificial Intelligence). He has organised workshops, and is author of more than 30 publications between journals and conference proceedings. Evelina Lamma received her degree in Electronic Engineering from University of Bologna, Italy, in 1985 and her Ph.D. degree in Computer Science in 1990. Currently she is Full Professor at the Faculty of Engineering of the University of Ferrara where she teaches Artificial Intelligence and Foundations of Computer Science. Her research activity focuses around: – programming languages (logic languages, modular and object-oriented programming); – artificial intelligence; – knowledge representation; – intelligent agents and multi-agent systems; – machine learning. Her research has covered implementation, application and theoretical aspects. She took part to several national and international research projects. She was responsible of the research group at the Dipartimento di Ingegneria of the University of Ferrara in the UE ITS-2001-32530 Project (named SOCS), in the the context of the UE V Framework Programme - Global Computing Action. Paola Mello received her degree in Electronic Engineering from the University of Bologna, Italy, in 1982, and her Ph.D. degree in Computer Science in 1989. Since 1994 she has been Full Professor. She is enrolled, at present, at the Faculty of Engineering of the University of Bologna (Italy), where she teaches Artificial Intelligence. Her research activity focuses on programming languages, with particular reference to logic languages and their extensions, artificial intelligence, knowledge representation, expert systems with particular emphasis on medical applications, and multi-agent systems. Her research has covered implementation, application and theoretical aspects and is presented in several national and international publications. She took part to several national and international research projects in the context of computational logic. Giovanni Sartor is Marie-Curie professor of Legal informatics and Legal Theory at the European University Institute of Florence and professor of Computer and Law at the University of Bologna (on leave), after obtaining a PhD at the European University Institute (Florence), working at the Court of Justice of the European Union (Luxembourg), being a researcher at the Italian National Council of Research (ITTIG, Florence), and holding the chair in Jurisprudence at Queen’s University of Belfast (where he now is honorary professor). He is co-editor of the Artificial Intelligence and Law Journal and has published widely in legal philosophy, computational logic, legislation technique, and computer law. Paolo Torroni is Assistant Professor in computing at the Faculty of Engineering of the University of Bologna, Italy. He obtained a PhD in Computer Science and Electronic Engineering in 2002, with a dissertation on logic-based agent reasoning and interaction. His research interests mainly focus on computational logic and multi-agent systems research, including logic programming, abductive and hypothetical reasoning, agent interaction, dialogue, negotiation, and argumentation. He is in the steering committee of the CLIMA and DALT international workshops and of the Italian logic programming interest group GULP.     

A normative framework for agent-based systems

One of the key issues in the computational representation of open societies relates to the introduction of norms that help to cope with the heterogeneity, the autonomy and the diversity of interests among their members. Research regarding this issue presents two omissions. One is the lack of a canonical model of norms that facilitates their implementation, and that allows us to describe the processes of reasoning about norms. The other refers to considering, in the model of normative multi-agent systems, the perspective of individual agents and what they might need to effectively reason about the society in which they participate. Both are the concerns of this paper, and the main objective is to present a formal normative framework for agent-based systems that facilitates their implementation. F. López y López is researcher of the Computer Science Faculty at the Benemérita Universidad Autónoma de Puebla in México, from where she got her first degree. She also gained a MSc in Computation from the Universidad Nacional Autónoma de México and a PhD in Computer Science from the University of Southampton in the United Kingdom. She is leading several theoretical and practical projects that use multi-agent systems as the main paradigm. Her research has been focused on Autonomous Normative Agents and Normative Multi-Agent Systems and she has published over 20 articles in these and related topics. M. Luck is Professor of Computer Science in the Intelligence, Agents, Multimedia Group of the School of Electronics and Computer Science at the University of Southampton, where he carries out research into the theory and practice of agent technology. He has published over 150 articles in these and related areas, both alone and in collaboration with others, and has published eight books. He is a member of the Executive Committee of AgentLink III, the European Network of Excellence for Agent-Based Computing. He is a co-founder of the European Multi-Agent Systems workshop series, is co-founder and Chair of the steering committee of the UK Multi-Agent Systems Workshops (UKMAS), and was a member of the Management Board of Agentcities.NET. Professor Luck is also a steering committee member for the Central and Eastern European Conference on Multi-Agent Systems. He is series editor for Artech House’s Agent Oriented Systems series, and an editorial board member of the Journal of Autonomous Agents and Multi-Agent Systems, the International Journal of Agent-Oriented Software Engineering, and ACM Transactions on Autonomous and Adaptive Systems. M. d’Inverno gained a BA in Mathematics and an MSc in Computation both from Oxford University. He also was awarded a PhD from University College London. He joined the University of Westminster in 1992 as a Lecturer, became a senior lecturer in 1998, a reader in 1999 and was appointed professor of computer science in 2001. He is interested in formal, principled approaches to modelling both natural and artificial systems in a computational setting. The main strand to this research, focuses on the application of formal methods in providing models of intelligent agent and multi-agent systems. His approach has sought to take a structured approach to the development of practical agent systems from theoretical models. He has published over 70 articles in these areas and has published four books and edited collections.     

Using an Iterated Prisoner's Dilemma with Exit Option to Study Alliance Behavior: Results of a Tournament and Simulation

Nearly half of all strategic alliances fail (Park and Russo, 1996; Dyer et al., 2001), often because of opportunistic behavior by one party or the other. We use a tournament and simulation to study strategies in an iterated prisoner's dilemma game with exit option to shed light on how a firm should react to an opportunistic partner. Our results indicate that a firm should give an alliance partner a second chance following an opportunistic act but that subsequent behavior should be contingent on the value of the next best opportunity outside the alliance. Firms should be more forgiving if the potential benefits from the alliance exceed other opportunities. The strategies were also found to be robust across a wide range of game lengths. The implications of these results for alliance strategies are discussed. Steven E. Phelan received his PhD in economics from La Trobe University (Australia) in 1998. Following five years at the University of Texas at Dallas, he joined the faculty of the University of Nevada Las Vegas in 2003. Dr. Phelan's research interests include competitive dynamics, organizational efficiency, acquisition and alliance performance, and entrepreneurial competence. His methods of choice to study these phenomena include agent-based modelling, experimental game theory, and event studies. Prior to joining academia, Dr. Phelan held executive positions in the telecommunications and airline industries and was a principal partner in Bridges Management Group, a consultancy specializing in strategic investment decisions. Richard J. Arend is a graduate of the University of British Columbia's doctoral program in Policy Analysis and Strategy. He is on the Management faculty of the University of Nevada, Las Vegas, arriving most recently from the Management faculty of New York University's Stern School of Business. Dr. Arend's interests lie in the analysis of unusual modes of firm value creation and destruction, where he has published in several top journals. He is a professional engineer with work and consulting experience in aerospace and computing. Darryl A. Seale joined the faculty of UNLV in 1999, following three years at Kent State University and the University of Alabama in Huntsville. Prior to Alabama, he completed his Ph.D. and M.S. degrees in Business Administration at the University of Arizona, his M.B.A. from Penn State University, and spent over ten years in management and market planning positions in the health care industry. Professor Seale's research interests include strategic decision making, bargaining and negotiation, and behavioral game theory. His research has been funded by the National Science Foundation and has been published in top-tier journals including Management Science, OBHDP, Games and Economic Behavior, and Strategic Management Journal. His teaching interests include business policy/strategy, managerial decision making, and bargaining and negotiation.     

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.     

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.     

Ethnic Preferences and Residential Segregation: A Commentary on Outcomes from Agent-Based Modeling

Mark Fossett's new research, published here in the Journal of Mathematical Sociology, is arguably the most important advance in studies of residential segregation in the past decade. While this study of the role of preferences in creating the patterns of residential separation does not answer all the questions about how preferences create separation in the residential mosaic, it provides a major extension of Schelling's seminal work of three decades ago. The paper shows clearly that preferences do matter and that the set of simulations leave little doubt that residential preferences and their underlying social dynamics have the capacity to generate high levels of ethnic segregation. The agent-based modeling technique, on which the results are based, is a major advance on previouswork using agent-based modeling and will set the standard for further studies of the underlying processes that create residential separation in U.S. cities.     

A cognitively based simulation of academic science

The models used in social simulation to date have mostly been very simplistic cognitively, with little attention paid to the details of individual cognition. This work proposes a more cognitively realistic approach to social simulation. It begins with a model created by Gilbert (1997) for capturing the growth of academic science. Gilbert’s model, which was equation-based, is replaced here by an agent-based model, with the cognitive architecture CLARION providing greater cognitive realism. Using this cognitive agent model, results comparable to previous simulations and to human data are obtained. It is found that while different cognitive settings may affect the aggregate number of scientific articles produced, they do not generally lead to different distributions of number of articles per author. The paper concludes with a discussion of the correspondence between the model and the constructivist view of academic science. It is argued that using more cognitively realistic models in simulations may lead to novel insights. Isaac Naveh obtained a master’s degree in computer science at the University of Missouri. His research interests include hybrid cognitive models and multi-agent learning. Ron Sun is Professor of Cognitive Science at Rensselaer Polytechnic Institute, and formerly the James C. Dowell Professor of Engineering and Professor of Computer Science at University of Missouri-Columbia. He received his Ph.D in 1992 from Brandeis University. His research interest centers around studies of cognition, especially in the areas of cognitive architectures, human reasoning and learning, cognitive social simulation, and hybrid connectionist models. For his paper on integrating rule-based and connectionist models for accounting for human everyday reasoning, he received the 1991 David Marr Award from Cognitive Science Society. He is the founding co-editor-in-chief of the journal Cognitive Systems Research, and also serves on the editorial boards of many other journals. He is the general chair and program chair for CogSci 2006, and a member of the Governing Board of International Neural Networks Society. His URL is: http://www.cogsci.rpi.edu/~rsun     

Computational experimentation on new organizational forms: Exploring behavior and performance of <Emphasis Type="Italic">Edge</Emphasis> organizations

New organizational forms are being conceived and proposed continually, but because many such organizations remain conceptual—and hence have no basis for empirical assessment—their putative advantages over extant organizational forms are difficult to evaluate. Moreover, many such organizational forms are proposed without solid grounding in our cannon of organization theory; hence understanding their various theoretical properties in terms of our familiar, archetypal forms remains difficult. This poses problems for the practitioner and researcher alike. The Edge represents one such, recent, conceptual organizational form, which lacks readily observable examples in practice, and the conceptualization of which is not rooted well in our established organization theory. Nonetheless, proponents of this new form argue its putative advantages over existing counterparts, with an emphasis upon complex, dynamic, equivocal environmental contexts; hence the appeal of this form in today’s organizational environment. The research described in this article employs methods and tools of computational experimentation to explore empirically the behavior and performance of Edge organizations, using the predominant and classic Hierarchy as a basis of comparison. We root our models of these competing forms firmly in Organization Theory, and we make our representations of organizational assumptions explicit via semi-formal models, which can be shared with other researchers. The results reveal insightful dynamic patterns and differential performance capabilities of Hierarchy and Edge organizations, and they elucidate theoretical ramifications for continued research along these lines, along with results amenable to practical application. This work also highlights the powerful role that computational experimentation can play as a complementary, bridging research method. Mark Nissen is Associate Professor of Information Systems and Management at the Naval Postgraduate School. His research focuses on dynamic knowledge and organization. He views work, technology and organization as an integrated design problem, and has concentrated recently on the phenomenology of knowledge flows. Mark’s publications span information systems, project management, organization studies, knowledge management and related fields. In 2000 he received the Menneken Faculty Award for Excellence in Scientific Research, the top research award available to faculty at the Naval Postgraduate School. In 2001 he received a prestigious Young Investigator Grant Award from the Office of Naval Research for work on knowledge-flow theory. In 2002–2003 he was Visiting Professor at Stanford, integrating knowledge-flow theory into agent-based tools for computational modeling. Before his information systems doctoral work at the University of Southern California, he acquired over a dozen years’ management experience in the aerospace and electronics industries.     

A Network Analysis Model for Disambiguation of Names in Lists

In research and application, social networks are increasingly extracted from relationships inferred by name collocations in text-based documents. Despite the fact that names represent real entities, names are not unique identifiers and it is often unclear when two name observations correspond to the same underlying entity. One confounder stems from ambiguity, in which the same name correctly references multiple entities. Prior name disambiguation methods measured similarity between two names as a function of their respective documents. In this paper, we propose an alternative similarity metric based on the probability of walking from one ambiguous name to another in a random walk of the social network constructed from all documents. We experimentally validate our model on actor-actor relationships derived from the Internet Movie Database. Using a global similarity threshold, we demonstrate random walks achieve a significant increase in disambiguation capability in comparison to prior models. Bradley A. Malin is a Ph.D. candidate in the School of Computer Science at Carnegie Mellon University. He is an NSF IGERT fellow in the Center for Computational Analysis of Social and Organizational Systems (CASOS) and a researcher at the Laboratory for International Data Privacy. His research is interdisciplinary and combines aspects of bioinformatics, data forensics, data privacy and security, entity resolution, and public policy. He has developed learning algorithms for surveillance in distributed systems and designed formal models for the evaluation and the improvement of privacy enhancing technologies in real world environments, including healthcare and the Internet. His research on privacy in genomic databases has received several awards from the American Medical Informatics Association and has been cited in congressional briefings on health data privacy. He currently serves as managing editor of the Journal of Privacy Technology. Edoardo M. Airoldi is a Ph.D. student in the School of Computer Science at Carnegie Mellon University. Currently, he is a researcher in the CASOS group and at the Center for Automated Learning and Discovery. His methodology is based on probability theory, approximation theorems, discrete mathematics and their geometries. His research interests include data mining and machine learning techniques for temporal and relational data, data linkage and data privacy, with important applications to dynamic networks, biological sequences and large collections of texts. His research on dynamic network tomography is the state-of-the-art for recovering information about who is communicating to whom in a network, and was awarded honors from the ACM SIG-KDD community. Several companies focusing on information extraction have adopted his methodology for text analysis. He is currently investigating practical and theoretical aspects of hierarchical mixture models for temporal and relational data, and an abstract theory of data linkage. Kathleen M. Carley is a Professor of Computer Science in ISRI, School of Computer Science at Carnegie Mellon University. She received her Ph.D. from Harvard in Sociology. Her research combines cognitive science, social and dynamic networks, and computer science (particularly artificial intelligence and machine learning techniques) to address complex social and organizational problems. Her specific research areas are computational social and organization science, social adaptation and evolution, social and dynamic network analysis, and computational text analysis. Her models meld multi-agent technology with network dynamics and empirical data. Three of the large-scale tools she and the CASOS group have developed are: BioWar a city, scale model of weaponized biological attacks and response; Construct a models of the co-evolution of social and knowledge networks; and ORA a statistical toolkit for dynamic social Network data.     

Privacy Preserving Link Analysis on Dynamic Weighted Graph

Link analysis algorithms have been used successfully on hyperlinked data to identify authoritative documents and retrieve other information. They also showed great potential in many new areas such as counterterrorism and surveillance. Emergence of new applications and changes in existing ones created new opportunities, as well as difficulties, for them: (1) In many situations where link analysis is applicable, there may not be an explicit hyperlinked structure. (2) The system can be highly dynamic, resulting in constant update to the graph. It is often too expensive to rerun the algorithm for each update. (3) The application often relies heavily on client-side logging and the information encoded in the graph can be very personal and sensitive. In this case privacy becomes a major concern. Existing link analysis algorithms, and their traditional implementations, are not adequate in face of these new challenges. In this paper we propose the use of a weighted graph to define and/or augment a link structure. We present a generalized HITS algorithm that is suitable for running in a dynamic environment. The algorithm uses the idea of “lazy update” to amortize cost across multiple updates while still providing accurate ranking to users in the mean time. We prove the convergence of the new algorithm and evaluate its benefit using the Enron email dataset. Finally we devise a distributed implementation of the algorithm that preserves user privacy thus making it socially acceptable in real-world applications. This material is based upon work supported by the National Science Foundation under Grant No. 0222745. Part of this work was presented at the SDM05 Workshop on Link Analysis in Newport Beach, California, April 2005. Yitao Duan is a Ph.D. candidate in Computer Science at the University of California, Berkeley. His research interests include practical privacy enhancing technologies for a variety of situations including: ubiquitous computing, collaborative work, smart spaces, and location-aware services etc. His research goal is to develop provably strong (cryptographic and information-theoretic) protocols that are practically realizable. He received his B.S. and M.S. in Mechanical Engineering from Beijing University of Aeronautics and Astronautics, China in 1994 and 1997. Jingtao Wang is a Ph.D. student in Computer Science at the University of California, Berkeley. His research interests include context-aware computing, novel end-user interaction techniques and statistical machine learning. He was a research member, later a staff research member and team lead at IBM China Research Lab from 1999 to 2002, working on online handwriting recognition technologies for Asian languages. He received his B.E. and M.E. in electrical and computer engineering from Xi'an Jiaotong University, China in 1996 and 1999. He is a member of the ACM and ACM SIGCHI since 2000. Matthew Kam is a Ph.D. student in computer science at the University of California, Berkeley working on educational technology and human-computer interaction for low-income communities in developing regions. He received a B.A. in economics and a B.S. in Electrical Engineering and Computer Sciences, also from Berkeley. He is a member of the ACM and Engineers for a Sustainable World. John Canny is the Paul and Stacy Jacobs Distinguished Professor of Engineering in Computer Science at the University of California, Berkeley. His research is in human-computer interaction, with an emphasis on modeling methods and privacy approaches using cryptography. He received his Ph.D. in 1987 at the MIT AI Lab. His dissertation on Robot Motion Planning received the ACM dissertation award. He received a Packard Foundation Faculty Fellowship and a Presidential Young Investigator Award. His peer-reviewed publications span robotics, computational geometry, physical simulation, computational algebra, theory and algorithms, information retrieval, HCI and CSCW and cryptography.     

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.     

A Dynamic Simulation Comparing Classical and Emergent-Network Models: Organizational Design Implications

In the last decade, organizations have spent more on the creation, transformation, and communication of information than on the production of physical goods. The information age has been ushered in by the widespread assimilation of information and communication technologies. Many contemporary practitioners and organizational theorists predict the demise of the classical organizational design because of its inability to accommodate the sociological change engendered by the information age.The current study advances an emergent-network model of organizational design and compares it to the classical approach through a dynamic simulation of prototypical organizational activities. Organizational activities approximating one year were simulated in each of five organizations under constant baseline conditions and over one hundred experimental design conditions. The emergent network model manifested higher levels of goal attainment, resource utilization, and organizational capacity for accommodating change. These findings suggest that organizations will benefit from conformance to the design principals of the emergent-network model.Bernard D. Hill Jr. earned his Ph.D. in Public Policy and Administration from Virginia Commonwealth University. He also holds a Master of Science in Business and a Bachelor of Science in Education. Bernard is currently employed as a Chief Information Officer with the Commonwealth of Virginia. He has held a broad array of technology leadership positions in both the public and private sectors and the academic arena. Bernard was selected as one of Computerworld’s Premier 100 IT Leaders for 2002. As CIO for the Virginia Department of Transportation, he also brokered a public-private partnership that won a nationwide Government Technology Leadership Award. This partnership provides security awareness training for employees in Virginia State government, as well as cities, counties, and localities throughout Virginia.Heinz Roland Weistroffer is an Associate Professor of Information Systems in the School of Business at Virginia Commonwealth University. Roland holds a Doctor of Science degree from the Free University Berlin, Germany, and a Master of Arts degree from Duke University. Previous appointments include Chief Research Officer at the Council for Scientific and Industrial Research in Pretoria, South Africa, and Senior Lecturer at the University of Natal in Durban, South Africa. Roland’s current research interests include computer assisted decision support, computer simulation modeling, object oriented modeling, and software engineering. He has published in IEEE Transactions on Software Engineering, IEEE Transactions on Systems, Man and Cybernetics, the Journal of Multi-Criteria Decision Analysis, and Socio-Economic Planning Sciences, among other journals.Peter Aiken is Director of the Institute for Data Research and an Associate Professor of Information Systems at Virginia Commonwealth University. His research has widely explored the area of data engineering and its relationship to systems and business reengineering. He is the author of Data Reverse Engineering and Clive Finkelstein’s co-author of Corporate Information Portals (McGraw-Hill 1996/99). His sixth book is titled XML in Data Management and is co-authored with David Allen. He has held leadership positions with the US Department of Defense and consulted with more than 50 organizations in 14 different counties. His research publications have appeared in the Communications of the ACM, IBM Systems Journal, IEEE Software and many others. He is a member of ACM, and the IEEE (Senior Member). He has been a DAMA International Advisor since 1999 and received their 2001 International Achievement Award. He has lectured internationally on these and related topics.     

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)