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A Quotation to Open On

Feature Article: What is Complexity Theory? Features and Implications

Systems Engineering News

  • Upcoming Submission Deadlines and Themes for INSIGHT
  • INCOSE eNote February 2011 v8 Issue 2
  • Systems Engineering Journal– early view
  • Lockheed Martin to Leverage INCOSE’s Systems Engineering Certifications Starting in 2011
  • University of Arizona Systems Engineering Pioneer Dies at 84
  • Accelerated Reliability Webinars
  • New System Dynamics Journal

Featured Societies – TBD

INCOSE Technical Operations – Systems Science Working Group

Systems Engineering Software Tools News

  • Winter 2011 issue of The Connector
  • Altia Announces New Product Releases
  • Special Offer on iThink and STELLA Software

Systems Engineering Books, Reports, Articles and Papers

  • Interaction between Requirements Engineering and Systems Architecting: An Emerging Theory Based on a Suite of Exploratory Studies
  • Systems Thinking Revolutionises Car Industry
  • CATWOE Analysis: How & When to Use It

Conferences and Meetings

Education and Academia

  • New Industrial & Systems Engineering Masters Degree
  • Idex Focus Turns To Growing Local Industry
  • HCT teams with Johns Hopkins University to boost Masters Engineering Program
  • Stevens Institute of Technology Launches New Technical Leadership Program
  • Postdoc Position Self-Adaptive (Automotive) Systems at CEA Research Center near Paris

Some Systems Engineering-Relevant Websites

Standards and Guides

  • ISO TC 159 – Ergonomics
  • IEEE Announces Standards Interest Group (Sig) For India; Move To Propel India’s Involvement in the IEEE Global Standards Process

A Definition to Close on

PPI News

A Quotation to Open On


Feature Article

What is Complexity Theory? Features and Implications

Dr Minka Woermann

Department of Philosophy; StellenboschUniversity


The term ‘complexity’ is often loosely appropriated by both academics and practitioners to describe things that lack simple explanations. However, little conceptual clarity exists regarding the meaning of complexity. This is, in part, due to the diverse history of complexity theory, which evolved from the interplay of several disciplines, including physics, mathematics, biology, economics, engineering, and computer science. Furthermore, its theoretical predecessors include movements such as cybernetics, autopoiesis, general systems theory, the theories of artificial intelligence and artificial life, chaos theory, and information theory.

Cybernetics: A theory based on feedback, entropy and information that can explain the operation of machines, as well as biological and social phenomena (Nobert Wiener).

Autopoiesis: A system’s capacity for self-production and maintenance through feedback loops (Humberto Maturana and Francisco Varela).

General Systems Theory: A logico-mathematical field in which the principles that are applicable to systems in general are derived and formulated (Ludwig von Berthalanffy).

Artificial Life: The study of living systems in artificial environments (Christopher Langton).

Chaos Theory: A field that addresses systems that display bifurcation, sensitivity to initial conditions, and other mathematically defined behaviour (Edward Lorenz).

Information Theory: A branch of applid mathematics and electrical engineering involving the quantification of the information content in a message (Claude E. Shannon).

Although it is beyond the scope of this article to study these influences in detail, one broad question that I shall try to address is ‘What is complexity theory?’ More specifically, I shall discuss two schools of thought on complexity, and try to highlight the implications that these discussions hold for how we view the goal and status of scientific practices.

Restricted vs. General Complexity

With regard to the two schools of complexity, I follow the French philosopher and complexity theorist, Edgar Morin (2007), in distinguishing between (what he calls) restricted complexity and general complexity. The central difference between these two paradigms concerns our attitude towards complex phenomena:

It is generally recognised that complex systems are comprised of multiple, inter-related processes. In terms of restricted complexity, the goal of scientific practices is to study these processes, in order to uncover the rules or laws of complexity. Researchers at the Santa Fe Institute (founded in 1984, and dedicated to the study of complex systems) support this goal, as is clear from the Institute’s aim, which is:

to discover, comprehend, and communicate the common fundamental principles in complex physical, computational, biological, and social systems that underlie many of the profound problems facing science and society today (my italics) [1].

For these researchers, complexity becomes the umbrella term for the ideas of chaos, fractals, disorder, and uncertainty. Despite the difficulty of the subject matter, it is believed that, with enough time and effort, we will be able to construct a unified theory of complexity – also referred to as the ‘Theory of Complexity’ (TOC) or the ‘Theory of Everything’ (TOE) (Chu, Strand & Fjelland 2003). In other words, the hope is that, as with chaotic phenomena, complex phenomena can also be encapsulated in a precise definition or mathematical equation. Prominent attempts at formulating such a TOC include Langton’s (1992) ideas on life at the edge of chaos, Bak’s (1997) work on self-organised criticality, and Kaufman’s (1993) work on attractors and strange attractors.

Life at the edge of chaos: Describes transition phenomena first observed in the behaviour of cellular automata.

Self-organised criticality: A phenomenon whereby certain systems reach a crucial state through their intrinsic dynamics, independently of the value of any control parameters.

Attractor: A point or an orbit in the phase space where different states of the system asymptotically converge.

Strange attractor: An attractor for which the approach to its final point in phase space is chaotic.

However, whilst such research has undoubtedly led to important advances in formalisation and modelling, attempts at formulating a TOC have failed. Seth Lloyd, a professor in mechanical engineering at MIT, has compiled a list of 31 different ways in which one can define complexity! As a result of the lack of conceptual unity, Jack D. Cowan (in Horgan, 1995: 104), one of the Institute’s founders, concludes that the major discovery to have emerged from the Santa Fe Institute is that ‘it’s very hard to do science on complex systems’.

Some of the ways in which to define complexity (Horgan, 1995: 107) include:

Entropy: Complexity equals the entropy or disorder of a system, as measured by thermodynamics.

Information: Complexity equals the capacity of the system to “surprise,” or inform, an observer.

Fractal dimension: The “fuzziness” of a system, the degree of detail it displays at smaller and smaller scales.

Effective complexity: The degree of “regularity” (rather than randomness) displayed by a system.

Hierarchical complexity: The diversity displayed by the different levels of a hierarchically structured system.

Grammatical complexity: The degree of universality of the language required to describe a system.

Thermodynamic depth: The amount of thermodynamic resources required to put a system together from scratch.

Time computational complexity: The time required for a computer to describe a system (or solve a problem).

Spatial computational complexity. The amount of computer memory required to describe a system.

Mutual information. The degree to which one part of a system contains information, or resembles, other parts.

Those who support a notion of general complexity would argue that it is impossible to do science on complex systems, if by science we understand the practice of uncovering the rules and laws that govern all phenomena. It is simply fallacious to reason that because computers following simple sets of mathematical rules give rise to extremely complicated patterns, worldly phenomena (which are also characterised by extremely complicated patterns) must therefore also be governed by simple rules (which we can unearth with the help of powerful computers) (Horgan, 1995). To equate complexity with original simplicity is to recognise complexity by decomplexifying it.

In terms of general complexity, attempts at formulating a TOC will necessarily fail because complexity itself is not accounted for. If we accept the fact that things are inherently complex, then it means that we cannot know phenomena in their full complexity (Morin, 2007; Cilliers 1998). In other words, complex phenomena are irreducible. Acknowledging complexity therefore has a profound impact not only on the status of scientific practices, but also on the status of our knowledge claims as such. More specifically, because our knowledge of complex phenomena is limited, our practices should be informed by, and subject to, a self-critical rationality (Preiser & Cilliers, 2010). We are not busy with an objective pursuit of truth, but rather working towards finding suitable strategies for dealing with complex phenomena. These strategies can, however, backfire and therefore it is important to remain self-aware and critical of our practices, and – if necessary – subject our knowledge claims to revisions. Acknowledging the irreducible nature of complexity also influences our understanding of the general features of complexity:

Features of Complex Systems

Complex systems are constituted by richly interconnected components

Complex systems consist of a large number of interrelated components, with fairly-short range interactions. In simple phenomena the system is the additive result of its components, whereas in complex phenomena, the system is the result of the nonlinear, dynamic relations between its component parts and is thus incompressible (Cilliers, 1998). This insight has important methodological implications:

The traditional scientific paradigm is premised on a reductive methodology in that it is believed that complex systems can be completely understood in terms of their components parts. Such analyses, however, serve to destroy the complexity, since what is of interest in complex systems is not the components themselves, but the interrelations between component parts. This is because these relations give rise to self-organising, non-linear and emergent behaviour. As such, reductionism is an inadequate methodology for understanding complex phenomena. This is a rather uncontroversial point, as, today, the weaknesses of the reductive approach are widely recognised. In response, many scientists have therefore turned to the system (rather than its constituting parts) as the object of analysis, in the hope of unearthing those principles that are common to all systems[2]. However, this approach is equally problematic, as it is based on the principle of holism, which, as Morin (1992: 372) explains, is also a form of reductionism:

Holism is a partial, one-dimensional, and simplifying vision of the whole. It reduces all other systems-related ideas to the idea of a totality, whereas it should be a question of confluence. Holism thus arises from the paradigm of simplification (or reduction of the complex to a master-concept or master-category).

In order to understand complex systems, we therefore need to account for a) the systemic identity of component parts; and, b) the complex nature of interrelations between the component parts, and between the component parts and the system as a whole.

The component parts of complex systems have a double-identity

The component parts of complex systems have a double-identity, which is premised on both a diversity and a unity principle (Morin, 1992). With regard to the diversity principle, we can say that the identities of the system’s components are irreducible to the whole, since each component still retains its own unique individual identity. For example, the fact that I enjoy painting cannot be deduced from my role as a philosophy lecturer at a university, although it is part of what makes me unique. However, the coupling of components also gives rise to a common identity (the unity principle) which constitutes their citizenship in a system. Therefore, the fact that I interact with other academics and students on a professional basis, constitutes behaviour that supports the goals of the university, and thus confirms my identity as a philosophy lecturer in the academic system. Despite the nature of the example, this point applies generally: when thinking about complex systems, this double-identity needs to be accounted for, because – on the one hand – if we forego the diversity-principle, our thinking becomes increasingly homogenised (holism); and – on the other hand – if we forego the unity-principle, our thinking loses a sense of unity (Morin, 1992).

Upward and downward causation give rise to complex structures

In complex systems, the competitive and cooperative interactions between component parts on a local level give rise to self-organisation, which is defined as ‘a process whereby a system can develop a complex structure from fairly unstructured beginnings’ (Cilliers, 1998: 12; my italics). Consider, for example, a group of students who come together to form a reading group on complexity theory. Through cooperating, their actions give rise to certain structures, thereby transforming their uncoordinated individual activities into coordinated, goal-orientated, group activities. In turn, these self-organising processes feed back to constrain the behaviour of the parts through a process of downward causation. In my example, the behaviour of the individuals in the reading group is constrained by the goals of the group itself. In other words, in the context of the reading group, it would be inappropriate to instigate a conversation on where I intend to go on vacation. In understanding complex phenomena, we must therefore substitute the principle of reductionism with a principle that conceives of whole-part mutual interaction (Morin, 1992). These mutual interactions result in, what Morin (2008: 49) terms, ‘organizational recursion’ where ‘the products and the effects are at the same time causes and producers of what produces them.’ This means, for example that, as individuals we create, engage in, and challenge our practices (including scientific practices), which simultaneously serve to shape us.

In sum, the complex interrelations between components and systems give rise to the following three systemic characteristics (Morin, 1992):

  • The whole is greater than the sum of its parts, in that systemic attributes cannot be reduced to the parts alone, but are the result of interconnections between the parts.
  • The whole is less than the sum of its parts, since some of the qualities of the parts are suppressed under the constraints that result from systemic organisation.
  • The whole is greater than the whole, due to the dynamic organisation that takes place is systems where local interactions between components give rise to phenomena that are dependent on the base, but simultaneously supersede that base.

In order to adequately understand this last characteristic it is necessary to turn to the next feature of complex systems, namely emergence.

Complex systems exhibit self-organising and emergent behaviour

In order to make the case for self-organisation, it is necessary to show that ‘internal structure can evolve without the intervention of an external designer or the presence of some centralised form of internal control’ (Cilliers, 1998: 89). Self-organisation is a necessary condition for emergence, which is defined as ‘the idea that there are properties at a certain level of organization which cannot be predicted from the properties found at lower levels’ (Emmeche, Koppe, Stjernfelt, 1997: 83; my italics). Specifically, self-organisation draws attention to the structural and temporal dimensions of emergence. Systems develop structure (i.e. hierarchies) by processing information and developing ‘memory’. The example of neural networks offers a good explanation of this principle: neural networks are chemically-connected or functionally-associated neurons. The interconnections between these neurons are called synapses. Over time, certain pathways are established in the brain, meaning that some of the synapses are reinforced through impulses, whereas others die off. In this way, structure develops as groups of neurons are selected, reinforced and transformed through interaction with their environment[3].This implies that a fairly undifferentiated brain develops structure or consciousness over time (Cilliers, 1998).

The previous section concluded with the idea that the whole is greater than the whole; and, indeed, one can convincingly argue that the mind is ‘greater’ or ‘more’ than the brain (which is made up of self-organised neurons or synapses). It therefore seems that, whilst self-organisation is a necessary condition for emergence, it is not sufficient. As such, anyone working with complex systems must take note of (and try to formulate answers to) the following questions pertaining to the nature of emergence[4]:

  • Does our notion of emergence depend on the nature of the system under study?
  • How should emergence be defined and are ideas such as irreducibility, unpredictability, conceptual novelty, ontological novelty, and supervenience necessary for understanding emergence?
  • What categories of entities can be emergent: properties, substances, processes, phenomena, patterns, laws, or something else?
  • Is emergence an objective feature of the world, or is it merely in the eye of the beholder?
  • What is the scope of actual emergent phenomena?
  • Does the emergence imply or require the existence of new levels of phenomena?

Complex systems are open systems

Compounding the issue further is the fact that complex systems are open systems. Unlike isolated systems, the intelligibility of open systems can only be understood in terms of their relation with the environment. This is because there is an energy, material, or information transfer into or out of a given system’s boundary. The nature and content of the system’s interaction with the environment is discipline-dependent. Whereas homeostatic systems are merely dependent on the environment for their survival (in that they are capable of facilitating their own production and maintenance through feedback loops) (Maturana & Varela, 1980); human identity, for example, is, in part, constituted by the environment. This is because our identities develop over time within a network of relationships with other identities. As such, who I become (i.e. my emergent identity) is not only a function of my genes, but also of my context (Woermann, 2010).

Regardless of the system under study, we can say that, methodologically-speaking, it is very difficult to study open systems. This is because the environment is simultaneously intimate and foreign: it is both part of the system (in that we reproduce the system-environment distinction when we model) and remains exterior to the system (Morin, 2008). In other words, the environment cannot be appropriated by the system. This means that the boundary between a system and its environment should be treated both as a real, physical category, and a mental category or ideal model (Morin, 1992). This last point has implications for how we view the boundaries of systems: although boundaries are a function of the activity of a system itself, they are also the product of the description that we give to the system. Hence, boundaries must be thought of ‘as something that constitutes that which is bounded’ (Cilliers, 2001: 141) rather than an objective demarcation of a system. The fact that we cannot draw a system’s boundary in any unproblematic fashion introduces further complexities, which is denoted by the observer problem.


Complex systems are not complicated systems

Engaging with complexity necessitates an acknowledgement of the fact that complex systems are, in principle, unsolvable; or, in the words of the evolutionary biologist, Robert Rosen (1985: 424), a system is complex precisely ‘to the extent that it admits non-equivalent encodings; encodings which cannot be reduced to one another.’ Herein lies a distinction between complicated and complex systems: whereas a complicated system may initially look complex (due to the large number of components that may constitute the system, and/or the sophistication of the tasks that the system can perform), the hallmark of a complicated system is that – unlike a truly complex system – we can figure it out. In this regard, an engine serves as an example of a complicated system (Cilliers, 1998).

Restricted complexity is premised on the belief that complex systems are merely complicated systems; and that, with enough hard work, we can get to the underlying principles that govern these systems. Admittedly, the distinction between complicated and complex systems is often undermined in practice by powerful new technologies, where complex phenomena turn out (on further inspection) to be merely complicated. However, despite the fact that this distinction cannot be drawn in an unproblematic fashion, it nevertheless remains a useful analytical tool, as it determines whether the study of complexity constitutes a search for rules, or whether it constitutes an engagement with both complexity and the implications that arise from complexity.

Modelling complexity is partly a normative exercise

We cannot understand phenomena in their full complexity, and therefore modelling is a necessary condition for creating meaning. In terms of restricted complexity, modelling complexity remains a purely descriptive task, in that the goal is to describe (preferably in mathematical terms) the principles and rules that underlie complex systems. In terms of general complexity, modelling necessarily involves a normative component, as we must make choices, judgements, and assumptions when deciding on the factors that are relevant in modelling complex systems. It is precisely because we cannot escape the realm of choice that complexity involves ethics (and often also politics!) (Preiser & Cilliers, 2010). The fact that an engagement with complexity is not a purely objective exercise, does not imply an ‘anything goes’ approach. Indeed, as Allen (2000: 93) states, ‘[a] representation or model with no assumptions whatsoever is clearly simply subjective reality’ and therefore ‘does not concern systemic knowledge’. The point is that we must apply our complexity reduction assumptions honestly. We should forego the desire to prove the ‘truth’ of our models and instead focus on the pragmatic criterion of whether the systemic knowledge provided by our models is useful or not. In order to safeguard the integrity of scientific practices, we must recognise that our framing or modelling strategy represents one choice amongst many; and that each choice gives rise to ‘a different spectrum of possible consequences, different successes and failures, and different strengths and weaknesses’ (102).

Therefore, modelling complexity (i.e. partitioning ambience into a system and an environment) enables us to reduce the complexity and to gain systemic knowledge of our world. However, a problem arises when weak reductionism (i.e. assuming a conscientious and critical attitude towards modelling) transforms into strong reductionism (i.e. modelling in order to uncover the truth). This is because when the scientist views operational closure as a systemic feature rather than an observational difficulty, one is led ‘to a vision of the world that is classificatory, analytical, [and] reductionist, with linear causality’ (interpretation of Maruyama in Morin, 2008: 12).

We must take responsibility for the consequences that arise from our models

If our models do not correspond with reality, and if they are the outcomes of certain choices, then we must also take responsibility for both the intended and unintended consequences that arise from our modelling strategies. Indeed, it may sometimes be the case that our actions ‘fly back at our heads like a boomerang’ (Morin, 2008: 55). As scientists, the critical attitude therefore also lies in acknowledging that:

There is no science of science, and even the science of science would be insufficient if it did not include epistemological problems. Science is a tumultuous building site, science is a process that could not be programmed in advance, because one can never program what one will find, since the characteristic of a discovery is in its unexpectedness. This uncontrolled process has led today to the development of potentialities of destruction and manipulation, which must bring the introduction into science of a double conscience: a conscience of itself and an ethical conscience (Morin, 2007: 21; my italics).

Science cannot be practiced in a vacuum, since our scientific practices are intricately connected with other aspects of our lives. Given the myriad crises that we face today, it is no longer viable to separate disciplines, cognitive difficulties, and challenges from one another. We have a moral obligation to account for the consequences that arise from our practices; and, if need be, to take corrective action.


Although this article does not provide practical guidance on how to model complex systems within a systems engineering environment, it does seek to focus attention on the general features of complex systems that should be considered when modelling, as well as on the ethical implications that arise when we model complex phenomena. Above all, a serious engagement with complexity implies that we should be critical of the reach of our claims, practice science modestly and vigilantly, and avoid falling in love with our models!


Allen, P. (2000) ‘Knowledge, ignorance, and learning’ in Emergence, 2(4): 78 – 103.

Bedau, M.A. & Humphreys, P. (eds) (2008) Emergence: Contemporary Readings in Philosophy and Science. Cambridge, MA: The MIT Press.

Bak, P. (1997) How Nature Works. New York: Oxford University Press.

Chu, D., Strand, R. & Fjelland, R. (2003) ‘Theories of complexity: common denominators of complex systems’ in Wiley Periodicals, 8(3): 19 – 30.

Cilliers, P. (2001) ‘Boundaries, hierarchies and networks in complex systems’ in International Journal of Innovation Management, 5(2): 135 – 147.

Cilliers, P. (1998) Complexity and Postmodernism: Understanding Complex Systems. London: Routledge.

Emmeche, C., Koppe, S. & Stjernfelt, F. (1997) ‘Explaining emergence: towards an ontology of levels’ in Journal for General Philosophy of Science / Zeitschrift für allgemeine Wissenschaftstheorie, 28(1): 83-119.

Horgan, J. (1995) ‘From complexity to perplexity’ in Scientific America, June 1995, 104 – 109.

Kaufmann, S.A. (1993) Origins of Order: Self-Organization and Selection in Evolution, technical monograph. New York: Oxford University Press.

Langton, C.G. (ed.) (1995) Artificial Life: An Overview. Cambridge, MA: The MIT Press.

Langton, C.G. (1992) ‘Life at the edge of chaos’ in Langton, C.G, Farmer, J.D., Rasmussen, S. and Taylor, C. (eds.) Artificial Life II: Santa Fe Institute Studies in the Sciences of Complexity, vol. 10. Reading, MA: Addison-Wesely, 41-91.

Lorenz, E. (1966) The Essence of Chaos. Washington: University of Washington Press.

Maturana, H.R. & Varela, F.J (1980) Autopoiesis and Cognition: the Realization of the Living. Dordrecht: D. Reidel.

Morin, E. (2008) On Complexity, trans. S.M. Kelly. Cresskill: Hampton Press.

Morin, E. (2007) ‘Restricted complexity, general complexity’, trans. C. Gershenson in Gershenson, C., Aerts, D. and Edmonds, B., (eds.) Worldviews, Science and Us: Philosophy and Complexity. Singapore: World Scientific, 5–29.

Morin, E. (1992) ‘From the concept of a system to the paradigm of complexity’, trans. S. Kelly in Journal of Social and Evolutionary Systems, 15(4): 371 – 385.

Preiser, R. & Cilliers, P. (2010) ‘Unpacking the ethics of complexity: concluding reflections’ in Cilliers, P. & Preiser, R. (eds.) Complexity, Difference and Identity. Dordrecht: Springer, 265 – 287.

Rosen, R. (1985) Anticipatory Systems: Philosophical, Mathematical and Methodological Foundations. Oxford: Pergamon Press.

Shannon, C.E. (1948) ‘A Mathematical Theory of Communication’ in Bell System Technical Journal, 27: 379 – 423.

von Bertalanffy, L. (1972) ‘The history and status of general systems theory’ in The Academy of Management Journal, 15(14): 407 – 426.

von Neumann, J. (1966) Theory of Self-Reproducing Automata, ed. A.W. Burks. Urbana: University of Illinois Press.

Wiener, N. (1965) Cybernetics: Or Control and Communication in the Animal and the Machine. Cambridge, MA: the MIT Press.

Woermann, M. (2010) ‘Corporate identity, responsibility and the ethics of complexity’ in Cilliers, P. & Preiser, R. (eds.) Complexity, Difference and Identity. Dordrecht: Springer, 265 – 287.

About the Author

Dr Minka Woermann is a lecturer in philosophy and applied ethics at Stellenbosch University, South Africa. Her research interests include complexity theory, ethics, business ethics, and post-structural philosophy. She recently completed her PhD, entitled ‘A Complex Ethics: Critical Complexity, Deconstruction, and Implications for Business Ethics’.

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Systems Science Working Group


Promote the advancement and understanding of Systems Science and its application to SE.


Co-Chair: Dr. James N Martin, Principal Engineering, The Aerospace Corporation

Co-Chair: TBD

Contact Systems Science Working Group for additional information or to join this group.


  • 1) Encourage advancement of Systems Science principles and concepts as they apply to Systems Engineering.
  • 2) Promote awareness of Systems Science as a foundation for Systems Engineering.
  • 3) Highlight linkages between Systems Science theories and empirical practices of Systems Engineering.

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Interaction between Requirements Engineering and Systems Architecting: An Emerging Theory Based on a Suite of Exploratory Studies

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Publisher: LAP LAMBERT Academic Publishing (February 25, 2011)

ISBN-10: 3844310363

ISBN-13: 978-3844310368

Interaction Between Requirements Engineering and Systems Architecting: An Emerging Theory Based on a Suite of Exploratory Studies (French Edition)Product Description:

Requirements Engineering (RE) and Systems Architecting (SA) are often considered the most important phases of the software development lifecycle. Because of their close proximity in the software development lifecycle, there is a high degree of interaction between these two processes. While such interaction has been researched in terms of new technology, there is a distinct lack of empirical understanding regarding the scientific properties of this interaction. For instance: What is the impact of an existing system’s architecture on requirements decision-making? What requirements-oriented problems are encountered during SA? What is the impact of requirements engineering knowledge on systems architecting? There is little in the literature addressing such questions. This book explores such issues through a suite of empirical studies. From the observations, a theory is proposed that describes the impact of human and technical factors in the interaction between RE and SA. The new knowledge has impact on: technology development for RE and SA; hiring and training personnel for RE and SA processes in industry; curriculum improvement in academia; and future empirical research.

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June 27-30, 2011, Sheraton Uptown Hotel, Albuquerque, NM, USA

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FMICS 2011 – 16th International Workshop on Formal Methods for Industrial Critical Systems new

August 29-30, 2011, Trento, Italy

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19th International Conference on Case Based Reasoning new

12-15 September 2011, Greenwich, London, UK

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IEEE SRDS 2011 – 30th International Symposium on Reliable Distributed Systems new

October 4-7, 2011, Madrid, Spain

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ER 2011, 30th International Conference on Conceptual Modeling Brussels, Belgium new

October 31 – November 3, 2011, Brussels, Belgium

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Education and Academia

New Industrial & Systems Engineering Masters Degree

The new University of Washington Professional Master’s Program in Industrial & Systems Engineering, offered by the Department of Industrial & Systems Engineering, is designed for engineers and scientists who wish to excel as complex system thinkers in today’s global business environment….

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Idex Focus Turns To Growing Local Industry

The Institute for Near East and Gulf Military Analysis, a Dubai and Beirut-based think tank, this week announced the launch of a competition for Emirati university students to design, build, test and pilot unmanned aerial vehicles. The contest is being billed as the first of its kind in the UAE.

Engineers from the American defence giant Northrop Grumman, the Higher Colleges of Technology (HCT) and Abu Dhabi Autonomous Systems Investment (Adasi), a local company, will assist the students. The competition will take place on May 4-5 in the capital.

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HCT teams with Johns Hopkins University to boost Masters Engineering Program

The International Defense Exhibition and Conference (IDEX 2011) was the venue for showcasing more successful partnerships – this one in the field of education.

Dr Tayeb Kamali, Vice Chancellor of the Higher Colleges of Technology (HCT), announced the launch of a Systems Engineering specialization in its Masters of Engineering Program, a core module of which will be delivered by the prestigious American teaching institution, Johns Hopkins University.

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Stevens Institute of Technology Launches New Technical Leadership Program

Stevens Institute of Technology is launching a unique new Technical Leadership Program for selected engineering and technology professionals. The program begins in June 2011. The program features an innovative curriculum emphasizing the experiential approach and systems thinking for which Stevens is internationally known.

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Postdoc Position Self-Adaptive (Automotive) Systems at CEA Research Center near Paris

The laboratory LISE (Model Driven Engineering Laboratory for embedded and real‑time systems), part of the CEA LIST (450 researchers in the field of software‑intensive systems, (see http://www-list.cea.fr/gb/index_gb.htm ) has an open position for a research assistant.

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Some Systems Engineering-Relevant Websites


Systems Thinking newsletter by isee systems.


The website of Hobbs Engineering specialising in HALT and HASS.

Standards and Guides

ISO TC 159 – Ergonomics

The ISO TC 159 website lists all the relevant information on this Technical Committee, including the following list of working groups:

Subcommittee/Working Group


TC 159/CAG

Chairman Advisory Group
The convener can be reached through the secretariat

TC 159/AG

AGAD: Advisory Group for Accessible Design
The convener can be reached through the secretariat

TC 159/WG 2

Ergonomics for people with special requirements
The convener can be reached through the secretariat

TC 159/SC 1

General ergonomics principles

TC 159/SC 3

Anthropometry and biomechanics

TC 159/SC 4

Ergonomics of human-system interaction

TC 159/SC 5

Ergonomics of the physical environment

The ISO TC 159 Business Plan describes:

  • The business environment for ISO/TC
  • The benefits expected from the work of the ISO/TC.
  • Representation and participation in the ISO/TC
  • Objectives of the ISO/TC and strategies for their achievement
  • Factors affecting completion and implementation of the iso/tc work programme
  • Structure, current projects and publications of the iso/tc

The work programme for each of the working groups of ISO/TC 159 is set out on the ISO website in terms of the standard or project, the Standards Development Process Stage code and the International Classification for Standards (ICS) reference.

The meeting calender is also provided on the website.

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IEEE Announces Standards Interest Group (Sig) For India; Move To Propel India’s Involvement in the IEEE Global Standards Process

IEEE, the world’s largest technical professional association, announced the formation of the IEEE Standards Interest Group (SIG) for India. In the works since last year, the IEEE SIG heralds a new chapter in India’s role in the global standards process and will provide a platform for increased involvement of the local technical community in global standards development.

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A Definition to Close on



PPI News



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Systems Engineering Public 5-Day Courses (2011)

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Cognitive Systems Engineering Courses (2011)

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  1. http://www.santafe.edu

  2. See: von Bertalanffy, L. (1972) ‘The history and status of General Systems Theory’ in The Academy of Management Journal, 15(14): 407 – 426.

  3. What should be clear from this description is that the structural and temporal dimensions of self-organisation (as an emergent process) do not allow for an understanding of complexity in terms of absolute thresholds (as implied, for example, by von Neumann’s (1966) use of the term ‘complexity barrier’). It is not the case that simple systems suddenly start showing emergent behaviour. As soon as dynamic and complex interactions between systemic components exist, systems start developing structures. However, complexity is also not an additive process, since the interactions between components are non-linear and allow for surprising reconfigurations of systemic structures. As such, trying to pinpoint optimal levels of organisation, through recourse to terms such as ‘self-organised criticality’, again denies a measure of complexity.

  4. Some of these questions are briefly addressed in the introduction of Bedau and Humphreys’ (2008) jointly edited book, entitled ‘Emergence: Contemporary Readings in Philosophy and Science’.

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