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.     

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.     

Can tools help unify organization theory? Perspectives on the state of computational modeling

Scholars engaged in the study of work group and organizational behavior are increasingly calling for the use of integrated methods in conducting research, including the wider adoption of computational models for generating and testing new theory. Our review of the state of modern computational modeling incorporating social structures reveals steady increases in the incorporation of dynamic, adaptive, and realistic behaviors of agents in network settings, yet exposes gaps that must be addressed in the next generation of organizational simulation systems. We compare 28 models according to more than two hundred evaluation criteria, ranging from simple representations of agent demographic and performance characteristics, to more richly defined instantiations of behavioral attributes, interaction with non-agent entities, model flexibility, communication channels, simulation types, knowledge, transactive memory, task complexity, and resource networks. Our survey assesses trends across the wide set of criteria, discusses practical applications, and proposes an agenda for future research and development. Michael J. Ashworth is a doctoral candidate in computational organization science at Carnegie Mellon University, where he conducts research on social, knowledge, and transactive memory networks along with their effects on group and organizational learning and performance. Practical outcomes of his work include improved understanding of the impact of technology, offshoring, and turnover on organizational performance. Mr. Ashworth has won several prestigious grants from the Sloan Foundation and the National Science Foundation to pursue his research on transactive memory networks. Journals in which his research has appeared include Journal of Mathematical Sociology, International Journal of Human Resource Management, and Proceedings of the International Conference on Information Systems. His recent work on managing human resource challenges in the electric power industry has been featured in the Wall Street Journal and on National Public Radio's ``Morning Edition.' Mr. Ashworth received his undergraduate degree in systems engineering from the Georgia Institute of Technology. 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), a university-wide interdisciplinary center that brings together network analysis, computer science and organization science (www.casos.ece.cmu.edu). Prof. Carley carries out research that combines cognitive science, dynamic social networks, text processing, organizations, social and computer science in a variety of theoretical and applied venues. Her specific research areas are computational social and organization theory; dynamic social networks; multi-agent network models; group, organizational, and social adaptation, and evolution; statistical models for dynamic network analysis and evolution, computational text analysis, and the impact of telecommunication technologies on communication and information diffusion within and among groups. Prof. Carley has undergraduate degrees in economics and political science from MIT and a doctorate in sociology from Harvard University.     

Some Simple Algorithms for Structural Comparison

Structural comparison (i.e., the simultaneous analysis of multiple structures) is a problem which arises frequently in such diverse arenas as the study of organizational forms, social network analysis, and automated text analysis. Prior work has demonstrated the applicability of a range of standard multivariate analysis procedures to the structural comparison problem. Here, some simple algorithms are provided which elucidate several of these methods in an easily implemented form. Carter T. Butts is Assistant Professor at the University of California-Irvine in the Department of Sociology, and is a member of the Institute for Mathematical Behavioral Sciences and the California Institute for Telecommunications and Information Technology. His current research focuses on communication during disasters, Bayesian inference for network data, network comparison, and the structure of spatially embedded interpersonal networks. Kathleen M. Carley is Professor at Carnegie Mellon University, with appointments in the Institute for Software Research International, the H.J. Heinz III School of Public Policy and Management, and the Department of Engineering and Public Policy. Her research centers around areas of social, organizational, knowledge and information networks, organizational design, change, adaptivity and and performance, computational organization theory, crisis management, social theory, impacts on information diffusion of changes in social policy and changes in communication technology, and mapping experts' and executives' knowledge networks using textual analysis techniques.     

Predictive analysis of concealed social network activities based on communication technology choices: early-warning detection of attack signals from terrorist organizations

Terrorist threat prevention and counteraction require timely detection of hostile plans. However, adversary efforts at concealment and other challenges involved in monitoring terrorist organizations may impede timely intelligence acquisition or interpretation. This study develops an approach to analyzing technological means rather than content of communications produced within the social networks comprising covert organizations, and shows how it can be applied towards detecting terrorist attack precursors. We find that differential usage patterns of hi-tech versus low-tech communication solutions could reveal significant information about organizational activities, which may be further used to detect signals of impending terrorist attacks. (Such potential practical utility of our method is supported by the detailed empirical analysis of available al Qaeda communications.) The described approach thus provides a common framework for utilizing diverse activity records from heterogeneous sources as well as contributes new tools for their rapid analysis aimed at better informing operational and policy decision-making.     

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.     

Intra-organizational Networks and Performance: A Review

Intra-organizational network research had its first heyday during the empirical revolution in social sciences before World War II when it discovered the informal group within the formal organization. These studies comment on the classic sociological idea of bureaucracy being the optimal organization. Later relational interest within organizational studies gave way to comparative studies on the quantifiable formal features of organizations. There has been a resurgence in intra-organizational networks studies recently as the conviction grows that they are critical to organizational and individual performance. Along with methodological improvements, the theoretical emphasis has shifted from networks as a constraining force to a conceptualization that sees them as providing opportunities and finally, as social capital. Because of this shift it has become necessary not only to explain the differences between networks but also their outcomes, that is, their performance. It also implies that internal and external networks should no longer be treated separately.Research on differences between intra-organizational networks centers on the influence of the formal organization, organizational demography, technology and environment. Studies on outcomes deal with diffusion and adaptation of innovation; the utilization of human capital; recruitment, absenteeism and turnover; work stress and job satisfaction; equity; power; information efficiency; collective decision making; mobilization for and outcomes of conflicts; social control; profit and survival of firms and individual performance.Of all the difficulties that are associated with intra-organizational network research, problems of access to organizations and incomparability of research findings seem to be the most serious. Nevertheless, future research should concentrate on mechanisms that make networks productive, while taking into account the difficulties of measuring performance within organizations, such as the performance paradox and the halo-effect.     

My agents love to conform: Norms and emotion in the micro-macro link

This contribution investigates the function of emotion in relation to norms, both in natural and artificial societies. We illustrate that unintentional behavior can be normative and socially functional at the same time, thereby highlighting the role of emotion. Conceiving of norms as mental objects we then examine the role of emotion in maintaining and enforcing such propositional attitudes. The findings are subsequently related to social structural dynamics and questions concerning micro-macro linkage, in natural societies as well as in artificial systems. Finally, we outline the possibilities of an application to the socionic multi-agent architecture SONAR. Christian von Scheve graduated in Sociology with minors in Psychology, Economics, and Political Science at the University of Hamburg, where he also worked as a research assistant at the Institute of Sociology. Currently, he is a 3rd year PhD student at the University of Hamburg. He was a Fellow of the Research Group “Emotions as Bio-Cultural Processes” at the Center for Interdisciplinary Research (ZiF) at Bielefeld University. In his doctoral thesis he develops an interdisciplinary approach to emotion and social structural dynamics, integrating emotion theories from the neurosciences, psychology, and the social sciences. He has published on the role of emotion in large-scale social systems, human-computer interaction, and multi-agent systems. He is co-editor of a forthcoming volume on emotion regulation. Daniel Moldt received his BSc in Computer Science/Software Engineering from the University of Birmingham (England) in 1984, graduated in Informatics at the University of Hamburg, with a minor in Economics in 1990. He received his PhD in Informatics from the University of Hamburg in 1996, where he has been a researcher and lecturer at the Department of Informatics since 1990. Daniel Moldt is also the head of the Laboratory for Agent-Oriented Systems (LAOS) of the theoretical foundations group at the Department of Informatics. His research interests focus on theoretical foundations, software engineering and distributed systems with an emphasis on agent technology, Petri nets, specification languages, intra- and inter-organizational application development, Socionics and emotion in informatics. Julia Fix is currently a PhD student at the Theoretical Foundations of Computer Science Group, Department for Informatics at the University of Hamburg. She studied Informatics and Psychology at the University of Hamburg, with an emphasis on theoretical foundations of multi-agent systems and wrote her diploma theses about emotional agent systems. Her current research interests focus on conceptual challenges and theoretical foundations of modelling emotions in multi-agent systems, emotion-based norm enforcement and maintenance, and Socionics. A further research focus are Petri nets, in particular the use of Petri-net modelling formalisms for representing different aspects of emotion in agent systems. Rolf von Lüde is a professor of Sociology at the University of Hamburg with a focus in teaching and research in Sociology of Organizations, Work and Industry since 1996. He graduated in Economics, Sociology, and Psychology, and received his doctorate in Economics and the venia legendi in Sociology from the University of Dortmund. His current research focuses on labor conditions, the organization of production, social change and the educational system, the organizational structures of university, Socionics as a new approach to distributed artificial intelligence in cooperation with computer scientists, new public management, and emotions and social structures. Rolf von Lüde is currently Head of Department of Social Sciences and Vice Dean of the School of Business, Economics and Social Sciences, University of Hamburg.     

Sheaf toposes for realizability

We compare realizability models over partial combinatory algebras by embedding them into sheaf toposes. We then use the machinery of Grothendieck toposes and geometric morphisms to study the relationship between realizability models over different partial combinatory algebras. This research is part of the Logic of Types and Computation project at Carnegie Mellon University under the direction of Dana Scott.     

Object oriented modeling of social networks

The aim of this paper is to explain principles of object oriented modeling in the scope of modeling dynamic social networks. As such, the approach of object oriented modeling is advocated within the field of organizational research that focuses on networks.We provide a brief introduction into the field of social networks and present an overview of existing network models and methods. Subsequently we introduce an elementary problem field in the social sciences in general, and in studies of organizational change and design in particular: the micro-macro link. We argue that the most appropriate way to hadle this problem is the principle of methodological individualism. For social network analysis, to contribute to this theoretical perspective, it should include an individual choice mechanism and become more dynamically oriented. Subsequently, object oriented modeling is advocated as a tool to meet these requirements for social network analysis. We show that characteristics of social systems that are emphasized in the methodological individualistic approach have their direct equivalences in object oriented models. The link between the micro level where actors act, and the macro level where phenomena occur as a consequence and cause of these actions, can be modelled in a straightforward way.     

NETEST: Estimating a Terrorist Network's Structure—Graduate Student Best Paper Award, CASOS 2002 Conference

Since the events of September 11, 2001, the United States has found itself engaged in an unconventional and asymmetric form of warfare against elusive terrorist organizations. Defense and investigative organizations require innovative solutions that will assist them in determining the membership and structure of these organizations. Data on covert organizations are often in the form of disparate and incomplete inferences of memberships and connections between members. NETEST is a tool that combines multi-agent technology with hierarchical Bayesian inference models and biased net models to produce accurate posterior representations of a network. Bayesian inference models produce representations of a network's structure and informant accuracy by combining prior network and accuracy data with informant perceptions of a network. Biased net theory examines and captures the biases that may exist in a specific network or set of networks. Using NETEST, an investigator has the power to estimate a network's size, determine its membership and structure, determine areas of the network where data is missing, perform cost/benefit analysis of additional information, assess group level capabilities embedded in the network, and pose what if scenarios to destabilize a network and predict its evolution over time.     

Stable network topologies using the notion of covering

An alternative perspective to evaluate networks and network evolution is introduced, based on the notion of covering. For a particular node in a network covering captures the idea of being outperformed by another node in terms of, for example, visibility and possibility of information gathering. In this paper, we focus on networks where these subdued network positions do not exist. We call these networks stable. Within this set we identify the minimal stable networks, which frequently have a ‘bubble-like’ structure. Severing a link in such a network results in at least one of the nodes being covered. In a minimal stable network therefore all nodes cooperate to avoid that one of the nodes ends up in a subdued position. Our results can be applied to, for example, the design of (covert) communication networks and the dynamics of social and information networks.     

Measuring agility of networked organizational structures via network entropy and mutual information

While the agility of networked organizational structures is important for organizational performance, studies on how to evaluate it remain scant, probably because the difficulty in measuring network evolution. In this conceptual paper, we propose two measures - network entropy and mutual information - to characterize the agility of networked organizational structure. Rooted in graph theory and information theory, these two measures capture network evolution in a comprehensive and parsimonious way. They indicate the uncertainty (or disorder) at the network level as well as the degree distribution at the individual level. We also propose an algorithm for applying them in the scenario of adding links to a network while holding the number of nodes fixed. Both simulated and real networks are used for demonstration. Implications and areas for future research are discussed in the end.     

Social network topology: a Bayesian approach

Elucidating the pattern of links within social networks is a challenging problem. Of particular difficulty is determining the existence of links in those groups that take active measures to conceal their internal connections, such as terrorist or criminal organizations where conventional social network analysis data-gathering techniques cannot be applied. Network representations of such organizations are useful, because they often represent a useful point of departure in thinking both about the potential capabilities of organizations and how to conduct effective measures to counter them. Developing an effective process for constructing such network representations from incomplete and limited data of variable quality is a topic of much current interest. Here, a method based on Bayesian inference is presented that probabilistically infers the existence of links within a social network. It is tested on data from open source publications. Additionally, the method represents a possible approach to dynamically modelling networks, as it is feasible to calculate how a network will reconfigure following an intervention.     

Using activity focus networks to pressure terrorist organizations

The predominant idea for using network concepts to fight terrorists centers on disabling key parts of their communication networks. Although this counternetwork strategy is clearly a sound approach, it is vulnerable to missing, incomplete, or erroneous information about the network. This paper describes a different and complementary application of network concepts to terrorist organizations. It is based on activity focus networks (AFNs), which represent the complex activity system of an organization. An activity focus is a conceptual or physical entity around which joint activity is organized. Any organization has a number of these, which are in some cases compatible and in some cases incompatible. The set of foci and their relations of compatibility and incompatibility define the AFN. A hypothetical AFN for a terrorist organization is specified and tested in a simulation called AQAS. It shows that certain activity foci, and in particular one combination, have high potential as pressure points for the activity system. The AFN approach complements the counternetwork approach by reducing the downside risk of incomplete information about the communication network, and enhancing the effectiveness of counternetwork approaches over time. Steven R. Corman is Professor in the Hugh Downs School of Human Communication at Arizona State University and Chair of the Organizational Communication Division of the International Communication Association. His research interests include communication networks and activity systems, high-resolution text and discourse analysis, and modeling and simulation of human communication systems.     

Communication and organizational social networks: a simulation model

Recent management research has evidenced the significance of organizational social networks, and communication is believed to impact the interpersonal relationships. However, we have little knowledge on how communication affects organizational social networks. This paper studies the dynamics between organizational communication patterns and the growth of organizational social networks. We propose an organizational social network growth model, and then collect empirical data to test model validity. The simulation results agree well with the empirical data. The results of simulation experiments enrich our knowledge on communication with the findings that organizational management practices that discourage employees from communicating within and across group boundaries have disparate and significant negative effect on the social network’s density, scalar assortativity and discrete assortativity, each of which correlates with the organization’s performance. These findings also suggest concrete measures for management to construct and develop the organizational social network.     

Destroying stationary sets

We present a forcing poset for destroying the stationarity of certain subsets ofP _kk⁺. Using this poset along with Prikry forcing techniques we establish some consistency results concerning saturated ideals andS(k, k ⁺). This paper forms a part of the author’s Ph.D. dissertation written under the supervision of Professor Cummings at Carnegie Mellon University.     

Computational organizational network modeling: Strategies and an example

This paper articulates the logic of computational organizational modeling as a strategy for theory construction and testing in the field of organizational communication networks. The paper introduces, Blanche, and objectoriented simulation environment that supports quantitative modeling and analysis of the evolution of organizational networks. Blanche relies on the conceptual primitives of attributes that describe network nodes and links that connect these nodes. Difference equations are used to model the dynamic properties of the network as it changes over time. This paper describes the design of Blanche and how it supports both the process of theory construction, model building and analysis of results. The paper concludes with an empirical example, to test the predictions of a network-based social influence model for the adoption of a new communication technology in the workplace.     

Destabilization of covert networks

Covert networks are often difficult to reason about, manage and destabilize. In part, this is because they are a complex adaptive system. In addition, this is due to the nature of the data available on these systems. Making these covert networks less adaptive, more predictable, more consistent will make it easier to contain or constrain their activity. But, how can we inhibit adaptation? Herein, covert networks are characterized as dynamic multi-mode multi-plex networks. Dynamic network analysis tools are used to assess their structure and identify effective destabilization strategies that inhibit the adaptivity of these groups.     

Diffusion dynamics in small-world networks with heterogeneous consumers

Diffusions of new products and technologies through social networks can be formalized as spreading of infectious diseases. However, while epidemiological models describe infection in terms of transmissibility, we propose a diffusion model that explicitly includes consumer decision-making affected by social influences and word-of-mouth processes. In our agent-based model consumers’ probability of adoption depends on the external marketing effort and on the internal influence that each consumer perceives in his/her personal networks. Maintaining a given marketing effort and assuming its effect on the probability of adoption as linear, we can study how social processes affect diffusion dynamics and how the speed of the diffusion depends on the network structure and on consumer heterogeneity. First, we show that the speed of diffusion changes with the degree of randomness in the network. In markets with high social influence and in which consumers have a sufficiently large local network, the speed is low in regular networks, it increases in small-world networks and, contrarily to what epidemic models suggest, it becomes very low again in random networks. Second, we show that heterogeneity helps the diffusion. Ceteris paribus and varying the degree of heterogeneity in the population of agents simulation results show that the more heterogeneous the population, the faster the speed of the diffusion. These results can contribute to the development of marketing strategies for the launch and the dissemination of new products and technologies, especially in turbulent and fashionable markets. This paper won the best student paper award at the North American Association for Computational Social and Organizational Science (NAACSOS) Conference 2005, University of Notre Dame, South Bend, Indiana, USA. Preceding versions of this paper have been presented to the Conference of the North American Association for Computational Social and Organizational Science (NAACSOS), 2005, University of Notre Dame, South Bend, USA and to the Conference of the European Social Simulation Association (ESSA), 2005, Koblenz, Germany. Sebastiano Alessio Delre received his Master Degree in Communication Science at the University of Salerno. After one year collaboration at the Institute of Science and Technologies of Cognition (ISTC, Rome, Italy), now he is a PhD student at the faculty of economics, University of Groningen, the Netherlands. His work focus on how different network structures affect market dynamics. His current application domain concerns Agent-Based Simulation Models for social and economic phenomena like innovation diffusion, fashions and turbulent market. Wander Jager is an associate professor of marketing at the University of Groningen. He studied social psychology and obtained his PhD in the behavioral and social sciences, based on a dissertation about the computer modeling of consumer behaviors in situations of common resource use. His present research is about consumer decision making, innovation diffusion, market dynamics, crowd behavior, stock-market dynamics and opinion dynamics. In his work he combines methods of computer simulation and empirical surveys. He is involved in the management committee of the European Social Simulation Association (ESSA). Marco Janssen is an assistant professor in the School of Human Evolution and Social Change and in the Department of Computer Science and Engineering at Arizona State University. He got his degrees in Operations Research and Applied Mathematics. During the last 15 years, he uses computational tools to study social phenomena, especially human-environmental interactions. His present research focuses on diffusion dynamics, institutional innovation and robustness of social-ecological systems. He combined computational studies with laboratory and field experiments, case study analysis and archeological data. He is an associate editor-in-chief of the journal Ecology and Society.