Understanding the relative criticality of employees is important in managing turnover and security risks associated with human capital in organizations. Traditional social network analysis measures are based on static, survey-based assessments of centrality and other sociometric aspects of organizations, limiting their effectiveness in fully evaluating human capital criticality, particularly criticality that may be “hidden ” in the non-social dimensions of an organization. We introduce new task- and knowledge-based measures designed to overcome such limitations, and we apply them to a sixteen-person software development team to compare their efficacy to that of traditional social network measures of degree and betweenness centrality. Our results suggest that while each class of measures provides useful insight on criticality of organization actors, knowledge-based measures provide the most robust predictions of each actor’s contribution to organizational performance. Contact:

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__________________________________________________________________ The use of information technology to facilitate knowledge transfer is a major concern for organizations. This paper focuses on the use of databases and the role they play in transferring knowledge. In particular, two data types are defined – task data and referential data. Virtual experiments are run using two varying conditions, task complexity and group experience. The analysis looks at how the use of the data types differ in moderating knowledge transfer for task complexity and group experience. A theory of data type use is then developed.

Organizations are occasionally faced with technology-based and accident-triggered crises that may cause costly disasters if not handled properly. Questions arise: How should organizations, with their complex processes and human involvement, be designed if they are to perform well in such crises? Would organizations benefit from structural changes during crises? From a neo-information processing perspective that views organizations as composed of cognitively restricted, socially situated, and task-oriented actors, we argue that the causes and consequences of crises may be better understood through the systematic examination of both environmental and organizational factors. We address our research questions using a rather unique approach: a matched analysis of 80 real organizational cases and 80 computer-simulated organizations. The findings show that a crisis can present critical challenges to organizational performance both externally and internally, and that there is no design guarantee that a high-performing organization will continue to perform well during a crisis situation. In addition, when organizations restructure to adapt to crisis situations, they often face the serious challenges of having to understand not only the external environment, but also organizational design traps. Key words: organizational performance; organizational design; computational modeling; real-crisis cases Whether theories of organization can be applied to nonconventional events or crisis situations has largely been assumed but certainly not fully explored (Carley

__________________________________________________________________ The reality of life is embedded in social networks. Understanding how social networks affect disease propagation and how the consequences of disease change social networks is critical for modeling early manifestation of diseases on diverse human activities. Moreover, disease outbreaks do not happen in vacuum, they are constrained by physical, economical, technological, media, health, and governmental infrastructures. It is critical to be able to systematically reason about the nature of outbreaks, early manifestation of diseases on diverse human activities, the potential of media and inoculation campaigns, and the relative value of various early warning devices. There is simply not enough actual data on bioattacks. What is needed is a cost-effective, ethical system for reasoning about such events. Focusing on sample-based statistical methods, conventional epidemiology ignores the reality of social networks and their dynamics, thus is illsuited for this. BioWar-- a scalable city-wide multi-agent network model –

simulation, model validation, organization theory, network organization, organizational Network organizations offer learning, adaptive and resilient capabilities that are particularly useful in high velocity environments as these capabilities allow the organization to effectively respond to change. The dynamic, evolutionary nature of network organizations affords such advantageous capabilities. Although the advantages of network organizations are well-studied, the risks associated with them are not. Of interest is the study of critical personnel. Understanding criticality within an organization can help improve performance and protect against the risk of loss. But the study of critical personnel has traditionally used static structural representations that do not represent the dynamic nature of network organizations. This thesis advances the study of critical personnel risks in network organizations

In this paper, we investigate coordination within responder radio communications during the World Trade Center disaster. Using a network analytic approach, we identify agents who exhibit high levels of coordinative activity. Agents are further classified by whether they appear to occupy formal roles whose responsibilities include coordination of communication, and by whether the agents belong to organizations whose mission includes emergency response. We find that, regardless of organizational type, the great majority of coordinators are emergent (i.e., do not occupy formal coordinative roles). At the same time, where agents with formal coordinative roles are present, they are substantially more likely to become actual coordinators. Uniformity of e#ects across organizational types suggests that emergent coordination in crisis situations is not strongly contingent on responder training or organizational structure.

Identifying and retaining key personnel is a major concern for knowledgebased enterprises. This paper applies social network criticality measures that take into account the knowledge, task and communication networks in order to identify key personnel. We then go one step further and perform a turnover risk analysis on the key personnel by way of simulation. Results show that the key personnel identified could impose a turnover risk if they are not replaced with others that have equal or greater expertise.

Computational Analysis of Social and Organizational Systems. To my wife, Libby, and to my family, whom I love beyond words… Computational methods combined with traditional empirical techniques offer a powerful new approach to the study of human performance. 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. In this collection of three studies, I first review the state of modern computational modeling and find a steady increase in the incorporation of dynamic, adaptive, and realistic behaviors of agents in social network settings. However, my analysis suggests areas that can be addressed in the next generation of organizational simulation systems. I compare 28 models according to more than 200 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

A computer-based simulation of a multi-agent networked, virtual organization is experimented with to explore the impact of human-agents misrepresenting information to others when performing an organization-level decision task. It is a common-place phenomenon that humans make misrepresentations (or errors) in judgment (accidental or otherwise) when evaluating information and then passing their resulting conclusion on to others, which can affect the accuracy of organization-level decisions. This can have great relevance in organizations under stress or undergoing large-scale change, e.g., as in the case of a merger or acquisition, as humans can make even more misrepresentations. For this study, repeated simulations are executed with different levels of actor knowledge-misrepresentation to assess the impact on the dynamics of organization performance. Organization performance is quantified by the accuracy of a knowledge-based task, repeated over multiple time periods. Each task is a single binarydecision, which is derived by actor majority vote. Actors base their vote on the partial, factual knowledge that each holds, but their best-effort vote can be reversed according to the actor’s tendency to misrepresent the totality of the accurate information they truly hold. The underlying