Evolutionary computation and agent-based modeling: biologically-inspired approaches for understanding complex social systems

Computational social science in general, and social agent-based modeling (ABM) simulation in particular, are challenged by modeling and analyzing complex adaptive social systems with emergent properties that are hard to understand in terms of components, even when the organization of component agents is know. Evolutionary computation (EC) is a mature field that provides a bio-inspired approach and a suite of techniques that are applicable to and provide new insights on complex adaptive social systems. This paper demonstrates a combined EC-ABM approach illustrated through the RebeLand model of a simple but complete polity system. Results highlight tax rates and frequency of public issue that stress society as significant features in phase transitions between stable and unstable governance regimes. These initial results suggest further applications of EC to ABM in terms of multi-population models with heterogeneous agents, multi-objective optimization, dynamic environments, and evolving executable objects for modeling social change.     

Crop planning optimization model: the validation and verification processes

Each optimization problem in the area of natural resources claims for a specific validation and verification (V&V) procedures which, for overwhelming majority of the models, have not been developed so far. In this paper we develop V&V procedures for the crop planning optimization models in agriculture when the randomness of harvests is considered and complex crop rotation restrictions must hold. We list the criteria for developing V&V processes in this particular case, discuss the restrictions given by the data availability and suggest the V&V procedures. To show its relevance, they are applied to recently constructed stochastic programming model aiming to serve as a decision support tool for crop plan optimization in South Moravian farm. We find that the model is verified and valid and if applied in practice—it thus offers a plausible alternative to standard decision making routine on farms which often leads to breaking the crop rotation rules.     

Agent-based simulation of innovation diffusion: a review

Mathematical modeling of innovation diffusion has attracted strong academic interest since the early 1960s. Traditional diffusion models have aimed at empirical generalizations and hence describe the spread of new products parsimoniously at the market level. More recently, agent-based modeling and simulation has increasingly been adopted since it operates on the individual level and, thus, can capture complex emergent phenomena highly relevant in diffusion research. Agent-based methods have been applied in this context both as intuition aids that facilitate theory-building and as tools to analyze real-world scenarios, support management decisions and obtain policy recommendations. This review addresses both streams of research. We critically examine the strengths and limitations of agent-based modeling in the context of innovation diffusion, discuss new insights agent-based models have provided, and outline promising opportunities for future research. The target audience of the paper includes both researchers in marketing interested in new findings from the agent-based modeling literature and researchers who intend to implement agent-based models for their own research endeavors. Accordingly, we also cover pivotal modeling aspects in depth (concerning, e.g., consumer adoption behavior and social influence) and outline existing models in sufficient detail to provide a proper entry point for researchers new to the field.     

The virtual design team: A computational model of project organizations

Large scale and multidisciplinary engineering projects (e.g., design of a hospital building) are often complex. They usually involve many interdependent activities and require intensive coordination among actors (i.e., designers) to deal with activity interdependencies. To make such projects more effective and efficient, one needs to understand how coordination requirements are generated and what coordination mechanisms should be applied for given project situations. Our research on the Virtual Design Team (VDT) attempts to develop a computational model of project organizations to analyze how activity interdependencies raise coordination needs and how organization design and communication tools change team coordination capacity and project performance. The VDT model is built based on contingency theory (Galbraith, 1977) and our observations about collaborative and multidisciplinary work in large, complex projects. VDT explicitly models actors, activities, communication tools and organizations. Based on our extended information-processing view of organizations, VDT simulates the actions of, and interactions among actors as processes of attention allocation, capacity allocation, and communication. VDT evaluates organization performance by measuring emergent project duration, direct cost, and coordination quality. The VDT model has been tested internally, and evaluated externally through case-studies. We found three way qualitative consistency among predictions of the simulation model, of organization theory, and of experienced project managers. In this paper, we present the VDT model in detail and discuss some general issues involved in computational organization modeling, including level of abstraction of tasks and actors' reasoning, and model validation.     

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.     

Inadvertent leaks: exploration via agent-based dynamic network simulation

A significant volume of information leaks in organizations are inadvertent, a form of information spillage. Because the leakage of information is driven by the complex interaction of technology, social, and behavioral factors, we use a hybrid agent-based and dynamic network model, Construct, to simulate the flow of sensitive information in knowledge-driven organizations. Because interaction patterns often change when an organization is under stress, we simulate stress to the organization with effect-based (reliability and integrity) crisis scenarios. Using a virtual experiment, we vary the crisis scenarios, organization’s structure, IT connections, and pressure to separate personnel based on security ratings. Our experiment suggests that the organization’s structure, IT connections, separation pressure, and typical performance all influence how much an organization suffers from inadvertent leakage. In evaluating how organizations respond to crisis, organizations with stove-piped IT tend to clamp down on leakage in response to the crisis, while organizations with Mesh IT tend to have more leakage. Integrity crises tend to decrease leakage; while reliability crises tend to increase leakage in organizations, especially those with Mesh-based IT.     

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

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

Generative Models of Segregation: Investigating Model-Generated Patterns of Residential Segregation by Ethnicity and Socioeconomic Status

This paper considers the potential for using agent models to explore theories of residential segregation in urban areas. Results of generative experiments conducted using an agent-based simulation of segregation dynamics document that varying a small number of model parameters representing constructs from urban-ecological theories of segregation can generate a wide range of qualitatively distinct and substantively interesting segregation patterns. The results suggest how complex, macro-level patterns of residential segregation can arise from a small set of simple micro-level social dynamics operating within particular urban-demographic contexts. The promise and current limitations of agent simulation studies are noted and optimism is expressed regarding the potential for such studies to engage and contribute to the broader research literature on residential segregation.     

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     

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

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

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     

Opinion transmission in organizations: an agent-based modeling approach

This paper builds a theoretical framework to detect the conditions under which social influence enables persistence of a shared opinion among members of an organization over time, despite membership turnover. It develops agent-based simulations of opinion evolution in an advice network, whereby opinion is defined in the broad sense of shared understandings on a matter that is relevant for an organization’s activities, and on which members have some degree of discretion. We combine a micro-level model of social influence that builds on the “relative agreement” approach of Deffuant et al. (J. Artif. Soc. Simul. 5:4, 2002), and a macro-level structure of interactions that includes a flow of joiners and leavers and allows for criteria of advice tie formation derived from, and grounded in, the empirical literature on intra-organizational networks. We provide computational evidence that persistence of opinions over time is possible in an organization with joiners and leavers, a result that depends on circumstances defined by mode of network tie formation (in particular, criteria for selection of advisors), individual attributes of agents (openness of newcomers to influence, as part of their socialization process), and time-related factors (turnover rate, which regulates the flow of entry and exit in the organization, and establishes a form of endogenous hierarchy based on length of stay). We explore the combined effects of these factors and discuss their implications.     

The impact of socio-technical communication styles on the diversity and innovation potential of global science collaboratories

Emerging cyber-infrastructure tools are enabling scientists to transparently co-develop, share, and communicate about real-time diverse forms of knowledge artifacts. In these environments, communication preferences of scientists are posited as an important factor affecting innovation capacity and robustness of social and knowledge network structures. Scientific knowledge creation in such communities is called global participatory science (GPS). Recently, using agent-based modeling and collective action theory as a basis, a complex adaptive social communication network model (CollectiveInnoSim) is implemented. This work leverages CollectiveInnoSim implementing communication preferences of scientists. Social network metrics and knowledge production patterns are used as proxy metrics to infer innovation potential of emergent knowledge and collaboration networks. The objective is to present the underlying communication dynamics of GPS in a form of computational model and delineate the impacts of various communication preferences of scientists on innovation potential of the collaboration network. Gained insight can ultimately help policy-makers to design GPS environments and promote innovation.     

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.     

Computational Modeling of Organizations Comes of Age

As they are maturing—i.e., as they are becoming validated, calibrated and refined—computational emulation models of organizations are evolving into: powerful new kinds of organizational design tools for predicting and mitigating organizational risks; and flexible new kinds of organizational theorem-provers for validating extant organization theory and developing new theory. Over the past 50 years, computational modeling and simulation have had enormous impacts on the rate of advancement of knowledge in fields like physics, chemistry and, more recently, biology; and their subsequent application has enabled whole new areas of engineering practice. In the same way, as our young discipline comes of age, computational organizational models are beginning to impact behavioral, organizational and economic science, and management consulting practice. This paper attempts to draw parallels between computational modeling in natural sciences and computational modeling of organizations as a contributor to both social science and management practice.To illustrate the lifecycle of a computational organizational model that is now relatively mature, this paper traces the evolution of the Virtual Design Team (VDT) computational modeling and simulation research project at Stanford University from its origins in 1988 to the present. It lays out the steps in the process of validating VDT as a computational emulation model of organizations to the point that VDT began to influence management practice and, subsequently, to advance organizational science. We discuss alternate research trajectories that can be taken by computational and mathematical modelers who prefer the typical natural science validation trajectory—i.e., who attempt to impact organizational science first and, perhaps subsequently, to impact management practice.The paper concludes with a discussion of the current state-of-the-art of computational modeling of organizations and some thoughts about where, and how rapidly, the field is headed.     

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.     

Modeling centralized organization of organizational change

Organizations change with the dynamics of the world. To enable organizations to change, certain structures and capabilities are needed. As all processes, a change process has an organization of its own. In this paper it is shown how within a formal organization modeling approach also organizational change processes can be modeled. A generic organization model (covering both organization structure and behavior) for organizational change is presented and formally evaluated for a case study. This model takes into account different phases in a change process considered in Organization Theory literature, such as unfreezing, movement and refreezing. Moreover, at the level of individuals, the internal beliefs and their changes are incorporated in the model. In addition, an internal mental model for (reflective) reasoning about expected role behavior is included in the organization model.     

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.     

An agent-based model of the development of friendship links within Facebook

This paper investigates how local preferences and social structural constraints might affect the development of the friendship network in Facebook. We do this by analyzing a dataset of an American university, Caltech, and by building an agent-based simulation for comparison. Several different, but plausible, processes of friendship network development are proposed in which the structural information of the growing network and the student preferences are taken into account. ‘Network formation based on personal preference and social structure’ matches the data best, and is thus the preferred hypothesis for the way that students add “friends” on Facebook.     

Diversity and Popularity in Organizations and Communities

This paper examines social groupings whose structure depends only on the distribution of ability to attract attention. When people&#x2018;s attention is a scarce resource, those individuals who are rated highest by a large number of other individuals will have to ration their attention, resulting in constraints on the social structure of the group. The incidence of popular individuals by that definition reflects the extent to which individuals agree on who their highest-rated colleague is. We propose three basic distributions or ways to generate the matrix of perceived ability so as to yield popularity profiles that can be parametrically adjusted to match observations. We demonstrate that each of these assumption sets leads to a slightly different correlation between the value of the assumption&#x2018;s parameter and the set of observable popularity patterns. Since popularity, in real life, often determines such things as power, centrality, over-utilization and perhaps reduced accessibility, having more realistic ways of representing it is important for modeling and understanding virtual organizations and communities.