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.     

Publicity vs. impact in nonprofit disclosures and donor preferences: a sequential game with one nonprofit organization and N donors

Charitable giving is one of the essential tasks of a properly functioning civil society. This task is greatly complicated by the lack of organizational transparency and by the information asymmetries that often exist between organizations and donors in the market for charitable donations. The disclosure of financial, performance, donor-relations, and fundraising-related data is thus an important tool for nonprofit organizations attempting to attract greater donations while boosting accountability and public trust. There are, however, varying payoffs associated with such disclosure depending on the nature of donor preferences and the relative openness and effectiveness of competing organizations. To help understand the interplay between nonprofit organizational disclosures and individual donations, we present a novel game-theoretic model of disclosure–donation interactions that incorporates the predominant forms of both donor preferences and “value-relevant” information.     

Adaptation as a Morphing Process: A Methodology for the Design and Evaluation of Adaptive Organizational Structures

Changes in objectives, in resources, or in the environment may necessitate the adaptation of an organization from one form to another. However, in many cases, the organizations need to continue functioning while adaptation takes place, i.e., it is not possible to stop the organizational activity in order to reorganize, and then start again. In this case, adaptation can be expressed as a morphing process in which the organization transitions from one form with its attendant task allocation to a different one through a series of incremental steps that preserve overall functionality and performance. Coordination between organization members during adaptation is critical. A computational model for this type of organizational adaptation at the operational level is presented. The model is implemented using the Colored Petri Net formulation of discrete event dynamical systems. A design methodology that utilizes this model is outlined and a simple example is used to illustrate the approach.     

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.     

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.     

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.     

The relationship between exploration and exploitation strategies,manufacturing flexibility and organizational learning: An empirical comparison between Non-ISO and ISO certified firms

The markets in which organizations currently operate require them to use behaviour based on both exploitation strategy and exploration strategy, each of which contributes fundamental benefits for the firm’s success. Exploitation strategy attempts to obtain the maximum advantage from existing abilities, whereas exploration strategy searches for new ways for the organization to adapt. Further, since manufacturing markets are characterized by short product life, they require a high level of manufacturing flexibility, which can play a crucial role in the development of the exploitation and exploration strategies. This study analyses the relationship of manufacturing flexibility to exploitation and exploration strategies, taking into account an issue of maximum international scope: the implementation of ISO standards. In many international markets, ISO implementation is crucial in enabling organizations to be competitive. However, no consensus has been reached concerning the real benefits that these systems provide to the organizations that implement them. The main goal of our study is to analyse whether there are significant differences in the relationship of manufacturing flexibility to exploitation and exploration strategies in ISO 9001:2000 certified organizations and non-certified organizations. Our results show that most of the relationships analysed are only significant in ISO certified organizations. As an added value, the study analyses the relationship of exploitation and exploration strategies to organizational learning orientation and finds this relationship to be significant in all cases.     

Structural reconfiguration and informal coordination in administrative organizations

This paper considers the problem of determining the optimal design of public organizations in terms of maximizing their reliability against institutional failures. To capture both the individual and the system-level aspects of organizational decisionmaking, first we present an analytical model that characterizes the optimal decision behavior of a single decision maker (unit, agent, in general: DM) in the context of a binary decision task. In this sense, reliability of a DM against the two possible error types: implementation of the wrong policy (error of comission, Type I error) and failure to act when it is necessary (error of omission, Type II error) are interpreted as the result of a particular decision strategy. Individual expertise is represented in the form of a Relative Operating Characteristic (ROC) curve that, in turn, depicts the necessary trade-off between the two errors when selecting an appropriate decision strategy. Component decisions are then combined along the lines of organizational structure which is described using a graph formalism. We show that the task of finding the best organizational design involves a joint optimization over structure and strategy, and implement the normative model in the context of a detailed example. Our numerical results suggest that when DMs coordinate their decision rules, there is little difference in the performance of various organizational structures.This work was supported by the Office of Naval Research under ONR contract #N0014-93-I-0793.     

Adapting to the Changing Environment: A Theoretical Comparison of Decision Making Proficiency of Lean and Mass Organization Systems

In this paper weexamine the adaptability of the Japanese style leanorganization system and the traditional American style mass organizationsystem under changing environments. From an organizational designperspective, key structural aspects of the two organizations are modeled ina problem solving context using computational methods. Organizational-levelperformance in terms of decision making accuracy and severity of errors ismeasured as an indicator of organizational adaptability under conditionswhere the task environment shifts between predictable to unpredictable orvise versa. Our study shows that both organizations have their respectiveadvantages under different task environments and that they adapt toenvironmental shifts in different forms. Specifically, when the timepressure is high the lean organization system's performance isvirtually identical to the mass organization system, even though the leanorganization systemÆs members are more proactive. When the timepressure is low, the mass organization system shows a much fasteradaptability when the environment shifts to a predictable one but it is alsomore vulnerable when the environment shifts to an unpredictable one. Incontrast, the lean organization systemÆs response to the changingenvironment is characterized by its slower adaptability. When theenvironment shifts to an unpredictable one, the lean organization systemshows a gradual improvement till reaching a high level. When the environmentshifts to a predictable one, however, the lean organization system shows agradual decrease of performance. Our study further shows that the leanorganization system, with its strong team decision making emphasis, can bemore successful in avoiding severe errors when compared with the massorganization system, even under a predictable task environment.     

Impact of quality on competitive advantage and organizational performance

Effective quality improvement has become a potentially valuable way of securing competitive advantage and improving organizational performance. This research conceptualizes and develops a framework that links quality, competitive advantage, and organizational performance. Data for the study were collected from 74 organizations and the relationships proposed in the framework were tested using structural equation modelling. The results indicate that quality improvement can lead to enhanced competitive advantage and improved organizational performance. The contribution of the paper is that it provides empirical support for direct and indirect effects of quality on organizational performance and competitive advantage in Tunisia.     

Networks, Fields and Organizations: Micro-Dynamics, Scale and Cohesive Embeddings

Social action is situated in fields that are simultaneously composed of interpersonal ties and relations among organizations, which are both usefully characterized as social networks. We introduce a novel approach to distinguishing different network macro-structures in terms of cohesive subsets and their overlaps. We develop a vocabulary that relates different forms of network cohesion to field properties as opposed to organizational constraints on ties and structures. We illustrate differences in probabilistic attachment processes in network evolution that link on the one hand to organizational constraints versus field properties and to cohesive network topologies on the other. This allows us to identify a set of important new micro-macro linkages between local behavior in networks and global network properties. The analytic strategy thus puts in place a methodology for Predictive Social Cohesion theory to be developed and tested in the context of informal and formal organizations and organizational fields. We also show how organizations and fields combine at different scales of cohesive depth and cohesive breadth. Operational measures and results are illustrated for three organizational examples, and analysis of these cases suggests that different structures of cohesive subsets and overlaps may be predictive in organizational contexts and similarly for the larger fields in which they are embedded. Useful predictions may also be based on feedback from level of cohesion in the larger field back to organizations, conditioned on the level of multiconnectivity to the field.     

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.     

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.     

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.     

Convention evolution in organizations and markets

Conventions are essential for the coordination of multi-agent systems. However, in many systems conventions can not be legislated in advance and need to emerge during the system's activity. As designers of such systems we may wish to ensure that conventions will evolve rapidly. Given a classical model for convention evolution where agents tend to mimic agents they interact with, the designer can control the organizational structure of the system in order to speedup the evolution of conventions. This paper introduces a study of convention evolution in the context of basic organizational structures. Our study sheds light on a basic aspect of organizational design which has not been discussed in the literature, and which is crucial for efficient design of non-trivial multi-agent systems.     

A Bivariate Model for Markov Manpower Planning Systems

Markov models are being extensively used for analysis of manpower planning systems. Most of these models concentrate either on estimating the gradewise distribution of future manpower structure, given the existing structure and promotion policies, or on deriving policies towards promotion, given the required future structure. However, in many large organizations, agreements between employee unions and management result in the framing of policies towards promotion based either on seniority (length of service in the grade) or on performance (as in the case of ‘high fliers’). In this paper these two criteria are considered in a bivariate distribution framework. The transition probabilities for promotion obtained from the Markov model are further translated into required seniority and performance rating. The procedure is illustrated through an example.     

Density Delay and Organizational Survival: Computational Models and Empirical Comparisons

Research on the ecological dynamics oforganizational populations has demonstrated that competitiveconditions at the time of founding have enduring effects onorganizational survival. According to ecological theories,organizational life chances are systematically affected by density (the number of organizations in a population) at thetime of founding because the lower resource endowments thatcharacterize organizations appearing in periods of highpopulation density tend to become self-reinforcing, and—over time—amplify differences in mortality rates oforganizations founded under different conditions. However,credible arguments have been offered that could justify both positive and negative effects of the delayed effectsof population density on organizational mortality rates, andreceived empirical research in part reflects this ambiguity.To develop new insight into this issue and to explore theboundaries of received empirical results, in this study wepresent a computational model of organizational evolutionaccording to which the global dynamics of organizationalpopulations emerge from the iteration of simple rules oflocal interaction among individual organizations. We use the synthetic data produced by simulation to estimate eventhistory models of organizational mortality, and compare theparameter estimates with those reported in the most recentempirical studies of actual organizational populations. Theconclusions supported by the model qualify and extendreceived empirical results, and suggest that delayed effectsof density are highly sensitive the details of local structure of connections among members of organizationalpopulations.     

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.     

A Personnel Selection Problem in Healthcare System Using Fuzzy-TOPSIS Approach

The methods of multiple criteria decision-making (MCDM) are increasingly becoming the most desired tools for making daily decisions in various fields of human endeavors. Staff employment in any sector requires a thorough evaluation of the applicant before selection to ensure effective and efficient service delivery. Besides, healthcare is one of the most complicated organizations dealing with human lives. This paper has developed a staff selection model considering a fuzzy environment by using the technique for order preference similar to the ideal solution (TOPSIS) method. For the delivery and promotion of quality healthcare systems, medical staff selection is crucial to the system. Therefore, the study evaluates medical staff by using the expert''s linguistic judgement under the criteria of skill, experience and ability to respond to a problem. The expert''s vagueness in judgments has been represented by using fuzzy triangular numbers. The study determines the closeness coefficient, the measures of separation and the ideal solutions of the TOPSIS method. The most appropriate medical staff are ranked and selected based on the available criteria. The Fuzzy-TOPSIS method is simple and can help other organizations achieve proper ranking, evaluation and selection of qualified candidates, as it takes imprecise information into account.