Complexity, Emergence, Resilience …

Pariès, Jean

doi:10.1201/9781315605685-7

Cited by 15 publications

(11 citation statements)

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“…In both recommended CCS definitions, the conceptualization of complexity is based in the complex-systems science literature. In this literature, complexity is defined in terms of characteristics such as variety and interactivity among elements; the potential for producing new, emergent phenomena; the potential for tipping points and other forms of nonlinearity; self-organization in response to shifts in the surrounding environment; and more (e.g., Heylighen et al, 2007;Paries, 2006). This treatment of complexity contrasts with focusing on the number of task steps or items held in working memory.…”

Section: Complex Versus Complicatedmentioning

confidence: 99%

Toward a Definition of Complex Cognitive Skill

Chen¹,

Neville²,

Massey³

et al. 2019

Proceedings of the Human Factors and Ergonomics Society Annual

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Over the past several decades of information technology growth, work has become increasingly cognitive. It is also becoming more complex, thanks in part to growing networks of information, capability, and resources that link us and our work into large, distributed teams and organizations. In this paper, we assert that skills required for complex cognitive work are acquired in ways that qualitatively differ from the ways used for training routine procedural and rule-based skill. We conducted a literature review, peer workshop, and candidate-definition evaluation to derive a definition of complex cognitive skill (CCS). The CCS definition meets a number of evaluative criteria that include addressing complex cognitive work characteristics described in the relevant literature. The definition is expected to help researchers develop a more focused and coherent CCS research base and to help training practitioners justify the types of methods and resources required for supporting CCS acquisition. Future work will include assessing the definition for clarity, unambiguity, and, more generally, usefulness and usability.

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Section: Complex Versus Complicatedmentioning

confidence: 99%

Toward a Definition of Complex Cognitive Skill

Chen¹,

Neville²,

Massey³

et al. 2019

Proceedings of the Human Factors and Ergonomics Society Annual

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“…Investment continues for research into complex systems engineering, for example, £2M of the UK Engineering and Physical Sciences Research Councils's current £7M funding into complexity science research is awarded to the Thales Bristol Hybrid Partnership in Autonomous Systems (Grant EP/R004757/1) to address the challenge of hyrbid autonomous systems engineering. Growth in autonomous systems, smart cities and systems‐of‐systems deployments foreground the challenges of increasing system complexity, where a large number of diverse and interdependent components, subsystems, and systems interact via nonlinear relationships resulting in emergent behavior and properties that can be difficult to predict and understand, such as the resilience of these complex systems 11–13 …”

Section: Introductionmentioning

confidence: 99%

Assaying the importance of system complexity for the systems engineering community

Potts

Sartor

Johnson

et al. 2020

Systems Engineering

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How should organizations approach the evaluation of system complexity at the early stages of system design in order to inform decision making? Since system complexity can be understood and approached in several different ways, such evaluation is challenging. In this study, we define the term “system complexity factors” to refer to a range of different aspects of system complexity that may contribute differentially to systems engineering outcomes. Views on the absolute and relative importance of these factors for early–life cycle system evaluation are collected and analyzed using a qualitative questionnaire of International Council on Systems Engineers (INCOSE) members (n = 55). We identified and described the following trends in the data: there is little between‐participant agreement on the relative importance of system complexity factors, even for participants with a shared background and role; participants tend to be internally consistent in their ratings of the relative importance of system complexity factors. Given the lack of alignment on the relative importance of system complexity factors, we argue that successful evaluation of system complexity can be better ensured by explicit determination and discussion of the (possibly implicit) perspective(s) on system complexity that are being taken.

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“…Finally, Section 6 concludes the paper and defines further research lines. [9], Paries [10] and Haimes et al [11] studied the emergent properties of resilience and considered that it cannot be directly measured as an assessment of the AS IS status. Nevertheless, it is necessary to analyse, in a certain instance, how much an enterprise is prepared to face specific disruptive events.…”

Section: Introductionmentioning

confidence: 99%

Optimising the Preparedness Capacity of Enterprise Resilience Using Mathematical Programming

2020

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In today’s volatile business arena, companies need to be resilient to deal with the unexpected. One of the main pillars of enterprise resilience is the capacity to anticipate, prevent and prepare in advance for disruptions. From this perspective, the paper proposes a mixed-integer linear programming (MILP) model for optimising preparedness capacity. Based on the proposed reference framework for enterprise resilience enhancement, the MILP optimises the activation of preventive actions to reduce proneness to disruption. To do so, the objective function minimizes the sum of the annual expected cost of disruptive events after implementing preventive actions and the annual cost of such actions. Moreover, the algorithm includes a constraint capping the investment in preventive actions and an attenuation formula to deal with the joint savings produced by the activation of two or more preventive actions on the same disruptive event. The management and business rationale for proposing the MILP approach is to keep it as simple and comprehensible as possible so that it does not require highly mathematically skilled personnel, thus allowing top managers at enterprises of any size to apply it effortlessly. Finally, a real pilot case study was performed to validate the mathematical formulation.

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Complexity, Emergence, Resilience …

Cited by 15 publications

References 0 publications

Toward a Definition of Complex Cognitive Skill

Toward a Definition of Complex Cognitive Skill

Assaying the importance of system complexity for the systems engineering community

Optimising the Preparedness Capacity of Enterprise Resilience Using Mathematical Programming

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