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Complex engineered, organizational and natural systems: Issues Underlying the Complexity of Systems and Fundamental Research Needed To Address These Issues Contract: National Science Foundation; grant number 0538768
Abstract
This paper describes an effort to determine the rationale and content for a research agenda in complex systems. This effort included a workshop conducted with 50 thought leaders in complex engineered, organizational, and natural systems. The results of this workshop were subsequently presented to seven groups in academia and industry across the United States. In this way, additional comments, suggestions, and insights were gained from roughly 200 participants in these presentations. The objectives of these eight events were to understand the underlying issues that cause us to perceive a system to be complex, and formulate a set of fundamental research questions whose pursuit would advance abilities to address these issues. © 2007 Wiley Periodicals, Inc. Syst Eng: 260–271, 2007
... Applications of theories of complex adaptive and dynamical systems are needed to successfully address many of the HFE complex humansystems integration problems, including the fundamental understanding required for modeling and simulation of human, social, economic, environmental, and cultural aspects of work systems, living areas, and the use of smart systems and smart consumer products (Ahram, Karwowski, & Amaba, 2011;Rouse, 2007). To adequately address the challenges for human-centered design of contemporary technological and service systems, researchers should develop complementary paradigms for managing complex and nonlinear human-technology-environment interactions. ...
... Complex human work systems often exhibit emergent global behaviors that are not predictable from their local properties. In the context of HFE problems, global behavior cannot be explicitly described by the behavior of the component systems, and therefore, it may be unpredictable and remain unexpected to designers, system users, or operators, leading to unrecognizable states that are often defined as system errors or aberrations that lead to degraded human performance, industrial accidents, system failures, and catastrophic losses (Dekker, 2011;Rouse, 2000Rouse, , 2007). ...
... Nonlinear dynamics can be used to examine the inherent variability in human motor performance that occurs across multiple repetitions of a task, including variability of human muscular activities (Amato, 1992;Stergiou, Buzzi, Kurz, & Heidel, 2004). Karwowski (1992aKarwowski ( , 1992b; Dooley (1997); Anderson (1999); Stacey (2001Stacey ( , 2005 Guastello (1995Guastello ( , 2000; Heath (2000) Rijpma (1997); Gillespie, Robards, & Cho (2004); Wolf and Sampson (2007); Dekker (2011) Science and engineering Goldberger, Rigney, & West (1990); Rouse et al. (1992); Rouse (2003Rouse ( , 2007Rouse ( , 2008; Banerjee, Rondoni, & Mitra (2011) Social science Eve et al. (1997); Axelrod (1997); Byrne (1998);Helbing (2012) On the basis of evidence from published research in HFE and related fields of medicine, science, and engineering, it can be postulated that many complex human systems or their interacting components and interactions may indeed exhibit high sensitivity to initial conditions that can lead to chaotic system behaviors. Today, such behaviors would typically be treated as anomalies or system aberrations (or unwanted events), as they do not fit the static and linear view of the many concepts that are widely accepted in HFE theory and practice. ...
In this paper, the author explores a need for a greater understanding of the true nature of human-system interactions from the perspective of the theory of complex adaptive systems, including the essence of complexity, emergent properties of system behavior, nonlinear systems dynamics, and deterministic chaos.
Human performance, more often than not, constitutes complex adaptive phenomena with emergent properties that exhibit nonlinear dynamical (chaotic) behaviors.
The complexity challenges in the design and management of contemporary work systems, including service systems, are explored. Examples of selected applications of the concepts of nonlinear dynamics to the study of human physical performance are provided.
Understanding and applications of the concepts of theory of complex adaptive and dynamical systems should significantly improve the effectiveness of human-centered design efforts of a large system of systems.
Performance of many contemporary work systems and environments may be sensitive to the initial conditions and may exhibit dynamic nonlinear properties and chaotic system behaviors. Human-centered design of emergent human-system interactions requires application of the theories of nonlinear dynamics and complex adaptive system.
The success of future human-systems integration efforts requires the fusion of paradigms, knowledge, design principles, and methodologies of human factors and ergonomics with those of the science of complex adaptive systems as well as modern systems engineering.
... The complexity of a system is reflected in its architecture, referring to the structure and relationships within a complex system and often expressed in terms of layers ranging from technical infrastructure to system operations [33]. A well-chosen systems architecture may enable new levels of integration and allow for new functionality [14]. ...
... Development standards and model-based systems engineering (MBSE) are often used to complement organizational approaches [40], [41]. An important rationale for using modeling and simulation is their potential to reflect multifaceted perspectives and interests [33]. Through transparency and actionability, simulation models can create new information about complex systems and help engineers and managers make complex design choices [42]. ...
Complex systems increasingly include embedded digital technologies that interact with and are constrained by physical components and systems. Although these systems play a central role in our society, they have only been scarcely addressed in contemporary research on digital transformation and the organization of innovation. This article explores the digital transformation in complex products and systems and its consequences for organizational design. A longitudinal study of avionics development since the 1950s uncovers the application of digital technologies, first as a sequence of initial experiments, followed by the use as add-on functionality, then as an integral part of achieving critical functionality in systems, and currently combining add-on and critical functionalities enabling generativity. The findings emphasize the evolution of the intricate relationships between the systems architecture and organizational approaches when digital technology enables and enforces increased complexity, expanded functionality, increased systems integration, and continuous development. These nested dependencies are accentuated by the complexity that has emerged beyond human cognition, where increasingly sophisticated boundary objects based on modeling, simulation, and data play an important role in the organization's ability. Boundary objects relate and decouple the multifacetted dynamic relation between organization and architecture. The results also extend existing perspectives on platform strategies by outlining the importance of generativity in combination with criticality control, rather than market control. Criticality control in combination with generativity has become imperative not least as generative digital technologies have become central in achieving critical properties such as safety. Several avenues for further research are outlined.
... An important focus of SSE is then the development of architectures able to support the design and development of service systems throughout their lifecycle [1]. Architectures are useful for understanding, designing, and operating complex systems such as service systems because they identify the levels at which such systems operate, and can help to improve the structure and behavior of system entities prior to and during deployment of the system [3]. ...
... This paper proposes an intentional architectural framework for developing knowledge-intensive service system architectures (IAF-KISSA). In this research, an architectural framework refers to a metamodel enabling the creation of specific service system architectures; the latter is taken to be an instance of a service system as entities, relationships, behaviours, and performance [3]. Intentional modelling refers to the design and development of systems using the concepts of intentional agents, their goals, and the strategies used to achieve them; this approach has been implemented in a number of related areas including enterprise modeling, requirements engineering, and service-oriented computing [4,5,18]. ...
... According to Bar-Yam (2003), a complex system exhibits behaviours not understandable, and which may not be inferred from the structure and behaviour of its component parts. These perceived complex behaviours can be attributed WR RQH RU PRUH RI WKH IROORZLQJ FKDUDFWHULVWLFV ³ODUJH numbers of elements, large numbers of relationships among elements, nonlinear and discontinuous relationships, and XQFHUWDLQ FKDUDFWHULVWLFV RI HOHPHQWV DQG UHODWLRQVKLSV´ (Rouse, 2007). Since these systems show evidence of complex structural and operational characteristics that are not accounted for within the traditional systems engineering framework, engineering research needs to propose a new modelling framework that addresses these characteristics. ...
... Since these systems show evidence of complex structural and operational characteristics that are not accounted for within the traditional systems engineering framework, engineering research needs to propose a new modelling framework that addresses these characteristics. As presented in Fig. 1, complexity in enterprise systems is due to one or more factors: system modelling through its architecture and multiple scale and time characteristics; system interactions through its internal interconnections and interfaces with other systems; system multiple objectives and multiple stakeholders, resulting in frequent trade-offs in the analysis process; system learning, resulting in adaptation and reconfigurability capabilities; system context, dealing with the environment in which the system operates; and, system information through the collection and distribution of data (Rouse, 2007). The complex enterprise systems architecture framework needs to address the physical component systems, the social organization of the component systems, as well as behaviour characteristics of individual humans and social organizations components. ...
This work comes as a contribution to the efforts that are undergoing within engineering systems community to account for the increased complexity of todays manufacturing or service systems. These systems are becoming more and more complicated due to the increase in the number of elements, interconnections within the system, and necessary integration with other systems. Moreover, through the emphasis on self-organization and considering the multi-stakeholders context and objectives, these systems are crossing the line towards complexity. There is a need for developing a framework to be used in modeling, analysis, and integration of systems that operate in uncertain environments, in which characteristics such as adaptation, self-organization and evolution, or in other words behavior prediction, need to be addressed. The proposed complex enterprise systems framework combines knowledge coming from complex systems science and systems engineering domains, and uses computational intelligence and agent-based systems simulation methodologies. The approach requires computational experience in manipulating large amounts of data and building large-scale simulation models. A significant result to be made possible by this research is that systems may no longer have a fixed, life-cycle long, design based on identified requirements; systems will be engineered to evolve and adapt as needed during the operational phase, while respecting their operational environment constraints.
... In general, an enterprise system can be modeled as a highly interconnected and layered network of physical, economic, informational, and social relationships. It is rooted in the idea that many natural, social, and economic phenomena are complex networked systems (Arthur, 1999;Rouse, 2007a). In the sciences, for example, biologists have examined networks of interactions between genes and proteins to study the behavior of organisms, to model diseases, or to explore the dynamics of food webs (Cohen, Briand, and Newman, 1990;Kauffman, 1969;Newman, 2003). ...
... This can be illustrated by the simple fact that the purchaser of an aircraft or an automobile must acquire an instance of every component of the product system, whereas consumers of healthcare would never avail themselves of every service offered by a single provider. The bottom element of Figure 2 addresses design via analytics, including complex system models (e.g., Rouse, 2003Rouse, , 2007aSage and Rouse, 2009), system architecture frameworks (e.g., Bernus and Nemes, 1996;Cloutier et al., 2010;Mertins and Jochem, 2005;Petrie, 1992), organizational simulations and games (e.g., Rouse and Boff, 2005), network and ecosystem visualizations (e.g., Basole, 2009;Moody et al., 2005), and statistical methods for data mining and enterprise intelligence (e.g., Han and Kamber, 2000). Consideration of these models, methods, and tools begs the question of system representation. ...
This article describes the development of a modeling hierarchy for complex enterprise networks. Drawing on the extant modeling literature, these network models are elaborated in terms of 4 salient problem characteristics: conversions, flows, controls, and social/organizational relationships. The authors relate these 4 characteristics to phenomena, representations, micromodels, macromodels, and modeling tools. The roles of information and incentives in complex enterprises networks are considered. Examples of 2 domains, global manufacturing and healthcare delivery, are woven through these discussions of alternative representations and models. The authors conclude by providing a structured comparison of these 2 domains, discussing theoretical and practical implications, and presenting opportunities for future enterprise transformation research.
... Performing network analysis allows one to inspect the complex structure of interfirm relationships [23]. Studies employing network analysis have been conducted to analyze interfirm relationships [24][25][26][27][28][29], such as competitor identification [30], firm ranking [17], and mapping of corporate linkages [31,32]. However, these studies have failed to capture industrial market segmentation using the notable characteristics of financial transaction networks (FTNs), such as directed and multiple transactions, transaction volumes, and firm attributes. ...
Market segmentation is a challenging and necessary task for improving marketing strategies and allocating resources effectively in a complex scenario of industrial or business-to-business (B2B) markets. Most of the traditional methods have not provided comprehensive and detailed guidelines to process massive, complex, and complicated data from financial transactions in industrial market. Furthermore, they often overlook the overlapping nature of segments, where a firm might belong to multiple segments of market. Financial transaction networks (FTNs) can be depicted as graph, where nodes and edges represent firms and transactions, respectively. A few network analysis studies have been employed for industrial market analysis, but those efforts are missed to capture the important features of financial transactions, such as the number of transactions, transaction volume, direction of transactions, and firm-specific attributes. In this study, we propose a novel method for industrial market segmentation that utilizes both financial multiple transactions and attribute of firms. By integrating a graph-based generative probabilistic model, we reveal and understand the complex and overlapping relationships within financial transaction data. Subsequently, we apply the proposed method to a real-world dataset. The results show that the proposed method outperforms established methods in network analysis, such as clustering and community detection in transactional industrial market (B2B) network.
... In this paper, we focus on the boundaries of emerging CoIS with both generative and safety-critical features based on the three perspectives introduced above -system design, operation, and organization. Compared to existing perspectives on CoPS boundaries that are emphasized in an operational environment and defined by an organization with a relatively high degree of authority over the system [65], the emergence of AI and autonomy in these systems implies that systems boundaries become more fluid and diffuse [2]. In such systems where human and autonomous agents both play a role, a new mode of complex interactions may emerge as well as new interfaces between intelligent and non-intelligent artifacts. ...
Rapid developments in artificial intelligence (AI) are driving the evolution of complex products and systems (CoPS) into complex intelligent systems (CoIS). The introduction of AI implies generativity and increasingly fluid boundaries in such systems and presents challenges for organizations to control and manage systems that are safety critical. Building on a case study representing future CoIS, this paper explores fluid boundaries in CoIS, including approaches for navigating system criticality and generativity. The findings point to the relationship between fluid boundaries and a stable organizational and system core, along with a shared core mission. Together, they serve as a platform that enables both contributions from various constituent systems and dynamic reconfigurations of the overall system-of-systems (SoS). System criticality and generativity are navigated through setting bounds to generativity by checks and balances involving both human and AI, including safety requirements for constituent systems and overall human oversight. Such an approach extends beyond traditional system integration activities and alters the role of CoIS integrators
... A key attribute of such an ecosystem is its ability to adapt, emerge, and evolve to internal and external changes. Thus, complex systems exhibit emergent behavior and are composed of dynamic entities called agents that adapt and evolve (Rouse, 2007). Research on CAS has emerged in the last few decades to understand the behavior of myriad, interconnected processes and agents from a system-wide perspective. ...
ABSTRACT: ⤵️
The cutting-edge economy has a lot of new wheels that turn the fortune of contemporary times.
Monetary innovation is no question one of those. The new credit-only economy depends on these
computerized monetary administrations. As of late, financial organizations, particularly banks, have
become more subject to these howdy tech administrations. For the review, the information has been
gathered by poll overview utilizing the positioned technique for examination utilizing SPSS, in which
IT-related individuals have been studied. The study explains the characteristics and features of FinTech
ecosystems emerging in Bangladesh. The "bKash" in Bangladesh is particularly appropriate for our
research as it seeks to become a world-class Fintech hub for innovation by capitalizing on the
opportunities brought by global technological trends. The study uses the CAS process model for
analyzing biological systems, difficulties, and possibilities of fintech, especially bKash in Bangladesh.
The results show that bKash has a promising future can significantly uplift Bangladesh's economy
... There has been an increase in network-based approaches analyzing inter-firm relationships (Amaral and Uzzi, 2007;Barringer and Harrison, 2000;Borgatti and Foster, 2003;Fombrun, 1982;Möller and Rajala, 2007;Rouse, 2007). There has been increased interest in understanding these relationships in a network because firms do not operate on their own; they are part of a large, complex system of components and relationships between components (Granovetter, 1985;Gulati et al., 2000). ...
Ranking firms in an inter-firm transaction network is a crucial task for identifying key players in an industry, thereby explaining the agglomeration of economic activities and assisting with competitor identification. To the best of our knowledge, despite the advantages of network-based approaches in market analysis, few studies have employed network analysis tools to rank firms. However, these studies failed to capture the characteristics of inter-firm transaction networks (i.e., evolving over time, having multiple edges between nodes, among others). In this study, we propose a new ranking method, FirmRank, that identifies key players based on centrality metrics in network analysis, leveraging inter-firm transactions to discern the characteristics of an inter-firm transaction network. Our proposed ranking method is evaluated using real-world datasets from a corporate information database, and the evaluation results demonstrate the superiority of our method over well-known ranking methods—PageRank and age-based PageRank.
... As mentioned in the previous section, the various stakeholders relative to a given cause or subject (Freeman, 1984), in our case, DLT provision regulation, may find themselves in potentially conflicting positions with regard to their respective objectives and interests (Rouse, 2007), which highlights the importance of establishing a process that leads to an acceptable and effective result for all affected parties. Moreover, once they were given "voice" in decision makers' deliberations of a regulation or a law, it was found that the stakeholders considered the Scholl, M.P.R. Bolívar Government Information Quarterly xxx (xxxx) xxx-xxx process fair, and they more readily accepted the decision and complied with the rule or regulation (Lind & Arndt, 2016). ...
Distributed ledger technologies (DLTs) such as Blockchain appear to have disruptive economic potential to traditional finance-sector and non-financial markets as the success of crypto currencies demonstrates. While traditional financial and securities markets are highly regulated, DLT transactions and exchanges have so far widely remained unregulated, which potentially poses major threats to individual investors and, via money laundering, terrorist financing, and tax evasion, to society at large. Governments have been taking different approaches to monitoring and regulating the fledgling DLT provision arena. However, the small jurisdiction of Gibraltar, an affluent self-governed and self-financed British overseas territory with a population of 34,000 at the Southern tip of the Iberian Peninsula has taken a proactive approach. What makes this case stand out is that Gibraltar is not only is the first jurisdiction worldwide to regulate general DLT provision, but it is rather using the regulation as a competitive tool and a means for creating new public value. The study inquires the potential benefits and challenges to this particular approach to DLT provision regulation, and it investigates and presents the various facets of public value generation, which might serve as a poster child for fast and smart regulation in the digital age.
... Checkland [1] describes scale as a system of perspectives where point "T" (for transformation) is our engineered system, scales smaller than that point represent subsystems or components, and larger scales represent viewpoints of the wider system. Rouse [2] expresses such scaling in the design realm as "system intent." In today's environment, we should be considering these broader intents in our system design in a context of environmental sustainability, social responsibility, equity, and resilience. ...
This paper presents the application of a sociotechnical modeling framework to capture the wider global system impacts of the U.S. shale gas boom in a context of the larger global energy marketplace. The global energy markets represent a complex adaptive system. Existing models are not sufficient to reflect the complexity of changes presently occurring in the system, and fail to trace interactions across a wide enough system of interest. This project introduces conceptual models of flow and interaction in the global energy marketplace in order to inform the design of analytical models that identify more complex behaviors of the system. This is an example of a set of emerging methods and tools for the design of complex systems and represents an important first stage in a research program to integrate complexity analysis tools with traditional system design tools.
... Most modern systems cannot be simply considered as the total of their constituent parts. They are rather, large dynamic highly heterogeneous nonlinear systems that are dependent of very complex networks to achieve their goals [1]. ...
eHealth covers the Information and Communication Technology (ICT) related interaction between healthcare professionals and the system clients. It can also include telemedicine services, systems for monitoring and assisting patients and health information networks. Health ICT industry can become the third largest industry in the health sector with a global turnover of €50-60 billion. While the advantages of ICT health related systems are rather noticeable, they have not yet enjoyed prevalent deployments. Many studies have been carried out to identify the reasons for that. Several of these reasons are at least implicitly related to the underlying communication networks and the Internet characteristics. The heterogeneous nature of the Internet, along with its intentional lack of central control and loose hierarchy pose many challenges for its management. In this work, a framework for analysis and management of such complex systems is presented. In particular, the advantages of utilizing these approaches to improve the overall performance of Internet and network-based healthcare systems for a wide range of operating conditions are discussed.
... This dual concept of "things" and "relationships among the things" is common across many fields, although the vocabulary varies from field to field, as suggested by the terms in Table 1. No difference has been found between describing a system as consisting of "nodes" and "links" and describing it as having "entities" and "relationships," so for the purpose of this paper, all the terms in a column of Table 1 (2000), Bolton (2007), Moses (2002;, Bar-Yam (1997), Miller and Page (2007), Rouse (2007), Calvano and John (2004), Holland (1995), and Mostashari and Sussman (2009) correspond to the framework here. It is shown that the six types and subtypes of complexity (Table 2) adequately address most of the types in the literature, given that artifact and development process complexities have some differences. ...
This paper shows how the literature on complexity is related to complex systems and to systems engineering. A framework for types of complexity is proposed that includes three types of structural complexity (size, connectivity, and architecture), two types of dynamic complexity (short‐term and long‐term), and one additional type, socio‐political complexity. These types cover most of the characteristics of complexity mentioned in the reviewed literature; omissions are noted.
It would be advantageous to identify specific measures of complexity so that the complexity of systems or development programs could be compared and tracked, and risks could be identified and mitigated. To this end an overview of systems engineering measurement is provided, followed by a discussion of how complexity types might be able to be measured. At this point, however, a composite measurement of the six types of complexity is not recommended.
Background:
Innovation and human adaptation in the face of unfolding catastrophe is the cornerstone of an effective systemwide response. Capturing, analysing, and disseminating this is fundamental in developing resilience for future events. The aim of this study was to understand the characteristics of adaptations to practice early in a paediatric major trauma centre during a mass casualty incident.
Methods:
A qualitative interview study of 40 healthcare staff at a paediatric major trauma centre in the immediate aftermath of a terrorist bombing was conducted. An inductive thematic analysis approach was used, followed by a deductive analysis of the identified adaptations informed by constructs of resilience engineering.
Results:
Five themes of adaptations to practice that enhanced the resilient performance of the hospital were identified: teamworking; psychologically supporting patients, families, and staff; reconfiguring infrastructure; working around the hospital electronic systems; and maintaining hospital safety. Examples of resilience potential in terms of respond, monitor, anticipate, and learn are presented.
Conclusions:
Our study shows how adaptations to practice sustained the resilient performance of a paediatric major trauma centre during a mass casualty incident. Rapid, early capture of these data during a mass casualty incident provides key insights into enhancing future emergency preparedness, response, and resilience planning.
The engineering community has made significant advances in traditional diversity measures in the last 50 years, but because the world has become more complex, the engineering community needs to embrace new ideas that are outside the realm of traditional engineering disciplines. This paper identifies that individuals are the sources of new ideas, but it is organizations that transform these ideas into value‐delivered innovations. It examines the relationship of maximizing information diversity to promote idea discovery while minimizing work force diversity to promote the sharing and acceptance of these ideas. It then discusses the concept of inclusion as a necessary team skill for organizations to deliver these ideas as innovations. This paper concludes by identifying systems engineering behaviors that create a diverse environment for idea discovery and an inclusive team environment that overcomes challenges and transitions ideas into delivered innovations, thus providing a competitive advantage.
Over the past decade, the Department of Defense (DoD) developed an emergent network of systems that operate together on the battlefield to provide value to the DoD. Advances in systems engineering have enabled the DoD to develop better, more integrated, and more complex systems in this time frame. One of the emerging topics in systems engineering is the importance of nonfunctional attributes captured in the ilities of a system. One of these ilities, interoperability, captures the ability of a system to operate with other systems as DoD systems do on the battlefield. This article applies social network analysis tools and methods to understand the effect of expanding the definition of interoperability beyond information flows to include shared resources and physical connections. Social network analysis metrics, such as density and average degree, quantify the effect of expanding interoperability to capture other aspects of interoperability. By capturing these additional aspects of interoperability, the number of connections in the DoD network of systems increases by nearly a factor of four. Thus, if the ility of interoperability narrowly focuses on information flows, then systems engineers could potentially miss a large number of important connections in a network of systems.
This chapter addresses the nature of enterprises and emergent behavior, including the path‐dependent aspects of emergent behaviors and reviews the notion of complexity and complex systems. It discusses multi‐level models to provide a framework for consideration of emergence in societies, cities, institutions, and companies. The examples elaborated in the chapter focus on understanding and addressing complex problems and not necessarily advancing the discipline of modeling and simulation. The chapter computationally models the functioning of the complex enterprise of interest to enable decision makers, and other significant stakeholders, to explore the possibilities and implications of changing their enterprises in various ways. Nevertheless, the use of modeling and simulation is key to uncovering emergent phenomena, either provided by the computations or the humans in the loop. The chapter reveals that our models and simulations are most successful when supporting decision‐making teams with strong leadership skills and commitment to moving the enterprise forward.
This article addresses complexity in information systems. It defines how complexity can be used to inform information systems research, and how some individuals and organizations are using notions of complexity. Some organizations are dealing with technical and physical infrastructure complexity, as well as the application of complexity in specific areas such as supply chain management and network management. Their approaches can be used to address more general organizational issues. The concepts and ideas in this article are relevant to the integration of complexity into information systems research. However, the ideas and concepts in this article are not a litmus test for complexity. We hope only to provide a starting point for information systems researchers to push the boundaries of our understanding of complexity. The article also contains a number of suggested research questions that could be pursued in this area. Purchase this chapter to continue reading all 12 pages >
This article addresses complexity in information systems. It defines how complexity can be used to inform information systems research, and how some individuals and organizations are using notions of complexity. Some organizations are dealing with technical and physical infrastructure complexity, as well as the application of complexity in specific areas such as supply chain management and network management. Their approaches can be used to address more general organizational issues. The concepts and ideas in this article are relevant to the integration of complexity into information systems research. However, the ideas and concepts in this article are not a litmus test for complexity. We hope only to provide a starting point for information systems researchers to push the boundaries of our understanding of complexity. The article also contains a number of suggested research questions that could be pursued in this area.
Breakthroughs in medical science, innovations in medical technologies, and improvements in clinical practices occur today at an increasingly rapid rate. Yet because of a fragmented healthcare delivery system, many Americans are unable to benefit from these developments. How can we design a system that can provide high-quality, affordable healthcare for everyone? In this book, William Rouse and Nicoleta Serban introduce concepts, principles, models, and methods for understanding, and improving, healthcare delivery. Approaching the topic from the perspectives of engineering and statistics, they argue that understanding healthcare delivery as a complex adaptive system will help us design a system that is more efficient, effective, and equitable.The authors use multilevel simulation models as a quantitative tool for evaluating alternate ways of organizing healthcare delivery. They employ this approach, for example, in their discussions of affordability, a prevention and wellness program, chronic disease management, and primary care accessibility for children in the Medicaid program. They also consider possible benefits from a range of technologies, including electronic health records and telemedicine; data mining as an alternative to randomized trials; conceptual and analytical methodologies that address the complexity of the healthcare system; and how these principles, models, and methods can enable transformational change. © 2014 Massachusetts Institute of Technology. All rights reserved.
Enterprises are essential to the sustainment of our modern society. However, they rarely receive the level of attention and rigor that technical systems do. Treating enterprises as systems is a promising approach, but enterprises depend on a substantial social component. The inherent complexity of social systems introduces epistemic limitations that inhibit our ability to model social systems and predict their behavior. Consequently, traditional engineering approaches that rely on prediction and control can be ineffective or misleading when applied to enterprises. In this paper we explore the implications of these epistemic limitations on the engineering of enterprises. We conclude that it is necessary to apply dynamic strategies to mitigate these limitations and adapt enterprise modeling efforts accordingly. The goal with enterprises, in contrast to traditional technical systems, is to influence rather than control. We outline an interdisciplinary research agenda to progress toward this goal.
Software engineering is known as a problem solving activity and modeling. All the principles in software engineering are emphasized on the conditions for software producers are unpredictable because of the interactions and mutual relationships between all the factors involved in creating and those conditions never allow full control over this process. Rapid changes in technology have created the double problems in the field of planning and organizing the structure of manufacturing software engineering projects. Since the emergence of software engineering processes, models and processes focus on reducing non-forecasting in the process model due to provide available software in a certain period with predicted cost, but it cannot be out of this complexity, and the simplicity would not lead by imposing a simple model to process model. The aim of this paper is recognition dynamic of the software development process with the use of nonlinear science and chaos theory and demonstrate the software process among the complex systems and then moved it to chaos domain. In this article, software engineering is proven as a nonlinear complex system, hence it is located in the ordination complex systems. It continues to review the components and features Self-Organized in software engineering. Then chaos techniques to analyze software production process and fractal structure of process models is presented. After demonstrating the software engineering is chaotic, we can use chaos theory and techniques relating to building complex systems for more accurate understanding of the process and prevented impact of inappropriate nonlinear to cost, function, relationship and program features.
The capability-based acquisition approach emphasizes required operational capabilities (rather than individual system performance) and thus directly leads to the simultaneous development of systems that must eventually interact within a system-of-systems. As a result, system interdependencies in the development and acquisition processes increase complexity and risk as well as complicate the tradeoff between risk and capability. The authors' prior work has developed a Computational Exploratory Model to simulate the development processes of interdependent systems intended for a system-of-systems capability. The progress documented in this paper focuses on the impact of network topologies on the propagation of disruption and achievement of target capabilities. The enhanced model differentiates the effectiveness of alternate configurations of constituent systems and quantifies the impact of varying levels of interdependencies on the timely completion of a project that aims to achieve a target capability. A proof-of-concept application problem examining options for missile defense detection and tracking is presented, including the identification of non-dominated solutions thatoffer a balance of development time and capability level.
Clients of ergonomics/human factors increasingly know that a systems E/HF approach is required to drive human factors delivery in the infrastructure and transport industries and major projects. However, the E/HF profession shows little clarity on what is involved and how this fits within a systems engineering perspective and approach. The paper is based on recent work by the author defining six attributes of a systems E/HF approach: systems focus, acknowledgement of interactions, concern for context, holistic concerns and approach, recognition of emergent properties of systems and embedding of the professional effort in a sector or organisation. The attributes will be illustrated by descriptions of work in rail human factors carried out by the author and colleagues at University of Nottingham and at Network Rail.
This study examines the topological characteristics of interfirm collaboration networks (CNs) in the global electronics industry. Our results show that high-performing firms exhibit significant relational CN power, manage CNs that follow a power-law shape degree distribution, are predominantly horizontally integrated with low geographic complexity, and maintain a balanced exploration-exploitation collaboration relationship portfolio. We complement our topological analysis with graphical visualizations of each of these CNs over three timeframes (2004-06; 2007-09; 2010-12). Theoretically, we demonstrate the association of topological CN characteristics with high-performance of firms. Methodologically, our study defines and implements a data-driven analyses and visualization of CNs in high clockspeed industries. Our study makes important managerial contributions to the systemic design, engineering, and management of CNs.
This work comes as a contribution to the efforts that are undergoing within engineering systems community to account for the increased complexity of today's manufacturing or service systems. These systems are becoming more and more complicated due to the increase in the number of elements, interconnections within the system, and necessary integration with other systems. Moreover, through the emphasis on self-organization and considering the multistakeholders context and objectives, these systems are crossing the line towards complexity. There is a need for developing a framework to be used in modeling, analysis, and integration of systems that operate in uncertain environments, in which characteristics such as adaptation, self-organization and evolution, or in other words behavior prediction, need to be addressed. The proposed complex enterprise systems framework combines knowledge coming from complex systems science and systems engineering domains, and uses computational intelligence and agent-based systems simulation methodologies. The approach requires computational experience in manipulating large amounts of data and building large-scale simulation models. A significant result to be made possible by this research is that systems may no longer have a fixed, life-cycle long, design based on identified requirements; systems will be engineered to evolve and adapt as needed during the operational phase, while respecting their operational environment constraints.
Emergent behaviors exist in biological systems, physical systems and human performance. Little is currently known about constructing an interoperable network of systems and the incorporation of known emergent behaviors. The purpose of this panel is to explore the methods and principles in developing an architecture including the emergent behavior.
SoS displays a global complexity that cannot be adequately managed by hierarchical structures and central control; therefore, traditional systems engineering and management approaches are inadequate and insufficient for a SoS.
The challenge is how to understand the initiation mechanisms of the emergent behaviors for a particular system architecture model so that the resident beneficial or harmful emergent behaviors can be enhanced or mitigated with selected changes in the current system architectural model. Is model‐based the only feasible approach to develop the architecture model with emergent behavior? If this is the answer, what kind of modeling methodology? Should it be solely based on agent‐based modeling? or combination of SysMl and agent‐based? Is SysML ready to deal with emergent behavior?
For a non‐modeling consideration, can we plan for the beneficial or harmful emergent properties? How do we overcome development friction that is bound to arise when there are complex, independent, overlapping governances? We may need experiment with “guided” emergence that help produce the desired SoS capabilities and behaviors.
The customer requirements for systems‐of‐systems evolve over time. There may only be system‐of‐system modeling at the level of the government agency, who actually procures but does not build, and not at the level of contractors who build the next generation of technologies.
Even we have methodology to build SoS architecture, whether model‐base or non model‐base. How do we train systems engineers to understand the emergent behavior to be incorporated in a newly designed SoS?
This paper is concerning about systems engineering which has strong relationship with software engineering field both in the concepts and in the practical aspects. This paper starts with the explanation about some important concepts in systems engineering such as definitions, characteristics, and real practice examples. The next part of this paper will discuss about the need of systems engineering and the professional competencies of systems engineers. The las part of this paper will discuss about the benefits of teaching systems engineering to software engineering students.
Like many complex enterprises, UK Defence is struggling with a series of frustrating organisational, system and technology problems, including: increasing scale and complexity, increasing need for continuous capability evolution and increasing need to optimise at the system of systems level. Within this context, Niteworks provides flexible decision support to address three priority areas: support to current operations; support to decision making by the Joint Capabilities Board (JCB), and; support to complex acquisition. This paper describes Niteworks' unique collaborative partnership construct that allows UK Ministry of Defence (MOD) and industry engagement in aneutral, consensus-based, yet agile manner to address a wide range of complex decision support activities. It details the enabling commercial arrangements, the underlying principles for success and a generic decision model. This approach, the 'Niteworks Way', has been designed by applying systems engineering thinking and methods to the Defence Enterprise and Niteworks within it. The approach has been demonstrated to be flexible and responsive by addressing issues across the range of the UK Defence Enterprise. It has also delivered sizeable value to both UK MOD and industry, demonstrating that collaborations can work effectively and that both industry and the MOD can benefit from embracing new concepts and commercial arrangements.
Information management and documentation are a major challenge for the modelling of complex systems. It is difficult for others than the modellers themselves to decide if the structural model at hand is sufficient in quality, scope and underlying information for the desired purpose, as it is usually not possible to reproduce the modelling purpose. Reproducibility in modelling in general is hard to achieve, as the modelling process depends on several individual influencing factors. Existing literature on reproducibility in modelling reveals that the creation process of the model as such and the information over the creation process are both essential for the reproducibility of the generated model. We identify three areas of support in order to increase the reproducibility of structural modelling. We provide a framework for increasing the reproducibility of structural modelling. A documentation template to capture the information generated within the modelling activities is suggested. As a result, the framework highlights the importance of documentation for modellers and ensures that all the required information is captured. The application and success evaluation show the benefit of the framework by increasing the reproducibility of structural modelling and that it also offers opportunities for better information management in general.
Reforming the U.S. educational system and workforce is a national challenge. Both industry leaders 30 and academics 2,28 concur that improving the quality, quantity [and alignment] of U.S STEM graduates are national imperatives. Models of the U.S. educational system, using complex sociotechnical systems’ approaches and tools that instill systems thinking, offer a holistic perspective to the educational and workforce challenges we face as a nation and allow us to identify and understand challenges associated with workforce preparedness, and increasing the number and technical excellence of STEM graduates 9,13,29. These models represent a sociotechnical system of systems with various sub-systems, each one representing an inherently complex and interdisciplinary problem of maintaining bi-directional, non-linear feedback relationships between one another. Each system involves multiple disparate stakeholders that need to collaborate within a time-and resource-intensive process while embedded in a larger sociotechnical system, aligned with the people, ideas, and support required to support desired global outcomes, of the system of systems, society and industry in particular11.
As the world increasingly becomes characterized by the provision of services to customers, service research faces many challenges and needs a new vision. Many of these challenges stem from the fact that technologies are continually experiencing dramatic change, and savvy customers are increasingly demanding more qualified services. Although research and experiential findings on services in many disciplines have achieved impressive results, systematic research on modern services systems is rarely considered. Aiming at services systems (not only services themselves), this paper discusses the contributions and the potentials for the field of systems theory for Services Science, Management and Engineering (SSME). Thus, SSME can provide suitable referential clues for discussing impacts of systems theory on services research, and systemic thinking can be applied to discuss key issues in SSME. Providing basic definitions of services and services systems, this paper elaborates on the research objects and methodologies of SSME from systems perspective, and proposes that systems theory can be applied as the foundations of services engineering. This paper further sheds light on systemic issues in services science and engineering, services operations, and services systems management. It also provides a summary of relevant theories and tools applied to services systems at the systems level. Finally, some potential prospects in SSME research are presented to meet research demands and practical challenges.
Single systems and systems of systems, alike, demand management approaches focused on performance; but system of systems management can only responsibly address performance if it accounts for characteristics such as the presence of distinct subsystems pursuing possibly disparate purposes, a characteristic by definition of no concern to single system management. By extension then, with to manage in many ways to measure, systems of systems demand performance measurement schemes that accommodate the traits that set them apart from individual systems. Enterprise AID represents a means for measuring and hence managing the current or future performance of systems of systems. Enterprise AID - or simply AID, for assessment, improvement, and design - is a methodology for the design and use of performance measurement systems able to uniformly address problems encountered with extant or envisioned, single system or system of systems type enterprises. This paper describes advantages held by the AID methodology for system of systems performance measurement, and it does so within a context set by appropriate definitions, selected methodology elements, and an application example focused on the selected elements. This paper concludes with a recognition of performance related issues attendant to system of systems improvement or design.
Achieving sustainability involves complex processes of technology, people, institutions, and the environment. The sustainability challenge requires a combination of social, political, and technological efforts. This paper discusses processes for technological change in order to meet the sustainability challenge. These complex processes are found to be a suitable application for systems engineering and for systems architecture in particular. Based on a thorough review of the literature, an architecture framework is developed to support management of portfolios of sustainable technology projects. This architecture framework is validated through a case study process, providing enhancements and lessons learned. The full architecture framework construct and associated proposed implementation approaches are presented, demonstrating the need for and applicability of such systems engineering approaches to achieve sustainability. Lessons learned from case studies and development of representative architectures are also described. ©2012 Wiley Periodicals, Inc. Syst Eng 16
Purpose
The purpose of this paper is to assess the potential factors that may generate complexity in managing operations in home care (HC) organizations. Hence, a framework which might assist decision making regarding complexity‐driven issues in HC organizations is developed.
Design/methodology/approach
Based on a systems thinking view, a framework was developed identifying complexity factors in HC organizations. The paper is based on field research to explore the practical challenges of managing operations in HC structures. The authors additionally give a state of the art in many scientific domains; definition of complexity and its implications; complexity in health care; description of home care operations and their management. The literature review enlightens the lack of consensus on HC complexity and thus provides a broad view and a critical analysis of the underlying concepts.
Findings
Proposal of a HC operations complexity model (and validation due to a practical application).
Practical implications
The framework developed by the authors permits one to identify rapidly the existing complexity factors which may create potential damages regarding value creation and cost improvement in HC. Two kinds of HC complexity are identified: HC organization complexity and HC individual patients' therapeutical projects complexity. Complexity factors are grouped into five categories: factors related to size; factors related to variety; interdependence factors; factors related to uncertainties; and factors related to context. By identifying the factors, decision support systems and response plans may be more adapted to the potential existing short comings of operations.
Originality/value
Based on authors' extensive knowledge of HC practices, as well as an international systematic review of literature, this paper contributes to a better characterization of factors inducing complexity in the HC context. To the best of the authors' knowledge, operations management literature lacks a general framework enabling a better identification and understanding of what makes HC operations specific. The paper presents an innovative way to analyze HC complexity using a systems thinking‐based approach. In this sense, its contribution is very complementary to traditional operations management models.
The first possible step to achieving an understanding the design and management of complex systems is to understand what is meant by 'complexity engineering'. Multitude of concepts in complexity engineering has been contextually developed in a wide range of disciplines by from scientists who are interested in bio-inspired methods and working in computer science or mobile robots, or they come from the area of systems engineering. A very fundamental need in complexity engineering is a general and well-developed methodology, set of design rules, and patterns for engineering 'manageable' and 'value robust' system architectures. The engineering of the Systemic Financial Risk Infrastructure Systems (SRITS) has notable resisted the application of the traditional system engineering approaches. SRITS is a complex adaptive system in the manner described by complexity science. The paper briefly presented a conceptual framework called CXLUD capable of dealing with the challenges posed by complex adaptive system, i.e. understanding, visualizing, designing, describing, managing and designing the future. The framework provides a means for system designers and managers to organize systems information in ways that allow for better collection, understanding and analysis of system information during early-phase conceptual design of complex systems. The paper summarizes the results from a series of design procedure case studies at one of the largest financial institutions in UK working on developing a complex systemic risk infrastructure projects to which the introduced decision making or way of thinking framework were applied.
Complex adaptive systems are usually difficult to design and control. The design of complex adaptive systems often, if not always, occurs in a context that is uncertain. In addition to the complexity of the system, the systems both affect and are influenced by a social environment containing a large variety of actors. Methods and processes that study the design of such types of systems can help scholars and practitioners to better understand, design and manage complex adaptive systems. The aim in this paper is to provide a methodology useful for designing and controlling systems developed to solve complex adaptive problems. In this paper, the complex systems life cycle understanding and design framework are introduced as an approach for satisficing of systems at conceptual design. To illustrate the methodology, a financial systemic risks infrastructure systems architecture development case study is presented. Copyright © 2013 John Wiley & Sons, Ltd.
Ergonomics/human factors is, above anything else, a systems discipline and profession, applying a systems philosophy and systems approaches. Many things are labelled as system in today's world, and this paper specifies just what attributes and notions define ergonomics/human factors in systems terms. These are obviously a systems focus, but also concern for context, acknowledgement of interactions and complexity, a holistic approach, recognition of emergence and embedding of the professional effort involved within organization system. These six notions are illustrated with examples from a large body of work on rail human factors.
General comments (specific comments inserted inline, below). Lysanne—I am taking the unusual step of sending you these comments not fully completed! I would like to finish the commentary in a second round—but unfor-tunately I will be unable to complete that before today's iTable!. Some notes on what happened, and where things stand: 1. As you will see, I have made a variety of relatively detailed comments up through 6. But I haven't yet made many overarching or general remarks. Be-fore doing that, I wanted a better grip on the overall structure or contribu-tion or intent of the paper. And up through 6 I was having trouble gleaning what exactly that was. 2. By the end of 6, on my first pass through, I felt that I was "losing the plot", to the point that it wasn't worth making more specific comments until I fin-ished a complete pass through it, so that I could locate what was going on in a general framework. 3. Then something interesting happened, reading s 7–9. Sure enough, I felt that I started to get a sense of what you were up to. Also, however, I started to wonder how much all of what preceded it mattered to the paper! In other words, I had a feeling that once I got to (what I think is) your real contribu-tion, the beginning sections began to fall away in importance (at least in my view). So what follows is the (inchoate) sense I have of what is going on in the paper: 1. The diagrams you present in 7, which (interestingly) are not much described in words, constitute something like an "analytic framework" you have devel-oped, for understanding how various views of complexity apply to the notion of design. Crucially: these diagrams—or rather the notions they are based on, of lens, application, etc.—represent your thinking, not the thinking of the people whose work you are reporting on. 2. In 9, somewhat similarly, you organize a set of issues or concerns that have to be thought about, in order to understand how complexity applies to de-sign. I wished that that had been at the beginning, not the end. .
This paper explores how service value is created in a network context and how the structure and dynamics of the value network as well as customer expectations influence the complexity of the services ecosystem. The paper then discusses what transformative role information and communication technology (ICT) plays in coordinating and delivering value and managing this complexity. A conceptual model is developed for understanding and investigating the nature, delivery, and exchange of service value and assessing the complexity of a service value network. Three central arguments are presented. First, value in the services economy is driven and determined by the end consumer and delivered through a complex web of direct and indirect relationships between value network actors. Second, the complexity of service value networks not only depends on the number of actors but also on the conditional probabilities that these actors are involved in delivering the service to the consumer. Third, ICT plays a central role in reducing complexity for consumers by providing greater levels of value network integration, information visibility, and means to manage and anticipate change.
This paper explores the factors responsible for success or failure of an automobile through a case study analysis of 10 product successes and 10 product failures in the United States auto market over the last 50 years. Through the creation and analysis of twenty case studies we have generated a first listing of 14 factors driving success or failure. Seven experts assessed the extent to which these factors were associated with each automobile. Their assessments indicate that the product development system of new automobiles, particularly those that are highly successful, is often born from financial and corporate responses to market crisis. The target segment of a new entry in the automotive market and its development process as they are adapted to economic conditions in the marketplace, coupled with leadership, are central success factors. The role of these factors play in the success of automobiles is discussed in the context of several case studies. © 2009 Wiley Periodicals, Inc. Syst Eng 13
The scope and complexity of engineered systems are ever-increasing as burgeoning global markets, unprecedented technological capabilities, rising consumer expectations, and ever-changing social requirements present difficult design challenges that often extend beyond the traditional engineering paradigm. These challenges require engineers and technical managers to treat the technological systems as a part of a larger whole. Existing system modeling frameworks are limited in scope for representing the information about engineering systems. This paper presents a conceptual framework and an improved modeling framework for engineering systems. Its value is that it allows engineers and managers an improved means to visually arrange information and structure discourse in a way that facilitates better systems engineering. It augments the existing literature by providing a clear and concise framework for an engineering system, and provides a methodology for engineers to tag and organize systems information in ways that allow for better collection, storage, processing, and analysis of systems engineering data. © 2011 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.
This article addresses complexity in information systems. It defines how complexity can be used to inform information systems research, and how some individuals and organizations are using notions of complexity. Some organizations are dealing with technical and physical infrastructure complexity, as well as the application of complexity in specific areas such as supply chain management and network management. Their approaches can be used to address more general organizational issues. The concepts and ideas in this article are relevant to the integration of complexity into information systems research. However, the ideas and concepts in this article are not a litmus test for complexity. We hope only to provide a starting point for information systems researchers to push the boundaries of our understanding of complexity. The article also contains a number of suggested research questions that could be pursued in this area.
Significant reform of the health care system sufficient to achieve universal coverage, a value-driven system and administrative simplification faces enormous barriers at the level of our societal ecosystem - barriers as large as any that can be faced in public policy. These barriers exist within the health system itself as a complex adaptive system, and are structured by our economic, legal, cultural and political systems. Because there are so many barriers, significant reform is a relatively rare occurrence. Yet it does happen and there are some important examples of major health care reforms. There are a number of lessons to be learned from the successful enactment of the Medicare and Medicaid programs that appear relevant to current and future reform efforts. First, a necessary condition for achieving significant reform is the existence of large and sufficiently enduring social forces sufficient to disrupt legislative and policy stasis and drive the necessary political solutions. Second, public sentiment and electoral "mandates" might be necessary to significant reform, but they are not sufficient. Third, assuming the theoretical capacity to manage the constellation of systemic, economic, legal, cultural and legislative barriers, there remains a political "tipping point" that must be crossed and translated into a Congressional super-majority in order to enact significant nationwide reform.
Engineering has and will continue to have a critical impact on healthcare; the application of technology-based techniques to biological problems can be defined to be technobiology applications. This paper is primarily focused on applying the technobiology approach of systems engineering to the development of a healthcare service system that is both integrated and adaptive. In general, healthcare services are carried out with knowledge-intensive agents or components which work together as providers and consumers to create or co-produce value. Indeed, the engineering design of a healthcare system must recognize the fact that it is actually a complex integration of human-centered activities that is increasingly dependent on information technology and knowledge. Like any service system, healthcare can be considered to be a combination or recombination of three essential components - people (characterized by behaviors, values, knowledge, etc.), processes (characterized by collaboration, customization, etc.) and products (characterized by software, hardware, infrastructures, etc.). Thus, a healthcare system is an integrated and adaptive set of people, processes and products. It is, in essence, a system of systems which objectives are to enhance its efficiency (leading to greater interdependency) and effectiveness (leading to improved health). Integration occurs over the physical, temporal, organizational and functional dimensions, while adaptation occurs over the monitoring, feedback, cybernetic and learning dimensions. In sum, such service systems as healthcare are indeed complex, especially due to the uncertainties associated with the human-centered aspects of these systems. Moreover, the system complexities can only be dealt with methods that enhance system integration and adaptation.
The literature of complexity is reviewed and the distinction between perceptual complexity and problem solving complexity is discussed. Within the context of two particular fault diagnosis tasks, four measures of complexity are considered. These measures are evaluated using data from two previously reported experiments which employed eighty-eight subjects. It is shown that two particular measures of complexity, one based on information theory and the other based on the number of relevant relationships within the problem, are reasonably good predictors of human performance in fault diagnosis tasks.
Trends in computer and communications technologies are enabling increased globalization and integration of enterprises, and corresponding increases of enterprise complexity.ï¾ ï¾ This article addresses management of this complexity using a complex adaptive systems framework.ï¾ ï¾ The nature of complex adaptive systems is first reviewed, drawing upon a range of examples to illustrate key concepts.ï¾ ï¾ The domain of disease control is then introduced in terms of prevention, detection, and treatment of disease, as well as major stakeholders in these processes.ï¾ ï¾ Overall approaches to managing complex systems are discussed in the context of disease control.ï¾ ï¾ Finally, strategic implications for stakeholders in disease control are considered.
The complexity of monitoring and controlling a large-scale system, such as a communication network, is considered. Relevant literature is reviewed, with emphasis on both behavioral and nonbehavioral approaches to measuring complexity. A simulated large-scale network is described that is used in an experiment to assess the effect of network redundancy and number of systems levels on human fault-diagnosis performance. Experimental data are also used to evaluate two time-varying measures of task complexity (using analysis of variance and time-series analysis). The first measure is dependent on the structure of the system; the second measure is dependent on the strategy of the person controlling the system. Results suggest that this distinction is appropriate. In addition, results emphasize the different implications that complexity can have for normal system operation and human failure diagnosis performance. Although system design characteristics such as redundancy may help to avoid the short-term effects of failures, these same characteristics may have the dual effect of making the human supervisory controller's task more difficult.