A comprehensive method for comparing mental models of dynamic systems

Mental models are the basis on which managers make decisions even though external decision support systems may provide help. Research has demonstrated that more comprehensive and dynamic mental models seem to be at the foundation for improved policies and decisions. Eliciting and comparing such models can systematically explicate key variables and their main underlying structures. In addition, superior dynamic mental models can be identified. This paper reviews existing studies which measure and compare mental models. It shows that the methods used to compare such models lack to account for relevant aspects of dynamic systems, such as, time delays in causal links, feedback structures, and the polarities of feedback loops. Mental models without those properties are mostly static models. To overcome these limitations of the methods to compare mental models, we enhance the widely used distance ratio approach (Markóczy and Goldberg, 1995) so as to comprehend these dynamic characteristics and detect differences among mental models at three levels: the level of elements, the level of individual feedback loops, and the level of the complete model. Our contribution lies in a new method to compare explicated mental models, not to elicit such models. An application of the method shows that this previously non-existent information is essential for understanding differences between managers’ mental models of dynamic systems. Thereby, a further path is created to critically analyze and elaborate the models managers use in real world decision making. We discuss the benefits and limitations of our approach for research about mental models and decision making and conclude by identifying directions for further research for operational researchers.     

Evaluating environmentally conscious business practices

Corporate environmental management is becoming more strategically oriented. With increased emphasis on the natural environmental by organizational stakeholders, including governments, stockholders, customers, employees and communities, the need for explicit consideration and incorporation of environmental strategy within corporate strategy has never been more critical to the organization. With such programs as design for the environment, total quality environmental management, life cycle analysis, green supply chain management, and ISO 14000 standards gaining notoriety, the operational and strategic decisions for environmental managers and businesses is becoming more complex. This paper integrates these elements and their attributes into a strategic assessment and decision tool using the systems with feedback or analytical network process (ANP) technique first introduced by Saaty. The ANP technique, which has been sparingly investigated by researchers or applied by practitioners is useful for modeling dynamic strategies systemic influences on managerial decisions.     

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.     

Dynamic construction of statistical models in managerial DSS

The fields of operations research (OR) and artificial intelligence (AI) provide complementary methods that may be combined into managerial decision support systems (DSS). However, the management domain is substantially different from domains in which prior expert systems have been developed. Consequently, successful application of OR/AI techniques in managerial DSS requires careful analysis and additional development. Ongoing research concerning design and implementation of managerial DSS is discussed. A prototype system capable of constructing linear statistical models of direct and indirect relationships from a knowledge base of relationships is described and evaluated.     

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.     

An Organizational Model of Integrated Budgeting for Short-Run Operations and Long-Run Investments

In managerial finance, the short run usually refers to multiperiod models which involve operating plans and cash flow analysis. Long-run models are for capital investment or capital budgeting. Their essential link is the emphasis on cash flow and budgets. This relation is explored with an integrated model and its decompositions by budgets. The decomposition yields subunit models for the short run and long run which are related to earlier models, but yields insight into the organizational use of budgets for coordination and control. To obtain relative efficiency measures on two alternatives organizational budgeting mechanisms, a simulation was performed and analyzed.     

Resource-based strategies and problem structuring: using resource maps to manage resource systems

The resource-based view of strategy seeks to explain why some firms consistently outperform rivals in the same industry by acquiring a unique set of strategic assets (or resources). However, differences between dominant managerial mental models in management teams lead to disagreement at the moment of implementing distinct resource-building strategies. This managerial and cognitive view of strategic decision making and competition lends itself to investigation through problem structuring methods. We suggest that resource maps, as a problem structuring method, can be used to interpret managerial mental models for strategic decision making in terms of resource-building processes. Through resource maps, we represent the system of asset stocks believed to be most important for driving business performance. We illustrate the framework by comparing and contrasting maps of the system of resources (asset stocks) that best characterize two leading firms in the UK commercial radio broadcasting industry.     

Inferring final organizational outcomes from intermediate outcomes of exploration and exploitation: the complexity link

The difficulty in definitively linking outcomes of managerial action to organizational outcomes has been a festering issue in organizational research. The problem arises because it is not easy to separate the distinctive contributions of managers at intermediate stages, as well as the contribution of external factors beyond the control of managers. Specifically, certain managerial actions focusing on exploratory or exploitative innovation produce an intermediate output, organizational knowledge. From this base of organizational knowledge, further management actions craft the final output that eventually faces the market test. Drawing from complexity concepts, I argue that the probability of correctly fashioning the subset of key elements in the intermediate output may be a good measure of the probability of organizational success. I use March’s iconic computational simulation model to demonstrate the merits of this principle. I model the effect of complexity on managerial intentionality towards exploratory and exploitative innovation. I elicit important insights for research and practice by comparing organizational knowledge outcomes with the outcomes for probability of organizational success, in stable and moderately turbulent environment.     

A multi-structural framework for adaptive supply chain planning and operations control with structure dynamics considerations

A trend in up-to-date developments in supply chain management (SCM) is to make supply chains more agile, flexible, and responsive. In supply chains, different structures (functional, organizational, informational, financial, etc.) are (re)formed. These structures interrelate with each other and change in dynamics. The paper introduces a new conceptual framework for multi-structural planning and operations of adaptive supply chains with structure dynamics considerations. We elaborate a vision of adaptive supply chain management (A-SCM), a new dynamic model and tools for the planning and control of adaptive supply chains. SCM is addressed from perspectives of execution dynamics under uncertainty. Supply chains are modelled in terms of dynamic multi-structural macro-states, based on simultaneous consideration of the management as a function of both states and structures. The research approach is theoretically based on the combined application of control theory, operations research, and agent-based modelling. The findings suggest constructive ways to implement multi-structural supply chain management and to transit from a “one-way” partial optimization to the feedback-based, closed-loop adaptive supply chain optimization and execution management for value chain adaptability, stability and crisis-resistance. The proposed methodology enhances managerial insight into advanced supply chain management.     

An analysis of the impact of chaotic dynamics on management information flow models

Defined very broadly, Chaos Theory is the study of the behavior of dynamic, nonlinear, feedback equations which, with certain parameters, produce random-appearing output, although all parts of the equation system are deterministic. In this research we use the insights provided by the study of Chaos Theory to investigate how chaos can impact management dynamics and thus influence managerial decision-making.It is common to use dynamic mathematical models as aids to management. If model formulation is such that the model produces chaotic output under certain circumstances, decisions based on the use of that model are seriously compromised. Further, when several models are used concurrently, the interactions between them may cause output to be chaotic even if no individual model exhibits such behavior. We provide an explanation of the reasons why this may happen, and illustrate the consequences through an example.     

Modelling and evaluation of time aspects in business processes

There is a need for modelling and performance evaluation techniques and tools for a fast and reliable design of workflow systems. This paper introduces a modelling methodology based on coloured stochastic Petri nets. It allows the integration of control flow, organizational, information related and timing aspects in one modelling framework. The processing delays include stochastic distributions in addition to deterministic times. Several workflows and the effects of constrained shared resources needed for different tasks can easily be described and analysed together. Control flow and organizational aspects are modelled separately in resource and workflow models. These models are automatically compiled into one model, which can then be used for qualitative analysis or performance evaluation. The proposed modelling and evaluation method is supported by the software tool TimeNET. An application example shows its use.     

Does knowledge management matter? Assessing the performance of electricity distribution districts based on slacks-based data envelopment analysis

This study uses slacks-based data envelopment analysis (DEA) to examine the performance of electricity distribution districts (EDDs) in Taiwan in 2004. It also explores the relationship between a knowledge management system (KMS) and variations in the efficiency of EDDs in the Taiwan Power Company from 2000 to 2004. The findings show that the EDDs performed well in terms of managerial efficiency in 2004, and 75% of the districts exhibited an increasing performance variation when using cross-period analysis for that period. We show that there is a positive relationship between a KMS and variations in organizational efficiency. In addition, we present a decision-making matrix and conceptual map to help EDD managers improve their managerial efficiency and skills. This study contributes to the field of knowledge management research by applying the DEA model to link the performance between KMS and an organization. Several significant findings emerge from the study.     

超市模型的实时动态控制及其数值分析

超市模型是针对大型并行排队网络所进行的 实时动态控制的随机负载平衡策略, 它在计算机网络、云计算、制造系统、交通网络等领域有着重要的实际应用价值. 本文考虑了超市模型中的若干重要问题: 实时动态控制模式; 效率比较; 平均场黑洞; 马氏变动环境; 稳定性; 固定点; 系统性能评价等等. 同时, 本文也通过数值算例研究了上述重要问题, 包括对顾客加入最短队列的超市模型与服务台服务最长队列的超市模型进行了性能比较, 给出了他们效率的优劣分析; 在超市模型中对控制到达过程机制进行了三种情况的对比; 对马氏变动环境下的超市模型进行了性能评价.     

The Systems Approach in an Information Society: A Reconsideration

An information society is one in which informational (or interactional) as opposed to material entities represent the dominant form of social and economic transaction. This paper argues that in such an information-rich environment, the conventional view of the systems approach may no longer be relevant, and may in fact be a serious barrier to the development and implementation of creative management technologies. We present an alternative perspective on the systems approach that emphasizes the primacy of informational concepts as opposed to the primacy of matter/energy concepts. This alternative focuses on interactions, on the logic of change, on circular causality, and on multiple concepts of time. There are important implications for organizational design, managerial control and information systems.     

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     

Computational and mathematical organization theory: Perspective and directions

Computational and mathematical organization theory is an interdisciplinary scientific area whose research members focus on developing and testing organizational theory using formal models. The community shares a theoretical view of organizations as collections of processes and intelligent adaptive agents that are task oriented, socially situated, technologically bound, and continuously changing. Behavior within the organization is seen to affect and be affected by the organization's, position in the external environment. The community also shares a methodological orientation toward the use of formal models for developing and testing theory. These models are both computational (e.g., simulation, emulation, expert systems, computer-assisted numerical analysis) and mathematical (e.g., formal logic, matrix algebra, network analysis, discrete and continuous equations). Much of the research in this area falls into four areas: organizational design, organizational learning, organizations and information technology, and organizational evolution and change. Historically, much of the work in this area has been focused on the issue how should organizations be designed. The work in this subarea is cumulative and tied to other subfields within organization theory more generally. The second most developed area is organizational learning. This research, however, is more tied to the work in psychology, cognitive science, and artificial intelligence than to general organization theory. Currently there is increased activity in the subareas of organizations and information technology and organizational evolution and change. Advances in these areas may be made possible by combining network analysis techniques with an information processing approach to organizations. Formal approaches are particularly valuable to all of these areas given the complex adaptive nature of the organizational agents and the complex dynamic nature of the environment faced by these agents and the organizations.This paper was previously presented at the 1995 Informs meeting in Los Angeles, CA.     

On the validity and utility of queueing models of human service systems

Based on observations made during an extensive study of police patrol operations in New York City, we examine the issues of the validity and utility of queueing models of service systems in which adaptive behavior by the (human) customers or servers is likely. We find that in addition to depending on the technical accuracy of its assumptions, the accuracy of such a model will also depend upon the level of managerial control of the system and adequacy of resources. We recommend that queueing models of human service systems be used in a normative fashion and incorporated in the management feedback loop.     

Managerial Decentralization and Decomposition in Mathematical Programming

There are many computational models available now which purport to add to our ability to plan and control organizations in a decentralized fashion. Some of these are custom built for the purpose while others incorporate mathematical programming decomposition procedures. Although comparisons have been made concerning their computational requirements, little analysis seems to have been done to explore the organizational implications of the various methods. A start is made on this task here and some conclusions drawn in terms of the managerial style of-and the motives for-decentralization.