Optimising Man-made Complex Systems

As analysed, complexity is about the study of emergent order in disorderly systems. Using feedback and learning algorithms it enables systems to progressively adapt to the environment and respond quicker and better to non-linearity, to begin evidencing a certain degree of macro-equilibrium.

P a g e | 85 By definition, no external controller can be applied to regulate systems behaviour thus they need to be self-aware and responsive to the surrounding stimuli. Living organisms are adaptive by nature and interoperability is innate, but complex information systems need interoperability as means to ensure good feedback capable of triggering proper responses and avoiding erratic behaviour.

Therefore, traditional complexity is analytic and does need optimisation; however, when being applied to other areas such as management, production or IS, several technologies can be embedded into systems design to enable optimised behaviour, e.g. monitoring processes, artificial intelligence (AI), change management, model-based systems engineering (MBSE), etc.

3.6.1. Systems Monitoring

A systems approach would find difficulties to model diversity at the level of each system organisation, and would also have trouble getting inside the systems black box to explain how decisions were made. However, the use of artificial adaptive agents enables these issues to be studied in depth.

In this context, monitoring is an important capability to collect information (characteristics and status) about resources (systems or system parts). Generally, monitoring processes include different stages from capturing the information to its elaboration and communication to the user, and are structured into specific components (generally the producer, the consumer and the registry) in order to meet a set of requirements (scalability, extensibility, data delivery models, portability and security). The whole process has to be carried out balancing extensibility and self-description capabilities on the one hand and compactness on the other (Deakin et al., 2006;

Morjaria & Santosa, 1996; Snodgrass, 1988).

3.6.2. Systems’ Learning and Artificial Intelligence

There is no doubt that one of the major drivers of complexity theory in information systems has been the widespread availability of AI methods, such as neural networks, ABM, and genetic algorithms. These techniques have enabled researchers to populate simulated worlds with multiple intelligent and idiosyncratic entities/agents, and study their behaviour (Davis & R. G. Smith, 1983).

Since its conception in the mid-1950s, artificial intelligence with its great ambition to understand and emulate intelligence in natural and artificial environments alike is now a truly multidisciplinary field that reaches out and is inspired by a great diversity of other fields.

The field was founded on the claim that a central property of humans, intelligence, can be so precisely described that it can be simulated by a machine. Thus, machine learning has been central to AI research from the beginning, using both unsupervised learning algorithms to detect specific patterns within streams of input, and supervised learning with classification schemes and numerical regression algorithms to describe the relationship between inputs and outputs and predicting how the outputs should mould to different inputs (Russell & Norvig, 2003).

There is no established unifying theory or paradigm that guides AI research. Many consider it should simulate natural intelligence by studying psychology or neurology and apply a sub-symbolic approach such as neural networks, fuzzy systems and evolutionary computation, while others claim

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that intelligence should be reproduced using high-level symbols, similar to words and ideas (Kelley, 2003). Nevertheless, it is agreed that intelligent agents are not limited to simply reacting in predetermined ways to inputs or environmental stimuli. They can learn, make inferences, and plan, thus optimising complex systems (Phelan, 1999).

AI systems can benefit from understanding in the field of artificial neural networks, but other application areas include pervasive/ubiquitous computing and autonomic computing (Saha &

Mukherjee, 2003; Kephart & Chess, 2003). This pursues the idea of having networked communication systems able of being autonomously controlled, in a sense that it can manage themselves as a kind of administrators.

3.6.3. Systems Change Management

Change management is the process of identifying and implementing new features to an existing system in order to result in overall improvements and causing minimal disturbances. Throughout the process, the aim is to ensure that traceability of changes remain transparent while minimizing the change impacts on systems normal behaviour.

Among the different components of change management process, one can account for the identification of problems, costs feasibility analysis, analysis of change impact, implementation and testing, followed by a review of change benefits. This optimisation strategy is frequently used in organisational management, maintaining a record of the changes in knowledge repositories using traceability features and evaluating the potential benefits of proposed changes using simulation.

This allows informed decisions to be made, which help to justify changes to a system before its implementation (CRESCENDO Partners, 2009a).

3.6.4. Network Optimisation: Model-Based Systems Engineering (MBSE)

Traditionally, in large engineering projects involving large networks, organisations employ a document-based systems approach characterized by the generation of textual specifications and design documents, in hard-copy or electronic file format, that are then exchanged between customers, users, developers, and testers. These documents and drawings represent the systems requirements and the design information that systems engineers control to ensure project’s validity, completeness, and consistency (Ogren, 2000). However, despite being rigorous, this method has many limitations due to the inherent complexity of the processes (e.g. relationships between requirements, design, engineering analysis, and test information) and the network itself (relationships between stakeholders).

For these situations, the model-based systems engineering strategy has been introduced with intention of optimising network-centric complex systems’ engineering activities using some of the procedures analysed above. MBSE is the formalised application of modelling to support cooperative engineering processes, namely requirements elicitation, product/systems design, analysis, verification and validation activities starting at the conceptual design phase and continuing throughout development and later lifecycle stages (INCOSE, 2007; INCOSE & OMG DSIG, 2011).

P a g e | 87 Figure 3.8: MBSE scope (adapted from (Nallon 2003 and Friedenthal et al. 2008))

Figure 2-1 illustrates a possible view on the MBSE process using multiple coherent models to describe a single system within a network of organisations, thus reducing not only potential problems on the project’s validity, completeness, and consistency, but also enabling monitoring and change management. This vision merges the full product lifecycle, starting from the concept where the stakeholder’s needs are identified until the disposal of the product, with the development of the following models:

 Requirements models that represent the relationships between user requirements and/or model objects. A primary benefit of modelling requirements is the opportunity this provides for analysing them with techniques such as requirements animation, reasoning, etc.

(Nuseibeh & Easterbrook, 2000). Examples of tools used to construct this models are DOORS, ReqPro or even Excel;

 Behaviour models to represent the intended and unintended behaviour of a system of interest (e.g. a product), thus responding to functional requirements. Examples of behaviour models include UML activity, sequence or state machine diagrams;

 Parametric models to reply to the non-functional requirements representing the formal relationships and constraints of the system and its components. Tools used for parametrics models include mathematic formulas and constraints; and finally

 Structure models which describe the enterprise and system level contexts from both logical and physical viewpoints. These are represented with external and internal block shared by the network of cooperating companies.

In a nutshell, MBSE can provide: (1) enhanced communications, offering more understanding between the development teams and the other stakeholders; (2) reduced development risk, because requirements validation and design verification improve the cost-effectiveness in the development of a system; (3) improved quality, with a more accurate traceability between requirements, design, analysis, and testing; (4) increased productivity, having an more flexible and

Structure

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easier readjustment of the existing models; (5) and enhanced knowledge transfer, since all partners share the same models (Friedenthal et al., 2008).

By the exposed, if properly integrated, MBSE framework can bring an added value to systems engineering enterprise networks, maximizing the efficiency of collaborations and stimulating interoperability via the models used along the system’s and products lifecycle. However, the issues exposed on section 1.1.3 “The Problem of Sustainability of Interoperable Solutions” and also in the grand-challenge raised in the previous chapter (see section 2.8.2.1), regarding sustainability and dynamicity of networks and evolution of requirements, remain pertinent and unaddressed so far.

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