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

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

Intra-organizational Networks and Performance: A Review

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

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.     

Computationally modeling organizational learning and adaptability as resource allocation: An artificial adaptive systems approach

A framework for and a computational model of organizational behavior based on an artificial adaptive system (AAS) is presented. An AAS, a modeling approach based on genetic algorithms, enables the modeling of organizational learning and adaptability. This learning can be represented as decisions to allocate resources to the higher performing organizational agents (i.e., individuals, groups, departments, or processes, depending on the level of analysis) critical to the organization's survival in different environments. Adaptability results from the learning function enabling the organizations to change as the environment changes. An AAS models organizational behavior from a micro-unit perspective, where organizational behavior is a function of the aggregate actions and interactions of each of the individual agents of which the organization is composed. An AAS enables organizational decision making in a dynamic environment to be modeled as a satisficing process and not as a maximization process. To demonstrate the feasibility and usefulness of such an approach, a financial trading adaptive system (FTAS) organization is computationally modeled based on the AAS framework. An FTAS is an example of how the learning mechanism in an AAS can be used to allocate resources to critical individuals, processes, functions, or departments within an organization.     

SDML: A Multi-Agent Language for Organizational Modelling

A programming language which is optimized for modelling multi-agent interaction within articulated social structures such as organizations is described with several examples of its functionality. The language is SDML, a strictly declarative modelling language which has object-oriented features and corresponds to a fragment of strongly grounded autoepistemic logic. The virtues of SDML include the ease of building complex models and the facility for representing agents flexibly as models of cognition as well as modularity and code reusability. Two representations of cognitive agents within organizational structures are reported and a Soar-to-SDML compiler is described. One of the agent representations is a declarative implementation of a Soar agent taken from the Radar-Soar model of Ye and Carley (1995). The Ye-Carley results are replicated but the declarative SDML implementation is shown to be much less computationally expensive than the more procedural Soar implementation. As a result, it appears that SDML supports more elaborate representations of agent cognition together with more detailed articulation of organizational structure than we have seen in computational organization theory. Moreover, by representing Soar-cognitive agents declaratively within SDML, that implementation of the Ye-Carley specification is necessarily consistent and sound with respect to the formal logic to which SDML corresponds.     

Towards an Organizational Model of Attitude Change

Building on the growing literature that views organizations as complex adaptive systems, this paper proposes a general model to analyze the relationship between organizational context and attitudes. In particular, we focus on how the system of formal and informal communication channels that characterize an organization and the timing of information flows affect the dynamic process of attitude change. We also use a stylized version of the model to illustrate how the general framework is able to generate insights that are relevant to particular 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.     

DOES COMPETENCY-BASED TRUST BENEFIT ORGANIZATIONAL PERFORMANCE—A MESO EXPLORATION USING COMPUTER MODELING

From a competency-based perspective of trust and an open system's view of organizations, this study explores the micro-macro linkage between interpersonal trust and organizational performance in work organizations where internal and external contexts can matter. With the help of a formal computer model for meso theorizing, this study shows that competency-based trust generally does not benefit organizational performance in a distributed decision-making setting, except under incorrect information conditions or when no formal procedure is available. The study further demonstrates that external environments, organizational structures, and internal operating conditions can all moderate such trust-performance relationships. Findings from this study suggest the need for new thinking relating to trust in organizations and the possibility to integrate psychological, economic, and sociological perspectives on trust.     

Setting leadership goals and getting those goals accomplished: insights from a mathematical model

The value and importance of leadership is evident by its prevalence throughout human societies and organizations. In this work, mathematical models are developed to study two key attributes of leadership: setting goals and getting others in the organization to achieve those goals. The models contain controllable parameters whose values reflect the size of the organization, the actions taken by the individuals in the absence of a leader, the goal-setting skill of the leader (that is, the ability of the leader to identify goals that will result in good performance), and the buy-in skill of the leader (that is, the ability of the leader to get others to achieve those goals). The models provide insights as to when goal-setting skill is more important than buy-in skill and when buy-in skill is more important than goal-setting skill. Mathematical analysis is used to derive conditions under which buy-in skill both enhances and detracts from organizational performance.     

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

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

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.     

Designing tree-structured organizations for computational agents

We describe a framework for defining the space of organization designs for computational agents, use our framework for analyzing the expected performance of a class of organizations, and describe how our analyses can be applied to predict performance for a distributed information gathering task. Our analysis specifically addresses the impact of the span of control (branching factor) in tree-structured hierarchical organizations on the response time of such organizations. We show quantitatively how the overall task size and granularity influence the design of the span of control for the organization, and that within the class of organizations considered the apropriate span of control is confined to a relatively narrow range. The performance predicted by our overall model correlates with the actual performance of a distributed organization for computer network monitoring. Consequently, we argue that our framework can support aspects of organizational self-design for computational agents, and might supply insights into the design of human organizations as well.     

Searching for Appropriate Theory and Practice in Multi-Organizational Fields

To some kinds of social scientists and change agents, there is nothing new about the idea of working in multi-organizational fields. To others, however, exposure to such a field can be an unfamiliar, daunting and indeed often bewildering experience. This paper will argue that, despite some progress in recent years, there remains a paucity of well-grounded and well-articulated theory to guide the endeavours of researchers and change agents working between rather than within organizations. It will identify and review five contexts for learning in which relevant experiences have recently been accumulating: these are the contexts of exploratory social research, of implementation research, of community development, of organizational development and of operational research. It is suggested that the development of an appropriate theory calls for a drawing together of experiences and insights from those now working within these various contexts- and especially those who have opportunities to move between one context and another.     

Multiscale decision-making: Bridging organizational scales in systems with distributed decision-makers

Decision-making in organizations is complex due to interdependencies among decision-makers (agents) within and across organizational hierarchies. We propose a multiscale decision-making model that captures and analyzes multiscale agent interactions in large, distributed decision-making systems. In general, multiscale systems exhibit phenomena that are coupled through various temporal, spatial and organizational scales. Our model focuses on the organizational scale and provides analytic, closed-form solutions which enable agents across all organizational scales to select a best course of action. By setting an optimal intensity level for agent interactions, an organizational designer can align the choices of self-interested agents with the overall goals of the organization. Moreover, our results demonstrate when local and aggregate information exchange is sufficient for system-wide optimal decision-making. We motivate the model and illustrate its capabilities using a manufacturing enterprise example.     

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     

The development and interaction of terrorist and fanatic groups

Through the mathematical study of two models we quantify some of the theories of co-development and co-existence of focused groups in the social sciences. This work attempts to develop the mathematical framework behind the social sciences of community formation. By using well developed theories and concepts from ecology and epidemiology we hope to extend the theoretical framework of organizing and self-organizing social groups and communities, including terrorist groups. The main goal of our work is to gain insight into the role of recruitment and retention in the formation and survival of social organizations. Understanding the underlining mechanisms of the spread of ideologies under competition is a fundamental component of this work. Here contacts between core and non-core individuals extend beyond its physical meaning to include indirect interaction and spread of ideas through phone conversations, emails, media sources and other similar mean. This work focuses on the dynamics of formation of interest groups, either ideological, economical or ecological and thus we explore the questions such as, how do interest groups initiate and co-develop by interacting within a common environment and how do they sustain themselves? Our results show that building and maintaining the core group is essential for the existence and survival of an extreme ideology. Our research also indicates that in the absence of competitive ability (i.e., ability to take from the other core group or share prospective members) the social organization or group that is more committed to its group ideology and manages to strike the right balance between investment in recruitment and retention will prevail. Thus under no cross interaction between two social groups a single trade-off (of these efforts) can support only a single organization. The more efforts that an organization implements to recruit and retain its members the more effective it will be in transmitting the ideology to other vulnerable individuals and thus converting them to believers.     

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.     

Some Simple Algorithms for Structural Comparison

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

Dialogue strategy for horizontal communication in MAS organization

A multi agent organization model defines the structure, roles, interaction ways and coordination styles of a multi agent system. Multi agent organizations may constrain the communication between included members. Organizations’ communication is the process of sending or receiving all the messages through a group of agents in order to achieve common goals. In a dialogue, agents follow some rules that define the permissive speech acts called dialogue protocol. Aiming for common goals, the dialogue strategy is the policy of agents to choose a particular speech act among the allowed ones by the protocol. In this paper a formal model for dialogue strategy for a group of agents in an organization is proposed in order to choose the most preferable speech acts. The argumentation theory is applied to the proposed method to define the values of plausible speech acts and to rank them. The algorithm finds the best option to utter and also it decreases the volume of exchanging messages. The proposed dialogue strategy is illustrated via a deliberation dialogue example in a group of agents.