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.     

Introduction to normative multiagent systems

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

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

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

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.     

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.     

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)     

A connectionist model of the organizational learning curve

Organizational learning can be understood as a spontaneous development of routines. Mathematically, this process can be described as a search for better paths on a graph whose nodes are humans and machines. Since the rules for connecting nodes depend on their ability to process goods, the slope of the learning curve may be connected to physical and psychological properties. Two suggestive examples are discussed. Guido Fioretti, born 1964, graduated in Electronic Engineering and obtained a PhD in Economics from the University of Rome “La Sapienza”. He is currently an assistant professor at the University of Bologna, Italy.His research interests span from decision theory to economics and organization science. In particular, he is interested in linking structural development to cognitive processes. The present article has been conceived as a theoretical underpinning of agent-based simulations of organizations. In particular, future applications of the Java Enterprise Simulator () may test the usefulness of the results derived herein.     

Normative KGP agents

We extend the logical model of agency known as the KGP model, to support agents with normative concepts, based on the roles an agent plays and the obligations and prohibitions that result from playing these roles. The proposed framework illustrates how the resulting normative concepts, including the roles, can evolve dynamically during the lifetime of the agent. Furthermore, we illustrate how these concepts can be combined with the existing capabilities of KGP agents in order to plan for their goals, react to changes in the environment, and interact with other agents. Our approach gives an executable specification of normative concepts that can be used directly for prototyping applications. Fariba Sadri is a senior lecturer at Imperial College London, from where she received her PhD. Her earlier work concentrated on integrity of deductive databases and temporal reasoning, in particular using the event calculus. In more recent years her work has been on agent technologies and multi-agent systems. She has worked on logic-based agent models, reasoning, dynamic belief revision, and inter-agent communication and negotiation for resources. She was co-awarded an EPSRC grant for research into logic-based multi-agents and was co-investigator in the EU SOCS project. Kostas Stathis is a senior lecturer at Royal Holloway, University of London and he holds a PhD from Imperial College London. His research interests are in the area of computational intelligence in general and in the intersection of computational logic and cognitive systems for social computing applications in particular. His research interests include: representation of human-computer (or computer-computer) interaction as a game; cognitive & autonomous agents; artificial agent societies; agent communication; programmable agents and agent platforms. He is a co-investigator of the EU ArguGRID project and was a co-investigator of the EU SOCS project. Francesca Toni is a senior lecturer at Imperial College London, from where she received her PhD. Her earlier work focused on abductive reasoning. In more recent years, she focused on argumentation, agent models and multi-agent systems. She has worked on computational logic-based agent models, agent reasoning, dynamic belief revision, and inter-agent communication and negotiation for resources. She has been co-ordinator of the EU SOCS project, which developed the KGP model of agency, and is coordinator of the EU ArguGRID project, on the application of argumentative agents within grid systems.     

Speech acts, commitment and multi-agent communication

The principle aim of this paper is to reconsider the suitability of Austin and Searle’s Speech Act theory as a basis for agent communication languages. Two distinct computational interpretations of speech acts are considered: the standard “mentalistic” approach associated with the work of Cohen and Levesque which involves attributing beliefs and intentions to artificial agents, and the “social semantics” approach originating (in the context of MAS) with Singh which aims to model commitments that agents undertake as a consequence of communicative actions. Modifications and extensions are proposed to current commitment-based analyses, drawing on recent philosophical studies by Brandom, Habermas and Heath. A case is made for adopting Brandom’s framework of normative pragmatics, modelling dialogue states as deontic scoreboards which keep track of commitments and entitlements that speakers acknowledge and hearers attribute to other interlocutors. The paper concludes by outlining an update semantics and protocol for selected locutions. Rodger Kibble is a Lecturer in the Department of Computing, Goldsmiths College, University of London. He has worked as a researcher at the Information Technology Research Institute, University of Brighton, and the School of Oriental and African Studies, University of London. He received his PhD from the Centre for Cognitive Science in the University of Edinburgh in 1997. He has published conference papers and journal articles in the formal semantics of natural language, natural language generation, anaphora resolution, dialogue modelling, argumentation and multi-agent communication; and coedited Information Sharing: Reference and Presupposition in Language Generation and Interpretation (CSLI, 2002).     

Complexity theory and collaboration: An agent-based simulator for a space mission design team

In this paper, we investigate how complexity theory can benefit collaboration by applying an agent-based computer simulation approach to a new form of synchronous real-time collaborative engineering design. Fieldwork was conducted with a space mission design team during their actual design sessions, to collect data on their group conversations, team interdependencies, and error monitoring and recovery practices. Based on the fieldwork analysis, an agent-based simulator was constructed. The simulation shows how error recovery and monitoring is affected by the number of small group, or sidebar, conversations, and consequent noise in the room environment. This simulation shows that it is possible to create a virtual environment with cooperating agents interacting in a dynamic environment. This simulation approach is useful for identifying the best scenarios and eliminating potential catastrophic combinations of parameters and values, where error recovery and workload in collaborative engineering design could be significantly impacted. This approach is also useful for defining strategies for integrating solutions into organizations. Narjès Bellamine-Ben Saoud is an Associate Professor at the University of Tunis and Researcher at RIADI-GDL Laboratory, Tunisia. After Computer Science engineering diploma (1993) of the ENSEEIHT of Toulouse, France, she received her PhD (1996), on groupware design applied to the study of cooperation within a space project, from the University of Toulouse I, France. Her main research interests concern studying complex systems particularly by modeling and simulating collaborative and socio-technical systems; developing Computer Supported Collaborative Learning in tunisian primary schools; and Software Engineering. Her current reserach projects include modeling and simulation of emergency rescue activities for large-scale accidents, modeling of epidemics and study of malaria, simulation of collabration artifacts. Gloria Mark is an Associate Professor in the Department of Informatics, University of California, Irvine. Dr. Mark received her Ph.D. in Psychology from Columbia University in 1991. Prior to UCI, she was a Research Scientist at the GMD, in Bonn, Germany, a visiting research scientist at the Boeing Company, and a research scientist at the Electronic Data Systems Center for Advanced Research. Dr. Mark’s research focuses on the design and evaluation of collaborative systems. Her current projects include studying worklife in the network-centric organization, multi-tasking of information workers, nomad workers, and a work in a large-scale medical collaboratory. Dr. Mark is widely published in the fields of CSCW and HCI, is currently the program co-chair for the ACM CSCW’06 conference and is on the editorial board of Computer Supported Cooperative Work: The Journal of Collaborative Computing, and e-Service Qu@rterly.     

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.     

Self-regulation through social institutions: A framework for the design of open agent-based electronic marketplaces

In this paper, we argue that allowing self-interested agents to activate social institutions during runtime can improve the robustness (i.e., stability, reliability, or scalability) of open multiagent systems (MAS). Referring to sociological theory, we consider institutions to be rules that need to be activated and adopted by the agent population during runtime and propose a framework for self-regulation of MAS for the domain of electronic marketplaces. The framework consists of three different institutional types that are defined by the mechanisms and instances that generate, change or safeguard them. We suggest that allowing autonomous agents both the reasoning about their compliance with a rule and the selection of an adequate institutional types helps to balance the trade-off between the autonomy of self-interested agents and the maintenance of social order (cf. Castelfranchi, 2000) in MAS, and to ensure almost the same qualities as in closed environments. A preliminary report of the evaluation of the prototype by empirical simulations is given. Christian S. Hahn studied computer science and economics at Saarland University and received his diploma in 2004. Currently, he works in a project of the priority program ‘Socionics’ funded by the German Research Foundation at the German Research Center for Artificial Intelligence (DFKI). Bettina Fley studied sociology, economics, law, and social and economic history at the University of Hamburg and received her diploma in 2002. She currently works in a project in the priority program ‘Socionics’, which is funded by the German Research Foundation (DFG), at the Department of Technology Assessment at the Hamburg University of Technology. Michael Florian, received his master in sociology at the University of Münster, where he also finished his doctoral degree in 1993. Since 1995, he holds a position as a senior researcher (‘Oberingenieur’) at the Department of Technology Assessment at the Hamburg University of Technology and heads the sociological part of a project in the priority program ‘Socionics’ funded by the German Research Foundation (DFG).     

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.     

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     

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.     

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.     

An agent-based investigation into the new product development capability

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

An interview with Laurens de Haan

Laurens de Haan was born January 15, 1937 in Rotterdam, The Netherlands. He graduated 1966 in mathematics and received a doctoral degree in 1970 from the University of Amsterdam, while working at the Mathematical center CWI in Amsterdam. Since 1973 he was Professor for probability and mathematical statistics at the Econometric Institute of the Economic Faculty at the Erasmus University Rotterdam, where he retired 1998. Since 2008 he is part-time professor at the Department of Econometrics and Operations Research of Tilburg University. Laurens de Haan has been active in research throughout his career. He has published more than 110 scientific papers. Among other distinctions, he was elected IMS fellow for his seminal contributions to extreme value theory in 1977, and he was appointed Honorary Doctor of the University of Lisbon in 2000.     

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.     

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