Human and organizational risk is characterized by the multi-level complexity and dynamics of the socio-technical system. As such, current theory defines human and organizational risks as emerging from lower-level interactions among the individual elements of a system. This research uses dynamic network analysis, a holistic approach using social networks and multi-agent simulation, to model the complex dynamics of human and organizational risk. This approach represents the socio-technical system as networks of dynamic interrelationships among people, knowledge, tasks, resources, technologies, organizations, and the surrounding environment. Dynamics in the simulation models are based on well-known social and cognitive processes that influence agent interactions. Thus emergent behaviors in the models arise from interactions among the individual elements of the socio-technical system. This methodological approach matches the multi-level representation and analysis required by current theories of organizational risk. Comprehensive analysis and innovative solutions are benefits of this work.
Several analyses have shown how organizational accidents are emergent events arising from interactions among the individual elements of a socio-technical system (Perrow 1999; Reason 1997; Snook 2000; Vaughan 1996; Weick and Sutcliffe 2001). These accidents and interactions all have a human and organizational component. For instance, the Columbia Investigation Board (CIB 2003) cited many organizational problems within NASA as contributing factors to the accident. Among the problems cited were barriers to communication, including information technologies, structural integration and databases, ineffective leadership and practical drift. Many of these organizational problems were previously cited in the accident report of the Challenger accident as well (PCSSCA 1986). Obviously, human and organizational problems pose risks which are important to address.
The difficulty in understanding and addressing human and organizational risks lies in the fact that they are complex multi-level problems of the socio-technical system. As Snook (2000) notes, “… the enormous complexity of the situation quickly outstrips our cognitive capabilities as bounded information processors” (p. 204). Socio-technical systems are complex systems that display non-linear, emergent dynamics. Humans as boundedly rational (Simon 1955) have a difficult, if not impossible, time comprehending all the potential interactions of such a system. Due to the complex nature of the socio-technical system, new techniques for analyzing the problem need to be developed
The objective of this research is to develop models of human and organizational risk using a relatively new methodology, dynamic network analysis. Dynamic network analysis is a holistic approach that models the multi-level dynamics of the socio-technical system. Using the MetaMatrix and multi-agent simulation, this approach models the socio-technical system as networks of dynamic interrelationships among people, knowledge, tasks, resources, technologies, organizations, and the surrounding environment. As such, organization-level attributes as well as individual-level attributes are simultaneously represented. Organization-level attributes include such things as culture, structure and shared situational awareness. Individual-level attributes include such things as organizational role, experience and stress response.
The dynamics of the socio-technical system are modeled using multi-agent simulation models. These models represent dynamics based on well-known social and cognitive processes that influence agent interactions. Agents, both human and technological, go through a process of action, learning and network alteration to produce emergent behavior. Thus emergent behaviors in the models arise from interactions among the individual elements of the socio-technical system; a methodology that matches the multi-level representation and analysis required by current theories of organizational risk.
These human and organizational risk models, consisting of both structure and dynamics, can capture the complexity of the socio-technical system. The multi-level capabilities of dynamic network analysis provide a comprehensive methodology for analyzing organizational risk and for developing innovative solutions to organizational problems. This approach has been used to model human and organizational risk for NASA, the U.S. Army and the U.S. Navy (see publications for more detail on how this approach has been used).
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