Modeling 21st century project teams: docking workflow and knowledge network computational models

This paper reports on an attempt to integrate and extend two established computational organizational models—SimVision® and Blanche—to examine the co-evolution of workflow and knowledge networks in 21 century project teams. Traditionally, workflow in project teams has been modeled as sets of sequential and/or parallel activities each assigned to a responsible participant, organized in a fixed structure. In the spirit of Jay Galbraith’s (1973) information processing view of organizations, exceptions—situations in which participants lack the required knowledge to complete a task—are referred up the hierarchy for resolution. However, recent developments in digital technologies have created the possibility to design project teams that are more flexible, self-organizing structures, in which exceptions can be resolved much more flexibly through knowledge networks that extend beyond the project or even the company boundaries. In addition to seeking resolution to exceptions up the hierarchy, members of project teams may be motivated to retrieve the necessary expertise from other knowledgeable members in the project team. Further, they may also retrieve information from non-human agents, such as knowledge repositories or databases, available to the project team. Theories, such as Transactive Memory, Public Goods, Social Exchange and Proximity may guide their choice of retrieving information from a specific project team member or database. This paper reports on a “docked” computational model that can be used to generate and test hypotheses about the co-evolution of workflow and knowledge networks of these 21 century project teams in terms of their knowledge distribution and performance. The two computational models being docked are SimVision (Jin & Levitt, 1999) which has sophisticated processes to model organizations executing project-oriented workflows, and Blanche (Hyatt, Contractor, & Jones, 1997), a multiagent computational network environment, which models multitheoretical mechanisms for the retrieval and allocation of information in knowledge networks involving human and non-human agents. This paper was supported in part by a grant from the U.S. National Science Foundation for the project “Co-Evolution of Knowledge Networks and 21 Century Organizational Forms (IIS9980109). Modeling 21 Century Project Teams: Docking Workflow and Knowledge Network Computational Models Edward T. Palazzolo, Archis Ghate, Roberto Dandi, Ashwin Mahalingam, Noshir Contractor and Raymond Levitt This paper explores how the evolution from hierarchical to network forms of organizing influences the modeling of project teams. We begin by describing contemporary theoretical models of workflow and knowledge networks in project teams. We overview two computational models -SimVision® and Blanche -that have been developed to characterize project teams from a workflow and knowledge networks perspective. Next we describe the rationale and process of “docking” these two computational models. We conclude with some thoughts on how docking these models can significantly advance our understanding of the performance of 21st century project teams. Workflow Models The intellectual premise of SimVision®, a workflow model grounded in "micro" contingency theory, is that organization behavior emerges from the decisions and actions of individual actors as they process information associated with activities, and as they create and respond to requests for information. SimVision® applies and extends the information processing framework (Galbraith, 1973) and the computational approach of Cyert and March's pioneering "Behavioral Theory of the Firm." Micro-behaviors currently implemented in SimVision® include attention allocation by actors to direct work and communication items in their in-trays; exception generation; communication tool selection; communication routing; and decision making about rework in the face of exceptions. Actors in SimVision® process work items arising from tasks that are assigned to them, stochastically encounter exceptions, and attempt to resolve the exceptions by sending them up the hierarchy to be resolved by managers to whom they report. SimVision® models actors—where each actor is an individual or an abstracted subteam—as possessing: a Skill Set, one or more skills, each rated at low, medium or high (L,M.H); Application Experience (L,M,H); and a Capacity measured in full time equivalents (FTE). Tasks are modeled in a sequential precedence network and are characterized by: a Skill Requirement (a single skill); a Work Volume or level of effort ( FTE-Days); and zero or more Reciprocal Dependency and Rework Dependency links to other tasks. The project organization is characterized by a set of decision-making policy attributes, including: Centralization, Formalization, Matrix Strength and Team Experience. In additional to modeling the direct work from tasks like Critical Path Method (CPM) models, SimVision® models and simulates the significant information-processing load imposed on the organization by the need to coordinate reciprocal dependencies, and the rework volume for each actor generated by exceptions that require rework to correct them. Actors in a SimVision® simulation—like those in a real project—can easily get backlogged by the combination of direct work, coordination work and rework that they must handle. When they become excessively backlogged (i.e., when their in -baskets contain more than a few FTE-days of work volume), they tend to focus on catching up on their direct work and de-emphasize coordination and error correction. This increases the likelihood of exceptions occurring downstream. In cases involving severe backlogs for critical managers, the workflow can become very turbulent, and the project may not complete (Levitt, et al. 2002). By modeling coordination and rework, and by simulating both the direct and 2 order effects of backlogs on project teams, SimVision® has been able to generate extremely accurate predictions of failures in real project teams (Kunz et al, 1998). As such, SimVision® is a general framework for examining the impact of specific organizational forms on organizational performance and workflow and can generate detailed predictions for teams faced with routine tasks, stable organizational structures and agents that do not learn. A key limitation of SimVision® is that it adopts the 20 century view of Galbraith and others that “the hierarchy is the knowledge network.” This “boss knows better” view of exception handling is clearly outdated for many kinds of 21 century work and needs to be extended. Formal and informal interactions are differentiated, as are interactions via different communication media. SimVision® uses an abstract (skill type) x (skill level) characterization of knowledge. It does not differentiate cognition into different types of internal knowledge. The authors are collaborating on an NSF KDI Grant (Contractor, et al 1998) to understand how workflow approaches like SimVision® can be integrated with tools that model information and knowledge exchange via flexible and dynamic knowledge networks, as predicted by various social scientific theories. To answer this question, the authors decided to attempt to dock SimVision®’s workflow model with a model of exception handling via flexible knowledge networks implemented in Blanche. The docking process is described following a brief overview of knowledge network models. Knowledge Network Models The nodes in a knowledge network include individuals as well as aggregates of individuals, such as groups, departments, organizations, and agencies. Increasingly, the nodes also include non-human agents such as knowledge repositories, web sites, content and referral databases, avatars, and “webbots” (Carley, 2002). The social structures in these networks refers to “who knows who” in the network, while the cognitive social structures refers to “who knows who knows who” (Krackhardt, 1987). The knowledge network linkages describe “who knows what,” while the cognitive knowledge network linkages refer to “who knows who knows what.” The communication networks linkages include the retrieval and the allocation of information from (or to) other human and non-human agents. Our goal is to extend the hierarchically based exception handling mechanisms specified in traditional workflow models. As discussed earlier, in traditional workflow models, a person in the chain of command must resolve an exception-handling request. However, in 21 century project teams, rather than seek resolution through the chain of command, members seek information from peers or non-human agents such as project databases. The decision about which peer (or non-human agent) they approach for a specific exception-handling request is at the discretion of the individual. Based on a theoretical and empirical review of the research on organizational networks, Monge and Contractor (in press) propose a multi-theoretical multi-level (MTML) model to explain why an individual may forge an information retrieval tie with another human or non-human agent. Preliminary results from our empirical research indicate that there are multiple social motivations that influence members’ tendencies to retrieve from other team members or from collective knowledge repositories such as databases (Contractor, Brandon, Dandi, Huang, Palazzolo, Ruta, Singh, and Su, 2002). The three mechanisms that are particularly influential in explaining the creation of network ties for retrieval of information from other team members include: (i) Cognitive mechanisms of transactive memory theory (Wegner, 1995): Members seek the expertise of others who they think are knowledgeable although they may not be accurate in their assessment of others’ knowledge. (ii) Social exchange mechanisms (cf. Cook, 1982): Members retrieve information about a topic from others who, in turn, seek information from them on other areas of expertise. (iii) Proxi